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617424 | 2 | null | 616925 | 0 | null |
## It seems to be about how far one can move away from the support
In the synthetic control setting, the hypothetical value that would have been observed without a treatment is approximated using a linear combination of subjects thought to be similar to the treatment subject. The authors stress that the weights in t... | null | CC BY-SA 4.0 | null | 2023-05-31T11:46:36.483 | 2023-05-31T11:46:36.483 | null | null | 250702 | null |
617425 | 1 | null | null | 1 | 23 | I have an experiment with time series data (spike rates).
A Python script calculating their autocorrelation with `statsmodels.tsa.stattools.acf` was apparently giving different answers than an implementation of equivalent logic in Python, using the same bins and 99 lags in each case. The answers had the same pattern, ... | Python's `acf` and Matlab's `xcorr` apparently give different magnitude (but same pattern) answers for some data | CC BY-SA 4.0 | null | 2023-05-31T11:52:35.650 | 2023-05-31T15:40:46.073 | 2023-05-31T15:40:46.073 | 245642 | 245642 | [
"python",
"autocorrelation",
"matlab",
"sparse"
] |
617426 | 1 | null | null | 0 | 15 | You'll often see the goal of a statistical estimation problem as being to fit a model such that it $\approx p_{*}(y|x)$ where $p_{*}(y|x)$ is the "true distribution of the data".
My question is: what uncertainty is possessed in this "true distribution of the data"? Does it assume infinite training data...in which case ... | Where does the uncertainty of the "true" $p_{*}(y|x)$ come from? | CC BY-SA 4.0 | null | 2023-05-31T12:23:22.303 | 2023-05-31T12:23:22.303 | null | null | 381061 | [
"machine-learning",
"bayesian",
"assumptions",
"posterior"
] |
617427 | 2 | null | 588751 | 0 | null | A fully convolutional network is independent of the number of pixels in the input if the output size is allowed to have a different number of pixels as well.
This is due to the fact that the number of parameters in a convolutional layer is independent of the number of pixels in the input.
However, the same convolution ... | null | CC BY-SA 4.0 | null | 2023-05-31T12:28:01.197 | 2023-05-31T12:28:01.197 | null | null | 95000 | null |
617428 | 2 | null | 605756 | 0 | null | I think that in your case, if it is "Cured", the event of interest will never happen and converge to a duration of inifinity. If it's the case, you just put your duration as the duree, the timeline as the maximum timeline you want to observe, and then "event" (not cured) as the event_col (0/1). The model will do the re... | null | CC BY-SA 4.0 | null | 2023-05-31T12:46:24.390 | 2023-05-31T12:48:11.617 | 2023-05-31T12:48:11.617 | 389264 | 389264 | null |
617429 | 1 | null | null | 1 | 46 | I am struggling with the following problem (casella & berger 4.30(b)):
$$ \text{Suppose that} \;\;\;Y|X=x \sim normal(x,x^2) \;\; \text{and} \;\; X\sim uniform\,(0,1).\\\text{Prove that} \;\;\frac{Y}{X} \;\text{and}\; X \;\text{are independent.} $$
My attempt:
$$ \text{Let} \; u= y/x \;\; \text{and}\;\;v=x. \\
\text{Th... | Prove that two random variables are independent | CC BY-SA 4.0 | null | 2023-05-31T12:49:06.613 | 2023-05-31T19:48:34.930 | null | null | 389258 | [
"mathematical-statistics",
"inference",
"random-variable",
"independence"
] |
617430 | 2 | null | 617046 | 0 | null |
## The difference is in the fact that $X_2$ is an effect of the target
This is a really cool question that I hadn't thought enough about!
A linear regression of the sort of $Y\sim T+X_1+X_2$ will aim to find the coefficients that explain the most variation an the target. In your first example, controlling for $X_2$ ... | null | CC BY-SA 4.0 | null | 2023-05-31T13:06:22.973 | 2023-05-31T13:06:22.973 | null | null | 250702 | null |
617431 | 2 | null | 617429 | 1 | null | You have
$$f_{U,V}(u,v) =\frac{v}{\sqrt{2\pi}v^2}e^{-\frac{1}{2v^2}(uv-v)^2}I_{\mathscr Y}((u,v))$$ though I think you should extend the square root to $\dfrac{v}{\sqrt{2\pi v^2}}e^{-\frac{1}{2v^2}(uv-v)^2}I_{\mathscr Y}(u,v)$.
There are $v$s you can cancel to give
$$f_{U,V}(u,v) =\frac{1}{\sqrt{2\pi }}e^{-\frac{1}{2}... | null | CC BY-SA 4.0 | null | 2023-05-31T13:08:17.283 | 2023-05-31T13:08:17.283 | null | null | 2958 | null |
617432 | 1 | null | null | 1 | 13 | From a total of $N$ words i have the following dataset where the first column represents the ranks and the second the frequency. For example
$$\begin{array}{cc}
1 & 4300 \\
2 & 3100 \\
3 & 2500 \\
4 & 1900 \\
\vdots & \vdots
\end{array}
$$
I want to find the constant where satisfies $$cf_i =\frac{\text{const}}{i}$$ whe... | Given the rank and frequency find the constant in Zipfs law | CC BY-SA 4.0 | null | 2023-05-31T13:22:04.153 | 2023-05-31T16:49:53.687 | 2023-05-31T16:49:53.687 | 5176 | 389267 | [
"power-law",
"zipf"
] |
617433 | 1 | null | null | 0 | 18 |
- A "new broom" in the modeling department has swept clean the existing 5-figure number of dictionary geo features (kept in a key/value store), replacing them with just their key (more precisely, by exact latitude and longitude of client's zip code - a pair of multi-level features that together can probably proxy for ... | Replacing 10k+ geo features with just their key (zipcode coordinates) in a GBDT model - a sound idea? | CC BY-SA 4.0 | null | 2023-05-31T13:22:10.157 | 2023-05-31T13:29:33.700 | 2023-05-31T13:29:33.700 | 325325 | 325325 | [
"machine-learning",
"feature-selection",
"boosting",
"feature-engineering",
"geography"
] |
617434 | 1 | null | null | 1 | 32 | I did a power analysis to calculate the sample size in GPower.
Now I'd like to do the same in R. However, I am not able to figure out how...
I found [ss.2way](https://rdrr.io/cran/pwr2/src/R/ss.2way.R) but that seems to require different inputs.
Is there any way to calculate the sample size for a 2x3 design in R?
Thank... | Power Analysis for 2-Way-Anova in R | CC BY-SA 4.0 | null | 2023-05-31T13:26:12.577 | 2023-05-31T13:28:54.750 | 2023-05-31T13:28:54.750 | 389269 | 389269 | [
"r",
"anova",
"statistical-power",
"gpower"
] |
617435 | 2 | null | 617375 | 0 | null | The confusion might come from the [multiple parameterizations of the Weibull distribution](https://en.wikipedia.org/wiki/Weibull_distribution#Alternative_parameterizations). Note that the first hazard function can be written in the form $\lambda(t)=\lambda_0(t) \exp(\eta_i)$, where $\eta_i$ is the linear predictor for ... | null | CC BY-SA 4.0 | null | 2023-05-31T13:35:19.033 | 2023-05-31T13:35:19.033 | null | null | 28500 | null |
617436 | 1 | null | null | 0 | 16 | Is it true that for a square symmetric matrix such as the covariance matrix, the singular values are equal to the eigenvalues? The eigen decomposition for covariance is the same as singular value decomposition?
| Singular value and Eigen value for Square Matrix | CC BY-SA 4.0 | null | 2023-05-31T13:37:46.370 | 2023-05-31T13:50:33.683 | null | null | 388783 | [
"eigenvalues"
] |
617437 | 1 | null | null | 0 | 13 | Let say I am sampling from a population with an unknown distribution to approximate the mean of the population.
I am trying to figure out how large my sample size n has to be in order to guarantee with 95% confidence that my sample mean is within, say, 1% of the population mean.
I know I can get the 95% confidence inte... | Estimate potential distance from population mean given only sample size? | CC BY-SA 4.0 | null | 2023-05-31T13:42:40.207 | 2023-05-31T13:42:40.207 | null | null | 76851 | [
"confidence-interval",
"sample"
] |
617438 | 2 | null | 617436 | 1 | null | For a real symmetric positive semi-definite matrix like a covariance matrix, the nonnegative square roots of the eigenvalues are equal to the singular values. The eigenvectors are also equal to the left and right singular vectors. This is because, for these types of matrices, the eigendecomposition and the SVD give equ... | null | CC BY-SA 4.0 | null | 2023-05-31T13:44:38.433 | 2023-05-31T13:50:24.093 | 2023-05-31T13:50:24.093 | 53580 | 53580 | null |
617439 | 2 | null | 617420 | 0 | null | @Frank Harrell
I'm not a statistics major, but I'll try to present a few motivations for using IPTW from a non-expert perspective. Assuming that by "covariate adjustment" you mean multiple covariate Cox regression:
- IPTW allows for adjustment of more parameters. I once attended a lecture on your modeling strategy, an... | null | CC BY-SA 4.0 | null | 2023-05-31T13:45:57.150 | 2023-05-31T13:45:57.150 | null | null | 388935 | null |
617440 | 1 | null | null | 0 | 7 | I have pooled several rounds of annual cross-sectional survey data to create 5 synthetic-cohorts to assess differences in (say smoking) prevalence between cohorts at the same age. The age-range (25-60) for the cohorts does not overlap completely - the most recent cohort has rates for ages 25-38, the oldest cohort 45-60... | Best model to use for computing prevalence rate ratios in cross-sectional data with binary outcome? | CC BY-SA 4.0 | null | 2023-05-31T13:50:28.157 | 2023-05-31T13:59:16.823 | 2023-05-31T13:59:16.823 | 345611 | 389270 | [
"logistic",
"binomial-distribution",
"cox-model",
"prevalence",
"synthetic-cohort"
] |
617441 | 1 | 617444 | null | 2 | 31 | In page 64 of [Bayesian Data Analysis](http://www.stat.columbia.edu/%7Egelman/book/) by Gelman et.al. they write
>
... sensible vague prior density for µ and σ, assuming prior independence of location and scale parameters, is uniform on ($\mu$, $\log~\sigma$) or, equivalently, $p(\mu, \sigma^2) \propto 1/\sigma^2$.
... | Derive the prior on variance scale if uniform prior placed on logarithm scale | CC BY-SA 4.0 | null | 2023-05-31T13:57:53.433 | 2023-05-31T14:30:51.373 | null | null | 43842 | [
"bayesian",
"prior"
] |
617442 | 1 | null | null | 2 | 48 | I'm a physicist trying to finally get a hold on practical statistics for particle physics and am having problem with the following -- I apologize for the lack of formality below.
Suppose the number of events within a single channel is governed by a Poisson distribution $P(N,\mu)$, whose parameter for Null ($\mu_b$) and... | Likelihood Ratio vs Modified Frequentist Approach (CLs) | CC BY-SA 4.0 | null | 2023-05-31T14:04:39.737 | 2023-06-02T16:50:20.283 | 2023-05-31T20:23:04.390 | 389271 | 389271 | [
"hypothesis-testing",
"confidence-interval",
"likelihood-ratio"
] |
617443 | 2 | null | 617333 | 1 | null | You can think of it like this: a [function](https://en.wikipedia.org/wiki/Function_(mathematics)) is a mapping $f: x \to y$. We use Gaussian Processes to model random functions $f \sim \mathcal{GP}$, where the mapping is non-deterministic. GP takes some points $x$ and the realizations of the functions $f(x) = y$ to lea... | null | CC BY-SA 4.0 | null | 2023-05-31T14:18:49.190 | 2023-05-31T14:18:49.190 | null | null | 35989 | null |
617444 | 2 | null | 617441 | 2 | null | Your error is going from $\text{Let}~ Y = \log \sigma^2$ to $\dfrac{dY}{d\sigma^2} = 2/\sigma$
You should have: $\dfrac{dY}{d\sigma^2} = 1/{\sigma^2}$ (simple derivative of a logarithm)
though perhaps you tried $\dfrac{dY}{d\sigma} = 2\sigma \frac{1}{\sigma^2}= 2/{\sigma}$ (chain rule).
This gives you: $\text{If}~ X =... | null | CC BY-SA 4.0 | null | 2023-05-31T14:22:54.700 | 2023-05-31T14:30:51.373 | 2023-05-31T14:30:51.373 | 2958 | 2958 | null |
617446 | 1 | null | null | 0 | 19 | I am trying to implement open set classification and from my research, softmax (usually with temperature scaling) can be used to create a confidence metric.
However, for a complete outlier input which is not part of any of the known classes, the temperature scaled softmax assigns a probability of 1 to the middle class ... | Softmax gives high value for middle class when seeing outlier data | CC BY-SA 4.0 | null | 2023-05-31T14:36:47.433 | 2023-05-31T14:36:47.433 | null | null | 389274 | [
"machine-learning",
"tensorflow",
"computer-vision",
"artificial-intelligence",
"softmax"
] |
617447 | 1 | null | null | 0 | 12 | I am working with different transformations of my response. I use two error metrics which normalize by the range of the data, in order to make comparisons between different models based on these transformations. Does anyone know a source which discuss the implications of normalizing by the range when the distribution i... | Implications of normalizing by the range of data when comparing evaluation metrics for different distributions | CC BY-SA 4.0 | null | 2023-05-31T15:13:33.563 | 2023-05-31T16:46:44.573 | 2023-05-31T16:46:44.573 | 320876 | 320876 | [
"distributions",
"normalization",
"error",
"model-evaluation"
] |
617448 | 1 | null | null | 0 | 17 | What kind of deep learning is the generation of numerical features (Y) from objects (X) used to compute a score (f(.), differentiable) that is to be maximized directly?
Basically NN$\theta$(x) = y, f(y) = score, so $\frac{d score}{d\theta} = \frac{dy}{d\theta} \frac{d f(y)}{dy}$ can be used for backpropagation; f(.) ca... | What kind of learning is feature generation for score maximization? | CC BY-SA 4.0 | null | 2023-05-31T15:25:06.880 | 2023-05-31T16:51:57.627 | 2023-05-31T16:51:57.627 | 389279 | 389279 | [
"machine-learning",
"generative-models"
] |
617449 | 2 | null | 617419 | 4 | null | The event* $$0 \in \left[Y-\log\left(\frac{1-\alpha_2}{\alpha_2}\right),Y-\log\left(\frac{\alpha_1}{1-\alpha_1}\right)\right]$$ is equivalent to the event $$\theta \in \left[X-\log\left(\frac{1-\alpha_2}{\alpha_2}\right),X-\log\left(\frac{\alpha_1}{1-\alpha_1}\right)\right]$$ so if you can show that that the first even... | null | CC BY-SA 4.0 | null | 2023-05-31T15:34:29.807 | 2023-05-31T19:19:57.517 | 2023-05-31T19:19:57.517 | 164061 | 164061 | null |
617450 | 1 | null | null | 1 | 18 | I want to characterize the relation of a few input parameters to a single output parameter. The problem I have is that my data is collected from several groups. The groups are defined both by the input parameters and by how the input parameters interact with the output parameter. I don't know the identity or proportion... | Regression with unlabeled data from several clusters | CC BY-SA 4.0 | null | 2023-05-31T15:34:50.507 | 2023-05-31T18:42:39.910 | 2023-05-31T18:42:39.910 | 52004 | 52004 | [
"regression",
"clustering",
"unsupervised-learning"
] |
617451 | 1 | null | null | 0 | 4 | I am using R. I have a dataset that looks like this using `srt()`:
```
'data.frame': 233 obs. of 3 variables:
$ Design : Factor w/ 4 levels "Crossover","Observational",..: 2 3 3 3 4 3 3 1 3 2 ...
$ Status : Factor w/ 3 levels "Active","Passive",..: 1 1 2 2 2 2 1 1 2 1 ...
$ Outcome: Ord.factor w/ 3 levels "Positi... | Testing difference of counts of one category across a combination of the other two categories | CC BY-SA 4.0 | null | 2023-05-31T15:54:46.943 | 2023-05-31T16:06:03.730 | 2023-05-31T16:06:03.730 | 378020 | 378020 | [
"categorical-data",
"many-categories"
] |
617452 | 1 | null | null | 4 | 156 | I I want to perform a paired t-test to check if there's some effect, I have the distribution of "before" and the distribution of "after" the manipulation. Do I need to assume the population variances of the two distribution are equal ?
| equal *population* variances in paired t test | CC BY-SA 4.0 | null | 2023-05-31T16:17:54.197 | 2023-06-01T07:21:36.193 | 2023-06-01T06:23:31.740 | 53690 | 389283 | [
"hypothesis-testing",
"distributions",
"variance",
"t-test",
"paired-data"
] |
617454 | 2 | null | 617452 | 8 | null | Any assumptions you make in a paired test would have to do with the paired differences. After all, a paired test is a one-sample test in disguise.
Therefore, NO, you do not need to assume equal variances of the two groups for a paired t-test.
| null | CC BY-SA 4.0 | null | 2023-05-31T16:31:44.597 | 2023-06-01T07:21:36.193 | 2023-06-01T07:21:36.193 | 247274 | 247274 | null |
617455 | 1 | null | null | 0 | 13 | As I am reading about recommender systems in Machine Learning, UV decomposition caught my eye ([click](https://stats.stackexchange.com/questions/189730/what-is-uv-decomposition) for an explanation or see below).
So I have two questions:
Question 1: what are the drawbacks of trying to UV-decompose a 1 by m vector into a... | Is it possible to apply matrix decomposition to a vector, injecting additional information to UV decomposition? | CC BY-SA 4.0 | null | 2023-05-31T16:48:05.837 | 2023-05-31T16:48:05.837 | null | null | 389285 | [
"machine-learning",
"linear-algebra",
"recommender-system",
"svd",
"matrix-decomposition"
] |
617456 | 1 | 617551 | null | 2 | 18 | Define $\pi_i$ as the probability that person $i$ will be missing from your sample and $Y_i = 1$ denotes that a subject is missing. Say we're in a missing at random (MAR) scenario where $\pi_i$ depends on two known continuous variables $X_1$ and $X_2$:
$$logit(\pi_i) = \beta_1 x_1 + \beta_2 x_2$$
Let's say that I intro... | Baseline rate of missing values. Can missing values be MAR and MCAR? | CC BY-SA 4.0 | null | 2023-05-31T16:49:09.203 | 2023-06-01T15:15:55.903 | 2023-06-01T15:15:55.903 | 45453 | 45453 | [
"logistic",
"mathematical-statistics",
"missing-data"
] |
617457 | 1 | null | null | 0 | 16 | It has KM and model predicted curve overlayed for repeated event model produced by below code. Struggling to interpret this plot. Any help appreciated.Thanks.
1)Is this for first event or all events(counting event method)regardless of subject?
fmods = flexsurvreg(Surv(START,STOP,EVENT) ~ 1,data=data,dist="weibull")
p=g... | does ggflexsurvplot overlay KM curve with model predicted curve for the first event or all events for repeated event analysis? | CC BY-SA 4.0 | null | 2023-05-31T16:53:04.007 | 2023-05-31T21:42:51.800 | 2023-05-31T21:42:51.800 | 297005 | 297005 | [
"survival"
] |
617458 | 1 | null | null | 0 | 26 | Suppose I have m observations of $y$ vectors of varying dimensions $y_1=(y_{11},\dots, y_{1n_1}),\dots,y_m=(y_{m1},\dots, y_{mn_m})$, where $y_i$ is of dimension $n_i\geq 300$ for $1\leq i\leq m$. Let $X_i$ be corresponding covariate matrix of $y_i$ of dimension $n_i\times p$. I will denote $D=(y_1,X_1,\dots, y_m,X_m)$... | Was there any mistake in my derivation in Gibbs sampling? | CC BY-SA 4.0 | null | 2023-05-31T16:57:08.053 | 2023-05-31T19:58:30.650 | 2023-05-31T19:58:30.650 | 79469 | 79469 | [
"regression",
"bayesian",
"markov-chain-montecarlo",
"model",
"gibbs"
] |
617459 | 1 | null | null | 2 | 36 | I have a known distribution for my population, and it is very right skewed. Let's say Lognormal with mu = 0 and sigma = 3. The mean of this distribution is about 90, and the median is 1.
For a given sample, I am interested in knowing the ratio of values in excess of a certain threshold (lets say 90) to the total sum of... | Distribution Estimator dependent on sample size | CC BY-SA 4.0 | null | 2023-05-31T16:58:47.690 | 2023-05-31T19:26:33.600 | 2023-05-31T19:26:33.600 | 389281 | 389281 | [
"distributions",
"mathematical-statistics",
"expected-value"
] |
617460 | 2 | null | 617459 | 0 | null | It seems like you want an example where the expected value of an estimator depends on the sample size. Such examples certainly exist.
Consider the mean $\mu$ and an estimator $\hat\mu = \bar X + \dfrac{1}{\sqrt{n}}$, where $\bar X$ is the usual sample mean.
Then $\mathbb E\left[\hat\mu\right] = \mu + \dfrac{1}{\sqrt{n}... | null | CC BY-SA 4.0 | null | 2023-05-31T17:08:50.807 | 2023-05-31T19:16:24.583 | 2023-05-31T19:16:24.583 | 247274 | 247274 | null |
617461 | 2 | null | 617143 | 1 | null | An option is to use proportional colored circles (or squares), showing simultaneously absolute numbers and ratios.
If you want to show the absolute number of servers, while taking into account the "size of the country" (e.g. the number of inhabitants, the total number of computers in this country, or whatever you think... | null | CC BY-SA 4.0 | null | 2023-05-31T17:12:21.080 | 2023-05-31T17:21:21.847 | 2023-05-31T17:21:21.847 | 164936 | 164936 | null |
617462 | 1 | null | null | 0 | 11 | What does the StandardScaler() command do when called on other than the individual subcommands?
Here are two code examples where I get a different ML-score. From which I conclude that the standardization must be different.
Standardization with fit and transform
```
clf = KNeighborsClassifier(n_neighbors=3)
X_Scale_trai... | What is the difference between StandardScaler() in pipline and StandardScaler().fit_transform separate from the ML-qualification | CC BY-SA 4.0 | null | 2023-05-31T17:18:09.813 | 2023-05-31T17:31:31.767 | 2023-05-31T17:31:31.767 | 389289 | 389289 | [
"machine-learning",
"python",
"scikit-learn",
"standardization",
"multidimensional-scaling"
] |
617463 | 1 | null | null | 0 | 19 | I am planning a study where we have a low number of observations. We know we need to control for at least two variables, but other variables also exist that we can control for.
It seems to me that adding variables to an analysis is always best. By controlling for the variables that have the largest effect, we can reduc... | What is the effect of adding variables to an analysis on type 1 and type 2 error? | CC BY-SA 4.0 | null | 2023-05-31T17:27:11.600 | 2023-05-31T17:27:11.600 | null | null | 338681 | [
"experiment-design",
"type-i-and-ii-errors"
] |
617464 | 2 | null | 617419 | 4 | null | The good think with the pivotal method, is that you can actually find a distribution of the observations independent of the unknown parameter $\theta$ and the implicitly through that distribution construct the confidence interval for $\theta$.
So, so the goal is to create a $(1-a_{1}-a_{2})$ confidence interval for $\t... | null | CC BY-SA 4.0 | null | 2023-05-31T17:31:40.480 | 2023-05-31T17:31:40.480 | null | null | 208406 | null |
617465 | 1 | null | null | 0 | 29 |
### There are two classifiers;
- Classifier_A -> [A] Classifier_A outputs a binary variable either A or nothing (implicitly not A).
- Classifier_B -> [B, C, D] Classifier_B outputs any n-combination of B, C, D. All three variables are booleans. And a lack of an output implicitly implies that that output is false. ... | Creating a test set where predicted variables are independent, ground truth variables are mutually exclusive. Two different classifiers | CC BY-SA 4.0 | null | 2023-05-31T17:38:02.543 | 2023-06-01T11:18:32.387 | 2023-06-01T11:18:32.387 | 386952 | 386952 | [
"classification",
"categorical-data",
"binary-data"
] |
617467 | 2 | null | 617208 | 0 | null | In this question, the aim is to make inference for the average monthly rate $p$ of faulty items in a production line ($p$=number of faulty items per total). Daily count data are available over a long time period (2 years). The total number of items per day is large (thousands), and the failure rate is not very small (a... | null | CC BY-SA 4.0 | null | 2023-05-31T18:03:28.643 | 2023-05-31T18:19:25.113 | 2023-05-31T18:19:25.113 | 237561 | 237561 | null |
617468 | 1 | null | null | 0 | 14 | I am working on a multilevel analysis aiming to investigate factors that impact student GPA. The data comes from 16 different schools. To include school effects, we are using a mixed effect model (with random intercepts). However, I would like to understand the effect of school tuition on student GPA and I am confused ... | How to structure higher level effect (between clusters) in mixed-effect models | CC BY-SA 4.0 | null | 2023-05-31T11:52:28.717 | 2023-06-01T13:12:49.440 | 2023-05-31T21:02:12.420 | 11887 | 284325 | [
"r",
"regression",
"mixed-model",
"lme4-nlme",
"multilevel-analysis"
] |
617469 | 1 | null | null | 0 | 6 | In a roll-playing board game, there are various kinds of dice. Dice may have 4, 6, 8, 10 or 12 sides. On any throw, we may toss 1-5 dice of the same kind. Dice are fair. Given a sample of 100 throws, I need to determine the most likely number of faces and dice per throw.
I supposs I am searching for a goodness of fit m... | Goodness of fit test, variable dice faces and count | CC BY-SA 4.0 | null | 2023-05-31T18:05:50.913 | 2023-05-31T18:05:50.913 | null | null | 389290 | [
"chi-squared-test",
"goodness-of-fit",
"discrete-data"
] |
617470 | 2 | null | 617442 | 1 | null | If I'm understanding you correctly, $P(N\mid \mu)$ means the value at $N$ of the Poisson probability mass function with expectation $\mu.$
To find this, you need the value of $N.$ If you observe the value of $N$ you can find $Q.$ The quantity you called $\mathrm{CL}_\mathrm{s}$ can be used if you know $N\le N_0$ but yo... | null | CC BY-SA 4.0 | null | 2023-05-31T18:23:25.060 | 2023-06-02T16:50:20.283 | 2023-06-02T16:50:20.283 | 5176 | 5176 | null |
617471 | 1 | null | null | 0 | 10 | I think I understand what average precision is: the area under the precision-recall curve.The curve is constructed by calculating the precision and recall metrics at each threshold. There are a few methods how you actually calculate/approximate area but that is not the focus of my question.
For me it was clear how AP i... | Average precision in in calssification vs in object detection | CC BY-SA 4.0 | null | 2023-05-31T18:52:24.097 | 2023-05-31T18:52:24.097 | null | null | 389064 | [
"neural-networks",
"classification",
"model-evaluation",
"object-detection",
"average-precision"
] |
617472 | 1 | null | null | 0 | 14 | i have difficulties to better understand about what we commonly called a sampler, especially how to produce a covariance matrix between parameters during a MCMC code run.
In MCM, I know that we start from "guess" values and after we iterate by choosing a random value and compute the Chi2 thanks to Experimental data.
If... | Subtilities of MCMC method and more generally about covariance matrix and Samplers | CC BY-SA 4.0 | null | 2023-05-30T18:08:50.073 | 2023-06-02T16:09:42.110 | 2023-06-02T16:09:42.110 | 11887 | 389017 | [
"normal-distribution",
"markov-chain-montecarlo",
"covariance-matrix"
] |
617473 | 1 | null | null | 0 | 12 | I have been using GAMMs to analyse time series data and I have included a smoothing term (hour of day by season) and I cant seem to find the results for the winter season. I have the proper information (edf, Ref.df, F, and p value) for all my smoothed terms and each season except for winter. I am using the summary func... | How to interpret smoothing effects in the summary output of a generalised additive mixed effect model GAMM | CC BY-SA 4.0 | null | 2023-05-31T18:07:15.737 | 2023-06-02T16:10:35.350 | 2023-06-02T16:10:35.350 | 11887 | null | [
"r",
"modeling"
] |
617474 | 2 | null | 343146 | 0 | null | From the `sklearn` [documentation](https://github.com/scikit-learn/scikit-learn/blob/1495f6924/sklearn/metrics/classification.py#L500):
$$
\kappa = (p_o - p_e) / (1 - p_e)
$$
>
where $p_o$ is the empirical probability of agreement on the label assigned to any sample (the observed agreement ratio), and $p_e$ is the exp... | null | CC BY-SA 4.0 | null | 2023-05-31T19:14:02.527 | 2023-05-31T22:40:29.353 | 2023-05-31T22:40:29.353 | 247274 | 247274 | null |
617475 | 2 | null | 617338 | 0 | null | In the simpler case of independent data points, a simple two-sample t-test here would give too-low p-values because you choose the change point to create the pair of datasets with the largest possible t-statistic.
Suppose we generate a time series of 26 observations of $N(0, 1)$ observations, with no change points. If ... | null | CC BY-SA 4.0 | null | 2023-05-31T19:16:11.380 | 2023-05-31T19:16:11.380 | null | null | 78857 | null |
617476 | 2 | null | 575314 | 0 | null | >
When evaluating a machine learning (or other statistical model) against multiple evaluation metrics, is there a standardized way to choose the "best" model?
NO
It depends on what you value from your predictions. In your example, if you value a high $F_1$ score over a high dice score, you might be inclined to go wi... | null | CC BY-SA 4.0 | null | 2023-05-31T19:29:44.840 | 2023-05-31T20:04:34.557 | 2023-05-31T20:04:34.557 | 247274 | 247274 | null |
617477 | 2 | null | 501835 | 1 | null | I've found that some of my students are helped by thinking of the p-value as a percentile. They are familiar with the concepts of being in the top 10% of a class by GPAs, or "among the 1%" in terms of wealth.
So for your example, a p-value of 0.04 means "Our observed value of the test statistic $T$ was among the top 4%... | null | CC BY-SA 4.0 | null | 2023-05-31T19:31:24.873 | 2023-05-31T19:31:24.873 | null | null | 17414 | null |
617478 | 2 | null | 405872 | 1 | null | (This seems to be a near-duplicate of a question I [answered](https://stats.stackexchange.com/a/577858/247274) a year ago.)
$R^2$ is often defined as a comparison of the sum of squared residuals for the model of interest vs the sum of squared residuals for a model that only has an intercept. With this in mind, I would ... | null | CC BY-SA 4.0 | null | 2023-05-31T19:40:57.790 | 2023-05-31T19:40:57.790 | null | null | 247274 | null |
617479 | 1 | null | null | 1 | 3 | In my team we do a study on a group of patients undergoing abdominal surgery and we evaluate the correlation between frailty amongst patients and complications.
Background knowledge:
We use a frailty-score (Clinical Frailty Score, CFS) where 1 is non-frail and 9 is severe frail. Patients are categorized into three grou... | Multiple groups with multiple events of different character | CC BY-SA 4.0 | null | 2023-05-31T19:48:17.930 | 2023-05-31T19:48:17.930 | null | null | 388632 | [
"recurrent-events"
] |
617480 | 2 | null | 617429 | 0 | null | Conditioned on $X$ having value $x$, the distribution of $Y$ is $N(x,x^2)$. The conditional distribution of $Z = \dfrac YX$ given that $X=x$ is the same as the distribution of $\dfrac Yx$ which, as you have discovered, is an $N(1,1)$. distribution. Thus, $$f_{Z \mid X=x}(\alpha
\mid X=x) = \frac{\exp\left(-\frac{(\alp... | null | CC BY-SA 4.0 | null | 2023-05-31T19:48:34.930 | 2023-05-31T19:48:34.930 | null | null | 6633 | null |
617481 | 1 | null | null | 1 | 15 | What is the relation between the vector X used to create a Gaussian process prior, the X used to 'train' the GP, ie. giving it some observations (X,y), and the X* (used to make predictions of y* values)?
| Gaussian Process prior, posterior, and predictive x vectors? | CC BY-SA 4.0 | null | 2023-05-31T19:52:59.963 | 2023-05-31T19:52:59.963 | null | null | 389294 | [
"bayesian",
"normal-distribution",
"gaussian-process"
] |
617482 | 2 | null | 503081 | 0 | null | An important consideration is that your models are not giving categories. They are giving values on a continuum that are binned according to a threshold to give discrete categories (above the threshold is one category, below the threshold is the other). Moving this threshold around is what yields ROC curves.
A similar ... | null | CC BY-SA 4.0 | null | 2023-05-31T20:02:06.007 | 2023-05-31T20:02:06.007 | null | null | 247274 | null |
617483 | 2 | null | 616677 | 1 | null | In the `emmeans` call, you can specify only predictor (independent) variables. Seems like you want `Persona` there. The dependent variable is understood from the model.
You mention that `Prominence` is a moderator, and if you think that it is influenced by `Persona`, consider adding `cov.reduce = Prominence~Persona` to... | null | CC BY-SA 4.0 | null | 2023-05-31T20:21:10.417 | 2023-05-31T20:21:10.417 | null | null | 52554 | null |
617484 | 1 | null | null | -1 | 32 | I am trying to better understand the importance of "matching" in medical studies.
For example, suppose I have a dataset that has different covariates (e.g. height, weight, sex, employment, place of residence, smoking history, etc.) for a large group of people, and a response variable if a person has asthma or not (let'... | Understanding the Need for "Matching" in Medical Studies | CC BY-SA 4.0 | null | 2023-05-31T20:25:39.387 | 2023-05-31T20:25:39.387 | null | null | 77179 | [
"regression"
] |
617485 | 1 | null | null | 0 | 16 | Let's say I have $n$ samples which are vectors of length $p$. I know that the $p \times p$ sample covariance matrix is singular if $n \leq p$. Is there another estimator for the covariance that results in a non-singular matrix when $n \leq p$?
My goal is to estimate covariance from many datasets and then quickly sample... | Is there an alternate estimator for a sample covariance matrix when n < p such that the estimator is not singular | CC BY-SA 4.0 | null | 2023-05-31T20:35:27.580 | 2023-05-31T20:35:27.580 | null | null | 261708 | [
"covariance",
"estimators",
"multivariate-normal-distribution",
"svd",
"singular-matrix"
] |
617486 | 2 | null | 615790 | 0 | null | Is there a clear and precise explanation of why minimising the variance of the weights in SIS with respect to a proposal ensures that the samples generated from the empirical distribution induced by the normalised weights will be closer to the posterior/target distribution?
I tend to think of this problem in terms of t... | null | CC BY-SA 4.0 | null | 2023-05-31T20:45:03.887 | 2023-05-31T20:45:03.887 | null | null | 78857 | null |
617487 | 2 | null | 616613 | 0 | null | I think you have a nested fixed-effects structure, where `group` is nested in `sub_type`. Did not `emmeans` auto-detect this? You can make this structure explicit by omitting any term where `group` does not interact with `sub_type`:
```
mod2 <- lm(value ~ (sub_type + sub_type:group)*study_day*gender, data = dd)
```
`e... | null | CC BY-SA 4.0 | null | 2023-05-31T20:48:24.410 | 2023-05-31T20:48:24.410 | null | null | 52554 | null |
617488 | 1 | null | null | 1 | 26 | I am trying to understand how the values of the irf plots are estimated
I read following page: [https://www.statsmodels.org/stable/vector_ar.html](https://www.statsmodels.org/stable/vector_ar.html)
But I don't understand how the values of the impulse response are estimated.
I have a model that I fit with order of 3.
``... | impulse response values VAR statsmodels | CC BY-SA 4.0 | null | 2023-05-31T21:02:11.623 | 2023-06-01T06:21:49.397 | 2023-06-01T06:21:49.397 | 53690 | 246234 | [
"python",
"vector-autoregression",
"statsmodels",
"impulse-response"
] |
617489 | 2 | null | 526583 | 1 | null | Here is a drawing of a two-layer neural network.
[](https://i.stack.imgur.com/AkvZD.png)
The blue, red, purple, and grey lines represet network weights, and the black line is a bias.
Assume the pink output neuron to have sigmoid activation function so it gives a predicted probability. Then the equation is:
$$
p = \text... | null | CC BY-SA 4.0 | null | 2023-05-31T21:21:33.053 | 2023-05-31T21:21:33.053 | null | null | 247274 | null |
617490 | 1 | 617499 | null | 0 | 26 | I have the following result from a hierarchical model.[](https://i.stack.imgur.com/c3dz2.png)
I know how to write the equation for a multiple regression model. Is it possible to write a similar mathematical equation using the coefficients from this hierarchical regression model?
| How to write the results of an hierarchical regression into an equation? | CC BY-SA 4.0 | null | 2023-05-31T21:36:06.777 | 2023-06-02T02:36:29.187 | null | null | 250576 | [
"regression",
"mixed-model",
"lme4-nlme"
] |
617493 | 1 | null | null | 0 | 39 | I was always taught to use $p\times(1-p)\times n$ for binomial variance. In a textbook for actuarial problems, I have:
```
probability of death benefit
A .01 200,000
B .05 100,000
```
Using
$Var(A) = .01\times 200000^2 - (.01\times 200000)^2 = 396000000$
I get the same answer with... | When to use $p\times (1-p)\times n^2$ for variance? | CC BY-SA 4.0 | null | 2023-05-31T23:32:07.170 | 2023-06-01T07:44:26.710 | 2023-06-01T00:25:28.083 | 44269 | 114193 | [
"variance"
] |
617495 | 2 | null | 617493 | 1 | null | You are right about the variance of a binomial random variable.
In your example, the number of deaths would be modelled as a binomial variable. In the example of your textbook, the quantity of interests is however not the number of deaths, but apparently the benefit payed for one particular person in one year. This is ... | null | CC BY-SA 4.0 | null | 2023-05-31T23:56:09.783 | 2023-05-31T23:56:09.783 | null | null | 237561 | null |
617496 | 1 | null | null | 0 | 19 | I am looking to build a multi state model some packages for instance in `R` are for panel or intermittently observed data (`msm` package) which I believe would be interval censored data and others can be used to fit models where transitions times are known (packages such as `mstate` and `flexsurv`). My question is to... | Defining states in Multi-State model | CC BY-SA 4.0 | null | 2023-06-01T00:27:00.737 | 2023-06-01T15:00:59.523 | 2023-06-01T01:35:21.743 | 281323 | 281323 | [
"r",
"survival",
"censoring",
"interval-censoring",
"competing-risks"
] |
617497 | 1 | null | null | 0 | 13 | I did ANN classification using SMOTE random sampling in python but I found strange plot loss and accuracy results. This is my code:
```
#With SMOTE
sm = SMOTE(random_state=42)
Train_X2_Smote, Train_Y2_Smote = sm.fit_resample(Train_X2_Tfidf, Train_Y2)
#TRIAL 4
def reset_seeds():
np.random.seed(0)
python_random.s... | ANN uses python smote random oversampling | CC BY-SA 4.0 | null | 2023-06-01T00:50:05.163 | 2023-06-01T00:50:05.163 | null | null | 375024 | [
"neural-networks",
"python",
"data-visualization",
"oversampling",
"smote"
] |
617498 | 1 | null | null | 0 | 7 | Can someone explain me the difference between these approaches, if you want i can provide the results, but since they are quite extensive, i could attach on demand.
I'm working with the Theory of Planned Behavior, lets say
- a1,a2,a3,a4 are for construct A
- s1,s2,s3,s4 are for construct S
- p1,p2,p3,p4 are for cons... | Difference between SEM AND ols+pca | CC BY-SA 4.0 | null | 2023-06-01T01:10:40.007 | 2023-06-01T01:10:40.007 | null | null | 376081 | [
"least-squares",
"structural-equation-modeling",
"lavaan"
] |
617499 | 2 | null | 617490 | 2 | null | If LaTeX output is OK, try `equatiomatic::extract_eq(mdl)`.
For example, on this model:
```
library(lme4)
(fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy))
equatiomatic::extract_eq(fm1)
```
I get the LaTeX:
$$
\begin{aligned}
\operatorname{Reaction}_{i} &\sim N \left(\alpha_{j[i]} + \beta_{1j[i]}(\opera... | null | CC BY-SA 4.0 | null | 2023-06-01T01:24:04.603 | 2023-06-02T02:36:29.187 | 2023-06-02T02:36:29.187 | 369002 | 369002 | null |
617500 | 1 | null | null | 0 | 23 | As is in the title, I'm curious: Is including only the consecutive observations of an ID in longitudinal data a prerequisite for estimating ID level fixed effect?
For instance, I have longitudinal data with the structure of firm_id-year. There is one value of firm_id, say, 'Corp_Umbrella,' with the value of `sales` at ... | Is consecutive observations only is a prerequisite of fixed effect estimation in panel data? | CC BY-SA 4.0 | null | 2023-06-01T01:25:52.863 | 2023-06-01T02:58:01.550 | 2023-06-01T02:58:01.550 | 362671 | 130153 | [
"regression",
"panel-data",
"fixed-effects-model"
] |
617501 | 1 | null | null | 2 | 61 | I am running a statistical test to determine if females are more influenced by the framing effect. I designed a survey with three overall questions, each with a "positive frame" and a "negative frame". Each participant would be randomly chosen to answer either the positive or negative frame, and each frame would have a... | Compare and find p-value between two t-tests | CC BY-SA 4.0 | null | 2023-06-01T01:30:05.373 | 2023-06-03T02:57:21.553 | 2023-06-03T02:57:21.553 | 389304 | 389304 | [
"hypothesis-testing",
"t-test",
"multiple-comparisons",
"difference-in-difference",
"group-differences"
] |
617502 | 1 | null | null | -1 | 24 | We'd like to test if two rates (number of occurrences / number of days) are statistically different for a paper. However, for one of the rates (let's say Rate A), we have uncertainty around the exposure (number of days).
We have several different estimates for the exposure of Rate A. But the exposure variability is not... | Confidence interval for a rate where there is uncertainty around the exposure (number of days) | CC BY-SA 4.0 | null | 2023-06-01T02:04:55.017 | 2023-06-01T04:36:04.813 | 2023-06-01T02:54:38.023 | 362671 | 389305 | [
"statistical-significance",
"confidence-interval",
"uncertainty"
] |
617503 | 1 | null | null | 0 | 18 | I have been using GAMMs to analyze time series data and I have included a smoothing term (hour of day by season) and I can't seem to find the results for the winter season. I have the proper information (edf, Ref.df, F, and p-value) for all my smoothed terms and each season except for winter. I am using the summary fun... | Why am I missing the result of a smoothing effect in a GAMM while interpreting results from summary command | CC BY-SA 4.0 | null | 2023-06-01T02:38:53.557 | 2023-06-01T14:27:41.707 | 2023-06-01T14:27:41.707 | 389307 | 389307 | [
"r",
"modeling",
"mgcv"
] |
617504 | 1 | null | null | 2 | 14 | Suppose that I observe a bi-variate joint distribution over two random variables, $(X_1,X_2)$. I want to represent this joint distribution as arising from a function $F$ applied to i.i.d. uniform random variables, that is, I want to find $F:\mathbb [0,1]^2\to\mathbb R^2$ such that when $U_1,U_2$ are i.i.d. $Uniform(0,... | Uniqueness of a Latent Representation Under Monotonicity Condition? | CC BY-SA 4.0 | null | 2023-06-01T03:26:35.630 | 2023-06-01T03:26:35.630 | null | null | 188356 | [
"uniform-distribution",
"copula",
"latent-variable",
"identifiability"
] |
617505 | 1 | null | null | 1 | 9 | When I do causal mediation analysis in R package mediation::mediate(), I need to print out the standard error of the indirect effect estimate. However, I suppose there is no such output? I have two questions as following,
- Is there anyway to get the SE based on this function output?
- The output gives me the CI. An... | The CI and standard error of indirect effect in Mediation analysis | CC BY-SA 4.0 | null | 2023-06-01T04:12:28.883 | 2023-06-01T04:43:52.847 | 2023-06-01T04:43:52.847 | 386760 | 386760 | [
"confidence-interval",
"bootstrap",
"standard-error",
"mediation"
] |
617506 | 1 | null | null | 0 | 5 | $U \colon (0,\infty) \to (0,\infty)$ is called a $\rho$-varying function is $\frac{U(xt)}{U(t)} \to x^{\rho}$ as $t \to \infty$.
Here, we assume that $U$ is $\rho$-varying function with $\rho>-1$. Furthermore, we assume $U$ is locally integrable and that it's integrable on any interval of form $(0,b), b < \infty$.
In t... | For $\rho$-varying function with $\rho>-1$, $\lim_{t \to \infty} \frac{\int_0^t U(sx)ds }{\int_{N}^t U(sx)ds} = 1$ | CC BY-SA 4.0 | null | 2023-06-01T04:15:56.797 | 2023-06-01T04:15:56.797 | null | null | 260660 | [
"extreme-value",
"measure-theory"
] |
617507 | 2 | null | 617270 | 2 | null | $\mathbf X$ is a random $n$-vector that models the observation $\mathbf x$ in $\mathbb R^n$. The decision rule is specified by partitioning $\mathbb R^n$ into disjoint subsets $R_1, R_2, \cdots$: if the observation $\mathbf X$ is an element of $R_j$, then we decide or declare that $\mathsf H_j$ is the true hypothesis: ... | null | CC BY-SA 4.0 | null | 2023-06-01T04:15:59.127 | 2023-06-01T20:52:27.123 | 2023-06-01T20:52:27.123 | 6633 | 6633 | null |
617508 | 2 | null | 617502 | 0 | null | I'm not sure there's a "best way" for this. All your results will have to be taken with a big pinch of salt, given that you are basically guessing the exposure time, and you don't know if your estimates are biased. Some options are:
- Use your own knowledge of how the exposure times were guessed, to choose the best on... | null | CC BY-SA 4.0 | null | 2023-06-01T04:36:04.813 | 2023-06-01T04:36:04.813 | null | null | 369002 | null |
617509 | 1 | null | null | 0 | 12 | Let W be a discrete random variable with cmf
$$
F_{W}(w)= 1-\left(\frac{1}{2}\right)^{\lfloor w \rfloor}\ \text{if}\ w>0\ (0\ \text{otherwise}),
$$
where $\lfloor w \rfloor$ is the largest integer less than or equal to $w$.
How can I get the pmf of $Y=W^2$?
| How to get the pmf of Y=W^2 when CMF is given | CC BY-SA 4.0 | null | 2023-06-01T05:11:53.567 | 2023-06-01T05:11:53.567 | null | null | 389309 | [
"density-function",
"cumulative-distribution-function"
] |
617510 | 1 | null | null | 0 | 6 | SGD disadvantage is scale gradient to all directions and Adam is fixed it. How can it be? How is the example if depicted in a graph?
| SGD disadvantage is scale gradient to all directions and Adam is fixed it. How can it be? | CC BY-SA 4.0 | null | 2023-06-01T05:26:31.600 | 2023-06-01T05:26:31.600 | null | null | 375024 | [
"optimization",
"gradient-descent",
"gradient",
"stochastic-gradient-descent",
"adam"
] |
617511 | 1 | null | null | 2 | 56 | We know that if $X \sim N_p(\mu,\Sigma)$ then $(X-\mu)^T \Sigma^{-1}(X-\mu) \sim \chi^2_p$, does the converse hold? Is it possible for a non-multivariate Gaussian random variable to satisfy $(X-E(X))^T (cov(X))^{-1}(X-E(X)) \sim \chi^2_p$?
| Does $(X-E(X))^T (cov(X))^{-1}(X-E(X)) \sim \chi^2_p$ imply normality? | CC BY-SA 4.0 | null | 2023-06-01T05:36:29.340 | 2023-06-01T13:30:10.293 | null | null | 68301 | [
"distributions"
] |
617512 | 2 | null | 617511 | 6 | null | A super simple counter example:
Let $X \sim \mathcal{N}(0, 1)$, but let $Y = |X|$. Well, what's the distribution of $Y^2$?
| null | CC BY-SA 4.0 | null | 2023-06-01T05:45:39.373 | 2023-06-01T13:30:10.293 | 2023-06-01T13:30:10.293 | 8013 | 8013 | null |
617513 | 1 | null | null | 0 | 12 | I am learning the weighted majority algorithm in "foundation of machine learning" by Mohri. But I can not understand the conclusion from the book and other reference.
It has the statement
>
No deterministic algorithm can achieve a regret $R_T = o(T)$ over all
sequences.
How can we prove this? The book provide a scen... | Regret for deterministic algorithm | CC BY-SA 4.0 | null | 2023-06-01T05:57:43.050 | 2023-06-01T05:57:43.050 | null | null | 157934 | [
"machine-learning",
"online-algorithms"
] |
617514 | 1 | null | null | 0 | 16 | 64,810 women were screened for cervical cancer with a pap-smear test, suppose 132 of the 177 women diagnosed with cancer using colonoscopy and another 983 women tested positive through the screening program
Construct a 2x2 table and answer the following:
b. What is the prevalence of disease in this population?
c. Calcu... | how to construct a 2x2 table: 64,810 women screened for cervical cancer,132/177 diagnosed with cancer by colonoscopy,983 pos with screening program? | CC BY-SA 4.0 | null | 2023-06-01T06:43:03.690 | 2023-06-01T06:43:03.690 | null | null | 389313 | [
"biostatistics"
] |
617515 | 2 | null | 617493 | 0 | null | The squared terms do probably not refer to the parameter $n$ in a binomial distribution. Instead your computation* and the formula with a square relates to a scaled [Bernoulli distribution](https://en.wikipedia.org/wiki/Bernoulli_distribution) with support $x \in \lbrace 0, w \rbrace$.
This has variance $w^2\cdot p(1-p... | null | CC BY-SA 4.0 | null | 2023-06-01T06:53:56.110 | 2023-06-01T07:44:26.710 | 2023-06-01T07:44:26.710 | 164061 | 164061 | null |
617516 | 1 | 617518 | null | 0 | 21 | I understand the analytic proof that lasso regularisation tends to shrink coefficients to zero.
However, from a practical standpoint, most of those methods are combined with gradient optimisation (like SGD).
For this reason, gradient of the loss w.r.t. each parameter is $\lambda\texttt{sign}(w_i)$, where $\lambda$ is t... | From a computational perspective, how does the lasso regression shrink coefficients to 0? | CC BY-SA 4.0 | null | 2023-06-01T07:01:07.523 | 2023-06-01T07:53:59.090 | 2023-06-01T07:03:34.313 | 389315 | 389315 | [
"regression",
"lasso",
"regularization"
] |
617518 | 2 | null | 617516 | 2 | null | The coefficients start at zero. That is, the algorithm starts by applying a sufficiently large penalty that all the coefficient estimates are exactly zero. As the penalty is progressively decreased, coefficients start moving away from zero, one at a time.
The problem you point out is one reason that starting from a h... | null | CC BY-SA 4.0 | null | 2023-06-01T07:53:59.090 | 2023-06-01T07:53:59.090 | null | null | 249135 | null |
617519 | 1 | null | null | 0 | 5 | How do I keep `normalmixEM` from printing the number of iterations it required? I am using it in a call inside a bootstrap and the resulting dynamic report in RMarkdown becomes a beast as it prints out the required number of iterations for each bootstrap resample fit, as per the 3-line sample below:
'## number of itera... | Silencing output of `normalmixEM` from R package `mixtools` | CC BY-SA 4.0 | null | 2023-06-01T08:14:36.590 | 2023-06-01T08:19:35.143 | 2023-06-01T08:19:35.143 | 110833 | 180421 | [
"r"
] |
617520 | 2 | null | 617511 | 2 | null | There are various artificial solutions to this
- Let $Y_1$ be any zero-mean variable that is lighter-tailed than Normal and has variance at most 1. Take $Q\sim \chi^2_2$ correlated with $Y_1^2$ so that $Q-Y_1^2$ is always non-negative and then take $Y_2=\sqrt{Q}$ with a random $\pm$ sign (so that its mean is zero).
... | null | CC BY-SA 4.0 | null | 2023-06-01T08:18:29.793 | 2023-06-01T08:18:29.793 | null | null | 249135 | null |
617521 | 1 | null | null | 0 | 24 | An often cited advantage of Structural Equation Modeling (SEM) is that it is able to account for measurement error in the observed indicator variables, therefore allowing for consistent estimates in the presence of error-in-variables (in contrast to standard linear regression). It is not clear to me, however, what type... | Types of measurement error and their implications in SEM | CC BY-SA 4.0 | null | 2023-06-01T08:22:09.370 | 2023-06-01T09:56:13.223 | null | null | 321797 | [
"structural-equation-modeling",
"measurement-error"
] |
617522 | 1 | null | null | 0 | 15 | I have a dataset where `treatment` and `subject` play a role in how the data is behaving. I am fitting a linear model where I am modeling feature abundance as function of both covariates. My aim is to perform comparisons at `treatment` level while removing the effect of `subject` on the data. However, in some cases I h... | Linear models: can we trust estimated coefficients when some are not estimatable? | CC BY-SA 4.0 | null | 2023-06-01T08:42:29.573 | 2023-06-01T08:42:29.573 | null | null | 59647 | [
"multiple-regression",
"linear-model"
] |
617523 | 1 | null | null | -2 | 17 | [](https://i.stack.imgur.com/gGt4q.jpg)
[enter image description here](https://i.stack.imgur.com/iUIld.jpg)
[](https://i.stack.imgur.com/yal3y.jpg)
[](https://i.stack.imgur.com/Zz20l.jpg)
[](https://i.stack.imgur.com/8qRrS.jpg)
need interpreatation
| can you interprete my graph? | CC BY-SA 4.0 | null | 2023-06-01T08:46:49.907 | 2023-06-01T08:46:49.907 | null | null | 389320 | [
"regression",
"interpretation",
"linear"
] |
617525 | 1 | null | null | 1 | 28 | I wanted to ask conceptual about what to do with main effects?
Assume, I have randomly assigned equal two groups(let's say CBT, Control; N1=25; N2:25). I collected the depression levels at three time points (pre, pos and follow-up). At pre level, using independent sample t-test, groups did not differ each other signifi... | Which is the correct way to deal with insignificant main effect of Group condition? Stick with interaction effect in RM-Anova or perform ANCOVA? | CC BY-SA 4.0 | null | 2023-06-01T09:09:44.397 | 2023-06-01T20:03:54.300 | 2023-06-01T20:03:54.300 | 389177 | 389177 | [
"anova",
"repeated-measures",
"ancova",
"random-allocation",
"main-effects"
] |
617526 | 2 | null | 616808 | 0 | null |
## The causal effect can be identified with the right methodology
An instrumental variable (IV) can be used to estimate the causal effect even under hidden confounding. However, one has to use a suitable estimation procedure and how to best estimate effects in an IV setting is a research question of its own. The Wik... | null | CC BY-SA 4.0 | null | 2023-06-01T09:22:28.343 | 2023-06-01T09:22:28.343 | null | null | 250702 | null |
617527 | 2 | null | 358766 | 1 | null | Just a small edit to Kevin's answer:
I think there's a small typo as the derivative of the expression $\frac{p}{1-p}$ written above reaches a stationary point at $x=\frac{0.0265}{2 * 0.000462}$. So $57.36$ should be divided by 2.
| null | CC BY-SA 4.0 | null | 2023-06-01T09:30:44.667 | 2023-06-01T09:30:44.667 | null | null | 389325 | null |
617528 | 1 | null | null | 0 | 7 | How can I convert 17 joint points into pose parameters in the SMPL model
| How to convert 17 joint points in the human 3.6 dataset into pose parameters for 24 nodes in the SMPL model? | CC BY-SA 4.0 | null | 2023-06-01T09:33:24.410 | 2023-06-01T09:33:24.410 | null | null | 389326 | [
"machine-learning",
"forecasting"
] |
617529 | 2 | null | 616904 | 2 | null | If you had a large number of data points, I'd strongly recommend simply fitting a random forest while keep your response continuous. Random forests can deal with possible nonlinearities and are structurally quite robust to overfitting. There's no need to dichotomise your continuous variable - it throws away information... | null | CC BY-SA 4.0 | null | 2023-06-01T09:38:47.173 | 2023-06-01T11:21:49.797 | 2023-06-01T11:21:49.797 | 121522 | 121522 | null |
617530 | 1 | null | null | 1 | 24 | I am working with a dataset of 110.000 rows. Each row only has categorical data, most of which is also nominal. Each of these rows represents an event that has several parameters (again, nominal) and an outcome. The question I'm trying to answer is what combination of parameters gives the best result. In this case, the... | Way to find the best performing combination of categorical parameters | CC BY-SA 4.0 | null | 2023-06-01T09:55:47.027 | 2023-06-01T09:55:47.027 | null | null | 388715 | [
"machine-learning",
"categorical-data"
] |
617531 | 2 | null | 617521 | 0 | null | It depends on your measurement design and model(s) what types of measurement "error" (systematic vs. unsystematic) you can account for and whether the different sources of "error" can be separated from one another. For example, to separate random error in self report measures of depression, you need at least two measur... | null | CC BY-SA 4.0 | null | 2023-06-01T09:56:13.223 | 2023-06-01T09:56:13.223 | null | null | 388334 | null |
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