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8571 | 2 | null | 7946 | 1 | null | What kind of graph should I create to illustrate my data?
I would use a scatterplot - it would give you an idea about the type of relationship between the data. It is important to identify if the relationship is linear or not, before calculating correlation between the measurements.
| null | CC BY-SA 2.5 | null | 2011-03-21T15:39:29.850 | 2011-03-21T15:39:29.850 | null | null | 2635 | null |
8572 | 1 | 8876 | null | 24 | 7329 | I know a fair amount about fitting continuous parameters particularly gradient-based methods, but not much about fitting discrete parameters.
What are commonly used MCMC algorithms/techniques for fitting discrete parameters? Are there algorithms which are both fairly general and fairly powerful? Are there algorithms w... | What MCMC algorithms/techniques are used for discrete parameters? | CC BY-SA 2.5 | null | 2011-03-21T15:51:51.340 | 2011-03-31T16:45:44.557 | 2011-03-31T14:59:59.527 | 1146 | 1146 | [
"bayesian",
"markov-chain-montecarlo"
] |
8573 | 1 | 8580 | null | 2 | 12079 | Probability distribution of two classes is given by $N(5,1)$ and $N(6,1)$ where
$N(\mu,\sigma^2)$:
$$f(x) = \frac{1}{\sqrt{2\pi \sigma^2}} e^{-\frac{(x-\mu)^2}{2\sigma^2}} $$
- How to classify them, and see error rate?
I am doing this in MATLAB
Taking 500 samples of each distribution and tagging them depending whe... | Bayes classifier of two normal distributions in MATLAB | CC BY-SA 2.5 | null | 2011-03-21T16:06:07.347 | 2016-10-17T13:42:52.167 | 2011-03-21T18:32:43.573 | 3681 | 3681 | [
"bayesian",
"classification",
"matlab"
] |
8574 | 2 | null | 8570 | 6 | null | James Gentle's Computational Statistics (2009).
James Gentle's Matrix algebra: theory, computations, and applications in statistics (2007), more so towards the end of the book, the beginning is great too but it's not exactly what you're looking for.
Christopher M. Bishop's Pattern Recognition (2006).
Hastie et al.'s T... | null | CC BY-SA 2.5 | null | 2011-03-21T17:29:40.023 | 2011-03-21T17:29:40.023 | null | null | 2660 | null |
8576 | 5 | null | null | 0 | null | [Python](https://www.python.org/) ([Wikipedia page](https://en.wikipedia.org/wiki/Python_%28programming_language%29)) is a general purpose programming language designed for ease of use. It is a commonly used platform for machine learning. Two very popular threads concerned with using Python for statistics and machine l... | null | CC BY-SA 3.0 | null | 2011-03-21T17:40:28.580 | 2016-01-15T23:24:07.973 | 2016-01-15T23:24:07.973 | 7290 | -1 | null |
8577 | 4 | null | null | 0 | null | Python is a programming language commonly used for machine learning. Use this tag for any *on-topic* question that (a) involves `Python` either as a critical part of the question or expected answer, & (b) is not *just* about how to use `Python`. | null | CC BY-SA 4.0 | null | 2011-03-21T17:40:28.580 | 2019-10-02T16:00:15.173 | 2019-10-02T16:00:15.173 | 121522 | 2660 | null |
8578 | 5 | null | null | 0 | null | [Principal component analysis](https://en.wikipedia.org/wiki/Principal_component_analysis) is a technique to decompose an array of numerical data into a set of orthogonal vectors (uncorrelated linear combinations of the variables) called principal components. The first few principal components often suffice to grasp ne... | null | CC BY-SA 3.0 | null | 2011-03-21T17:46:43.223 | 2017-07-23T04:14:37.080 | 2017-07-23T04:14:37.080 | 7290 | 28666 | null |
8579 | 4 | null | null | 0 | null | Principal component analysis (PCA) is a linear dimensionality reduction technique. It reduces a multivariate dataset to a smaller set of constructed variables preserving as much information (as much variance) as possible. These variables, called principal components, are linear combinations of the input variables. | null | CC BY-SA 3.0 | null | 2011-03-21T17:46:43.223 | 2016-03-28T10:21:26.183 | 2016-03-28T10:21:26.183 | 3277 | 2660 | null |
8580 | 2 | null | 8573 | 2 | null | Here's how I have done this in matlab:
```
mu = [mu1,mu2];sigma = [sigma1,sigma2]; %group them
%suppose you get your test data from somewhere.
%for kicks, I put random data in:
xtest = randn(2*n,1); %OP example code has the labels in the data var; ack
deviance = bsxfun(@minus,xtest,mu); %tbc
deviance = bsxfun(@r... | null | CC BY-SA 2.5 | null | 2011-03-21T18:19:28.450 | 2011-03-21T18:19:28.450 | null | null | 795 | null |
8581 | 1 | 8613 | null | 13 | 610 |
### Context:
I'm a Psychology PhD student. As with many psychology PhD students, I know how to perform various statistical analyses using statistical software, up to techniques such as PCA, classification trees, and cluster analysis.
But it's not really satisfying because though I can explain why I did an analysis a... | Transition from using statistical software to understanding mathematical equations? | CC BY-SA 3.0 | null | 2011-03-21T18:33:17.083 | 2017-11-27T13:03:22.367 | 2020-06-11T14:32:37.003 | -1 | 3827 | [
"references",
"notation",
"software"
] |
8583 | 1 | 8584 | null | 17 | 18550 | I have conducted an analysis in which I have modeled different variance components. When reporting the results in a table, it is much more concise to report standard deviations instead of variances.
So, this brings me to the question - is there ever a reason to report variance instead of standard deviation? Is it ever ... | When would it be appropriate to report variance instead of standard deviation? | CC BY-SA 2.5 | null | 2011-03-21T19:51:20.863 | 2021-02-17T00:02:48.980 | null | null | 2750 | [
"standard-deviation",
"variance",
"tables"
] |
8584 | 2 | null | 8583 | 20 | null | If you report the mean, then it is more appropriate to report the standard deviation as it is expressed in the same unity. Think about dimensional homogeneity in physics.
Moreover, it is easier for the reader to consider confidence intervals (for large n, in order to use the Central Limit Theorem and consider a normal ... | null | CC BY-SA 2.5 | null | 2011-03-21T19:57:14.420 | 2011-03-21T19:57:14.420 | null | null | 1351 | null |
8585 | 2 | null | 8583 | 10 | null | This is similar (but not equivalent). Nonetheless, standard deviation is expressed in the same units as the variable whereas the units of the variance are those of the variable to the power two. This makes standard deviation easier to interpret.
| null | CC BY-SA 4.0 | null | 2011-03-21T19:57:27.323 | 2021-02-17T00:02:48.980 | 2021-02-17T00:02:48.980 | 311558 | 3019 | null |
8586 | 1 | 8589 | null | 9 | 387 | I have a few hundred estimates of a parameter calculated from two different models and I would like to know if these parameters have different variances.
What is a straightforward test for comparing the variances of these parameters? (straightforward meaning, least assumptions).
| How can I test $H_0:\sigma^2_1=\sigma^2_2$? | CC BY-SA 2.5 | null | 2011-03-21T20:22:14.803 | 2011-04-03T06:51:57.360 | 2011-03-21T21:34:42.577 | 2750 | 2750 | [
"hypothesis-testing",
"variance",
"mean"
] |
8589 | 2 | null | 8586 | 8 | null | For comparing variances, Wilcox suggests a percentile bootstrap method. See [chapter 5.5.1 of 'Introduction to Robust Estimation and Hypothesis Testing'](http://books.google.com/books?id=_tAJr4ooOM8C&lpg=PR1&dq=Introduction%20to%20Robust%20Estimation%20and%20Hypothesis%20Testing&pg=PA170#v=onepage&q&f=false). This is a... | null | CC BY-SA 2.5 | null | 2011-03-21T21:36:33.820 | 2011-03-24T17:17:31.463 | 2011-03-24T17:17:31.463 | 795 | 795 | null |
8590 | 1 | null | null | 13 | 25035 | I come from the social sciences, where p < 0.05 is pretty much the norm, with p < 0.1 and p < 0.01 also showing up, but I was wondering: what fields of study, if any, use lower p-values as a common standard?
| Examples of studies using p < 0.001, p < 0.0001 or even lower p-values? | CC BY-SA 2.5 | null | 2011-03-21T21:39:32.833 | 2021-05-12T07:15:55.597 | null | null | 3582 | [
"statistical-significance",
"p-value"
] |
8591 | 1 | null | null | 7 | 5036 | How do probability distributions of continuous random variables transform under functions?
I.e. I have a random variable, X, drawn from a normal distribution with mean 0 and variance 1. What is the probability distribution associated with sin(X)?
![Histograms mimicking probability density functions of X and sin(X)](htt... | Operations on probability distributions of continuous random variables | CC BY-SA 3.0 | null | 2011-03-21T16:57:30.150 | 2013-09-30T19:50:09.047 | 2020-06-11T14:32:37.003 | -1 | 3830 | [
"distributions",
"probability"
] |
8593 | 2 | null | 8590 | 9 | null | My opinion is that it does (and should) not depend on the field of study. For example, you may well work at a lower significance level than $p<0.001$ if, for example, you are trying to replicate a study with historical or well-established results (I can think of several studies on the [Stroop effect](http://en.wikipedi... | null | CC BY-SA 2.5 | null | 2011-03-21T22:14:36.940 | 2011-03-21T22:14:36.940 | null | null | 930 | null |
8595 | 2 | null | 4086 | -2 | null | you have 5 years of data and 40 observations per year. Why don't you post them on the web and allow us to actually answer this at ground zero rather than philosophizing at 500 miles high.
I look forward to the numbers. WE have seen data like this for example the number of customers who trade in their time sharing week ... | null | CC BY-SA 2.5 | null | 2011-03-21T23:19:35.030 | 2011-03-21T23:19:35.030 | null | null | 3382 | null |
8596 | 2 | null | 8590 | 8 | null | It might be rare for anyone to use a pre-specified alpha level lower than, say, 0.01, but it is not nearly as rare that people claim an implied alpha of less than 0.01 in the mistaken belief that an observed P value of less than 0.01 is the same as a Neyman-Pearson alpha of less than 0.01.
Fisher's P values are not the... | null | CC BY-SA 3.0 | null | 2011-03-21T23:26:06.033 | 2013-04-07T15:18:05.777 | 2013-04-07T15:18:05.777 | 7290 | 1679 | null |
8598 | 1 | 8602 | null | 5 | 6379 | I'm working on a problem as follows for a course that I'm auditing:
>
Suppose a 95% symmetric t-interval is applied to estimate a mean, but the
sample data are non-normal. Then the probability that the confidence interval covers
the mean is not necessarily equal to 0.95.
Use a Monte Carlo experiment to
estimat... | Monte Carlo experiment to estimate coverage probability | CC BY-SA 4.0 | null | 2011-03-21T23:43:26.520 | 2019-01-28T07:48:16.197 | 2019-01-28T07:48:16.197 | 128677 | null | [
"r",
"self-study",
"monte-carlo",
"simulation"
] |
8599 | 2 | null | 6498 | -3 | null | An ARIMA model is simply a weighted average. It answers the double question;
- How many period (k )should I use to compute a weighted average
and
- Precisely what are the k weights
It answers the maiden's prayer to determine how to adjust to previous values ( and previous values ALONE ) in order to project the ... | null | CC BY-SA 2.5 | null | 2011-03-21T23:53:18.980 | 2011-03-21T23:53:18.980 | null | null | 3382 | null |
8601 | 2 | null | 8598 | 1 | null | You have several issues with your code:
- Your mean(UCL < 0 & LCL > 0) is decidedly strange, and in particular is failing because UCL is coming out positive so you are taking the mean of an empty set. A $\chi^2$ distribution takes only positive values.
- (since solved) You have UCL less than LCL, which is a slightly... | null | CC BY-SA 2.5 | null | 2011-03-22T00:36:10.603 | 2011-03-22T07:20:49.733 | 2011-03-22T07:20:49.733 | 2958 | 2958 | null |
8602 | 2 | null | 8598 | 6 | null | I disagree with Henry - I think you should be dividing by sqrt(n), because it's a confidence interval for the mean. You also have to add a `df = n-1` argument to your qt calls.
And the last line should be `mean(LCL < 2 & UCL > 2)`. This is because 2 is the true mean, and you're interested in the condition that 2 is in ... | null | CC BY-SA 2.5 | null | 2011-03-22T02:49:38.337 | 2011-03-22T02:49:38.337 | null | null | 3835 | null |
8603 | 2 | null | 8566 | 0 | null | Naive Bayes and Logistic Regression (Classification) are both linear classifiers. If you remove all misclassified instances, then you will allow an infinite number of separators to have 0 training error. In the case of the logistic regression, this translate to your information matrix being singular (The information ma... | null | CC BY-SA 2.5 | null | 2011-03-22T02:53:50.613 | 2011-03-22T02:53:50.613 | null | null | 3834 | null |
8604 | 1 | null | null | 54 | 36528 | I admit I'm relatively new to propensity scores and causal analysis.
One thing that's not obvious to me as a newcomer is how the "balancing" using propensity scores is mathematically different from what happens when we add covariates in a regression? What's different about the operation, and why is it (or is it) better... | How are propensity scores different from adding covariates in a regression, and when are they preferred to the latter? | CC BY-SA 2.5 | null | 2011-03-22T03:41:20.293 | 2022-03-31T17:38:50.513 | null | null | 3836 | [
"regression",
"multivariate-analysis",
"causality",
"propensity-scores"
] |
8605 | 1 | 8612 | null | 26 | 57156 | I would like to perform column-wise normalization of a matrix in R. Given a matrix `m`, I want to normalize each column by dividing each element by the sum of the column. One (hackish) way to do this is as follows:
```
m / t(replicate(nrow(m), colSums(m)))
```
Is there a more succinct/elegant/efficient way to achieve ... | Column-wise matrix normalization in R | CC BY-SA 2.5 | null | 2011-03-22T04:17:39.163 | 2014-04-02T04:30:31.847 | 2011-03-23T20:16:41.383 | 1537 | 1537 | [
"r",
"data-transformation",
"normalization",
"matrix"
] |
8606 | 1 | null | null | 2 | 271 | I am working on a stopping rule for an optimization algorithm that produces an upper bound and lower bound for the objective value of an optimization problem. In my case, the lower bound is deterministic, but the upper bound is an estimate derived from $N$ data points $UB_1, UB_2... UB_N$ with mean $\widehat{UB}$ and s... | Designing a stopping rule using a hypothesis test | CC BY-SA 3.0 | null | 2011-03-22T04:28:52.787 | 2011-06-29T01:37:30.327 | 2011-06-28T17:41:17.213 | null | 3572 | [
"hypothesis-testing",
"optimization"
] |
8607 | 2 | null | 8541 | 6 | null | Short answer: Gibbs or Metropolis-Hastings-within-Gibbs (MCMC) should work just fine on joint distributions and full conditional distributions that are mixed products of pmfs and pdfs. If you're doing MCMC, just make sure that sampling from the candidate distributions gives you values in the right domain.
Long answer:
... | null | CC BY-SA 2.5 | null | 2011-03-22T04:41:00.800 | 2011-03-22T04:50:36.987 | 2011-03-22T04:50:36.987 | 3831 | 3831 | null |
8608 | 1 | 8647 | null | 2 | 1325 | I am reading this example, but could you explain a little more. I don't get the part where it says "then we Normalize"... I know
```
P(sun) * P(F=bad|sun) = 0.7*0.2 = 0.14
P(rain)* P(F=bad|rain) = 0.3*0.9 = 0.27
```
But where do they get
```
W P(W | F=bad)
-----------------
sun 0.34
rain 0.66
```
![enter ... | Decision network example | CC BY-SA 2.5 | null | 2011-03-22T04:58:01.640 | 2011-04-29T00:58:38.457 | 2011-04-29T00:58:38.457 | 3911 | 3681 | [
"probability",
"bayesian",
"conditional-probability"
] |
8610 | 2 | null | 8604 | 20 | null | The short answer is that propensity scores are not any better than the equivalent ANCOVA model, particularly with regard to causal interpretation.
Propensity scores are best understood as a data reduction method. They are an effective means to reduce many covariates into a single score that can be used to adjust an ef... | null | CC BY-SA 3.0 | null | 2011-03-22T05:19:25.640 | 2016-01-27T15:06:15.753 | 2016-01-27T15:06:15.753 | 485 | 485 | null |
8611 | 1 | 8638 | null | 4 | 805 | I have two different columns of data which are recorded in different configurations and I want to show the users that these two records varie (data is time in seconds). The dataset is not of the same size as shown below. The end users are all experienced people in stats and math. My question is how can I plot a graph ... | How to show differences between two univariate datasets graphically? | CC BY-SA 2.5 | null | 2011-03-22T05:21:30.313 | 2011-03-22T17:53:05.827 | 2011-03-22T16:38:59.353 | 919 | 3270 | [
"data-visualization",
"matlab",
"gnuplot"
] |
8612 | 2 | null | 8605 | 45 | null | This is what sweep and scale are for.
```
sweep(m, 2, colSums(m), FUN="/")
scale(m, center=FALSE, scale=colSums(m))
```
Alternatively, you could use recycling, but you have to transpose it twice.
```
t(t(m)/colSums(m))
```
Or you could construct the full matrix you want to divide by, like you did in your question. H... | null | CC BY-SA 3.0 | null | 2011-03-22T06:07:21.883 | 2014-04-02T04:30:31.847 | 2014-04-02T04:30:31.847 | 3601 | 3601 | null |
8613 | 2 | null | 8581 | 10 | null |
### Overview:
- My impression is that your experience is common to a lot of students in the social sciences.
- The starting point is a motivation to learn.
- You can go down either self-taught or formal instruction routes.
### Formal instruction:
There are many options in this regard.
You might consider a ma... | null | CC BY-SA 3.0 | null | 2011-03-22T06:18:34.707 | 2017-11-27T13:03:22.367 | 2017-11-27T13:03:22.367 | 22047 | 183 | null |
8614 | 1 | 8622 | null | 1 | 2388 | I am trying to determine if a given noise from a compass sensor is time-correlated (it is supposed to be!) and for that I tried to compute the cross correlation between the noise signal and the time of sampling using Matlab xcorr() function. However, I am getting a random value indicating that it is not time-correlated... | How to determine if a given signal is time-correlated? | CC BY-SA 2.5 | null | 2011-03-22T06:54:59.047 | 2015-01-25T15:09:19.667 | null | null | null | [
"time-series",
"matlab",
"cross-correlation"
] |
8615 | 2 | null | 8606 | 3 | null | You should take the difference between upper and lower, then t-test migh be more suitable than with ratios...
Anyway, if the probability to reject the null wrongly is $1-\alpha$ the probability to accept it wrongly won't be $\alpha$, if you want to control the error of accepting the null wrongly you have to specify on... | null | CC BY-SA 2.5 | null | 2011-03-22T07:17:13.800 | 2011-03-22T07:37:55.537 | 2011-03-22T07:37:55.537 | 223 | 223 | null |
8616 | 2 | null | 8566 | 6 | null | The following is not restricted to NB + LogRes
Overfitting = Loss of generalization.
When you train a model on dataset you generally assume that the data you use for training has a similar structure than the data the model is applied to later (the general assumption of predicting the future from the past). So if you re... | null | CC BY-SA 2.5 | null | 2011-03-22T07:39:48.263 | 2011-03-22T07:39:48.263 | 2017-04-13T12:44:32.747 | -1 | 264 | null |
8617 | 1 | 8618 | null | 19 | 1842 | An increase in the number of cases and deaths occurs during epidemics (sudden increase in numbers) due to a virus circulation (like West Nile Virus in USA in 2002) or decreasing resistance of people or contamination of food or water or increase in the number of mosquitoes.
These epidemics will present as outliers whic... | Can data cleaning worsen the results of statistical analysis? | CC BY-SA 2.5 | null | 2011-03-22T07:56:30.263 | 2011-08-16T07:34:59.977 | 2011-03-22T22:55:17.803 | null | 2956 | [
"time-series",
"forecasting",
"epidemiology",
"outliers"
] |
8618 | 2 | null | 8617 | 13 | null | It actually depends on the purpose of your research. In my opinion, there could be several:
- You want to understand what are the typical factors that causes cases and deaths and that are not affected by epidemic periods and factors that causes epidemics (so you are interested in typical not force major probabilities)... | null | CC BY-SA 2.5 | null | 2011-03-22T08:51:11.383 | 2011-03-22T08:51:11.383 | null | null | 2645 | null |
8619 | 2 | null | 8617 | 16 | null | I personally wouldn't call this "data cleaning". I think of data cleaning more in the sense of data editing - cleaning up inconsistencies in the data set (e.g. a record has reported age of 1000, or a person aged 4 is a single parent, etc.).
The presence of a real effect in your data does not make it "messy" (to the ... | null | CC BY-SA 2.5 | null | 2011-03-22T09:24:20.850 | 2011-03-22T09:24:20.850 | null | null | 2392 | null |
8620 | 2 | null | 8608 | 4 | null | This is a straight forward analysis of Bayes Theorem. Now Bayes Theorem reads:
$$P(H|F,I)=\frac{P(H|I)P(F|H,I)}{P(F|I)}$$
So in your example you have two "hypothesis" SUN and RAIN. $F$ stands for the the forecast, and $I$ for the prior information (or assumptions). The "I" has not been included explicitly in your qu... | null | CC BY-SA 2.5 | null | 2011-03-22T09:45:20.753 | 2011-03-22T09:45:20.753 | null | null | 2392 | null |
8621 | 2 | null | 8611 | 2 | null | Rather than a boxplot, I'd use a strip chart, since both data sets are small and it will be easy to see each data point.
| null | CC BY-SA 2.5 | null | 2011-03-22T10:10:29.543 | 2011-03-22T10:10:29.543 | null | null | 686 | null |
8622 | 2 | null | 8614 | 5 | null | Despite the fact that there were a lot of discussion on the relative topics (though no any answer provided), I would like to add some ideas from my own teaching and model-building experience. It would be also very useful for you to study/read any (good) textbook in econometrics or time-series analysis first (just to sp... | null | CC BY-SA 2.5 | null | 2011-03-22T11:00:05.527 | 2011-03-22T11:00:05.527 | null | null | 2645 | null |
8623 | 2 | null | 8617 | 6 | null | The role of "data cleansing" is to identify when "our laws (model) do not work". Adjusting for Outliers or abnormal data points serve to allow us to get "robust estimates" of the parameters in the current model that we are entertaining. These "outliers" if untreated permit an unwanted distortion in the model parameters... | null | CC BY-SA 2.5 | null | 2011-03-22T11:18:37.447 | 2011-03-24T12:21:41.397 | 2017-04-13T12:44:21.160 | -1 | 3382 | null |
8624 | 2 | null | 4604 | 2 | null | To me, it sounds more like you want a conditional frequency, as a conditional probability has no "error" so to speak. The only error from a probability is from either from a mathematical approximation, or a mathematical error in the calculation. Once you make this conceptual distinction, I think finding the exact mea... | null | CC BY-SA 2.5 | null | 2011-03-22T11:32:23.003 | 2011-03-22T11:32:23.003 | null | null | 2392 | null |
8625 | 1 | 8671 | null | 16 | 10229 | I was fiddling with PCA and LDA methods and I am stuck at a point, I have a feeling that it is so simple that I can't see it.
Within-class ($S_W$) and between-class ($S_B$) scatter matrices are defined as:
$$
S_W = \sum_{i=1}^C\sum_{t=1}^N(x_t^i - \mu_i)(x_t^i - \mu_i)^T
$$
$$
S_B = \sum_{i=1}^CN(\mu_i-\mu)(\mu_i-\mu)^... | Deriving total (within class + between class) scatter matrix | CC BY-SA 3.0 | null | 2011-03-22T12:43:24.383 | 2018-01-15T12:16:49.350 | 2018-01-15T12:16:49.350 | 28666 | 760 | [
"discriminant-analysis"
] |
8626 | 2 | null | 8581 | 2 | null | I understand your difficulty as I have a similar problem when I try to do something new in statistics (I'm also a grad student, but in a different field). I have found examining the R code quite useful to get an idea how something is calculated. For example, I have been recently learning how to use `kmeans` clustering ... | null | CC BY-SA 2.5 | null | 2011-03-22T13:11:06.113 | 2011-03-22T13:30:20.690 | 2017-05-23T12:39:26.203 | -1 | 2635 | null |
8627 | 2 | null | 8604 | 7 | null | A likely obtuse reference, but if you by chance have access to it I would recommend reading this book chapter ([Apel and Sweeten, 2010](http://dx.doi.org/10.1007/978-0-387-77650-7_26)). It is aimed at social scientists and so perhaps not as mathematically rigorous as you seem to want, but it should go into enough depth... | null | CC BY-SA 2.5 | null | 2011-03-22T13:14:03.273 | 2011-03-22T13:14:03.273 | 2017-04-13T12:44:41.980 | -1 | 1036 | null |
8628 | 2 | null | 8617 | 6 | null | To give you a general answer to your question, let me parapharse one of my old general managers: the opportunities of research are found in the outliers of the model you are fitting.
The situation is similar to the experiment performed my Robert Millikan in determining the charge of an electron. Decades after winning ... | null | CC BY-SA 2.5 | null | 2011-03-22T13:25:28.850 | 2011-03-22T13:25:28.850 | null | null | 3805 | null |
8629 | 2 | null | 8581 | 3 | null | I get the impression that you think that you can get insight into a statistical equation
by programming it into either R or C++; you can't. To understand a statistical equation,
find an "undergraduate" textbook with lots of homework problems at the end of each chapter that contains the equation, and then do the homewor... | null | CC BY-SA 2.5 | null | 2011-03-22T13:58:45.933 | 2011-03-22T13:58:45.933 | null | null | 3805 | null |
8630 | 1 | null | null | 19 | 3223 | I have carried out a principal components analysis of six variables $A$, $B$, $C$, $D$, $E$ and $F$. If I understand correctly, unrotated PC1 tells me what linear combination of these variables describes/explains the most variance in the data and PC2 tells me what linear combination of these variables describes the nex... | Principal component analysis "backwards": how much variance of the data is explained by a given linear combination of the variables? | CC BY-SA 3.0 | null | 2011-03-22T14:00:23.313 | 2016-08-24T22:58:15.310 | 2015-01-28T09:21:45.423 | 28666 | 3845 | [
"variance",
"pca",
"r-squared",
"covariance-matrix"
] |
8631 | 1 | null | null | 7 | 571 | I want to understand how to make calculations on the prevalence of a disease in a country population and the impact that the element of average life expectancy (of those suffering with the disease at time of diagnosis) has on this calculation.
Are there any 'best practice' papers on making epidemiology calculations?
| How would life expectancy impact the calculation of disease prevalence? | CC BY-SA 2.5 | null | 2011-03-22T14:12:22.203 | 2012-09-02T15:56:25.523 | 2012-09-02T15:56:25.523 | 919 | 3844 | [
"epidemiology"
] |
8632 | 1 | null | null | 1 | 3274 | The [ISO VIM](http://www.iso.org/sites/JCGM/VIM/JCGM_200e.html) defines them as:
>
measurement method:
generic description of a logical organization of operations used in a
measurement.
measurement procedure: detailed description of a measurement according to one or more measurement principles and to a given measu... | What's the difference between "measurement method" and "measurement procedure"? | CC BY-SA 2.5 | null | 2011-03-22T14:28:02.757 | 2011-06-30T07:27:45.080 | 2011-03-23T08:23:14.400 | 2645 | 3823 | [
"teaching",
"terminology",
"measurement",
"methodology"
] |
8633 | 1 | null | null | 2 | 640 | If I find that my covariate (reaction time) alters over the length of my experiment (e.g. due to fatigue), can I somehow build that into my model?
So what I am saying is that the effect of my covariate is not constant (between subjects and within subjects).
| ANCOVA with multiple instances of the between-subject covariate | CC BY-SA 2.5 | null | 2011-03-22T14:57:53.010 | 2011-08-25T01:34:29.183 | 2011-03-22T22:51:11.193 | null | 3822 | [
"repeated-measures",
"ancova"
] |
8634 | 1 | 8648 | null | 9 | 7852 | Given two bivariate normal distributions $P \equiv \mathcal{N}(\mu_p, \Sigma_p)$ and $Q \equiv \mathcal{N}(\mu_q, \Sigma_q)$, I am trying to calculate the Jensen-Shannon divergence between them, defined (for the discrete case) as:
$JSD(P\|Q) = \frac{1}{2} (KLD(P\|M)+ KLD(Q\|M))$
where $KLD$ is the Kullback-Leibler... | Jensen-Shannon divergence for bivariate normal distributions | CC BY-SA 2.5 | null | 2011-03-22T16:15:30.263 | 2022-10-17T03:56:20.170 | 2011-03-23T01:53:13.153 | 2970 | 3843 | [
"normal-distribution",
"distance-functions",
"information-theory"
] |
8637 | 2 | null | 2181 | 11 | null | Almost the same question was asked recently on the [ISOSTAT](http://www.lawrence.edu/fast/jordanj/isostat.html) listserver (frequented by college professors):
>
If you had a strong undergraduate student who was interested in learning about various multivariate methods (e.g. PCA, MANOVA, discriminant analysis, ...) is ... | null | CC BY-SA 2.5 | null | 2011-03-22T17:21:14.290 | 2011-03-22T17:21:14.290 | null | null | 919 | null |
8638 | 2 | null | 8611 | 3 | null | I would try a q-q plot if you have enough data;
```
%make fake data;
x1 = randn(1000,1) .^ 2;x2 = (1.3 * randn(2000,1)).^2;
%which quantiles?
alphas = linspace(0,1,100);alphas = alphas(2:end-1);
q1 = interp1(linspace(0,1,numel(x1)),sort(x1),alphas,'linear');
q2 = interp1(linspace(0,1,numel(x2)),sort(x2),alphas,'linear'... | null | CC BY-SA 2.5 | null | 2011-03-22T17:21:44.560 | 2011-03-22T17:53:05.827 | 2011-03-22T17:53:05.827 | 795 | 795 | null |
8639 | 1 | 8641 | null | 4 | 160 | Greetings,
Is it possible to use evidence in a Winbug model? For example, a random variable in a model has been observed, and I'd like to update the other variables in the model, pretty much the same update perfomed in tools like Smile, or other inference software.
Gibbs sampling is supposed to use observed values in... | Can I insert an observation (evidence) to a Winbugs model? | CC BY-SA 2.5 | null | 2011-03-22T17:23:12.153 | 2016-09-12T19:30:54.530 | 2016-09-12T19:30:54.530 | 28666 | 3280 | [
"inference",
"bugs"
] |
8641 | 2 | null | 8639 | 2 | null | Of course it's possible to use evidence from observations in WinBUGS! Try working through any of the examples in the documentation that comes with the program to see how.
| null | CC BY-SA 2.5 | null | 2011-03-22T18:58:10.133 | 2011-03-22T18:58:10.133 | null | null | 449 | null |
8642 | 1 | 8643 | null | 8 | 5966 | I want to perform a single-tail test on a single sample of real numbers (N~100) against an expected value. The population is known to be not normally distributed. So from what I've read about stats, I can do my testing using
- Wilcoxon signed rank test, or
- bootstrap shifted sample data to obtain the null distribu... | What method is preferred, a bootstrapping test or a nonparametric rank-based test? | CC BY-SA 3.0 | null | 2011-03-22T18:58:38.307 | 2023-05-09T17:07:10.133 | 2017-04-13T12:44:28.873 | -1 | 3847 | [
"hypothesis-testing",
"nonparametric",
"bootstrap",
"wilcoxon-signed-rank"
] |
8643 | 2 | null | 8642 | 1 | null | You just described the difference. No one can know in advance outcome differences because it greatly depends on the nature of your data.
Do you know the non-normal distribution you're working with? If so, you could simulate some results and see what the typical error rates for the different tests were and how they ... | null | CC BY-SA 2.5 | null | 2011-03-22T19:52:01.620 | 2011-03-22T19:52:01.620 | null | null | 601 | null |
8644 | 2 | null | 6772 | 4 | null | Following whuber's [link to Wikipedia](http://en.wikipedia.org/wiki/Fieller%27s_theorem#Case_1) you have
>
Assume that $a$ and $b$ are jointly
normally distributed, and that $b$ is
not too near zero (i.e. more
specifically, that the standard error
of $b$ is small compared to $b$)
$$\operatorname{Var} \left( \... | null | CC BY-SA 2.5 | null | 2011-03-22T20:22:25.160 | 2011-03-22T20:22:25.160 | null | null | 2958 | null |
8645 | 2 | null | 8614 | 0 | null | Following DC's excellent summary of available approaches let me add:
The question "if a given noise from a compass sensor is time-correlated " raises suggestions as how to analyse it in order to make a conclusion. In the absence of user-specified possible support/explanatory series one is left with approaches that ente... | null | CC BY-SA 2.5 | null | 2011-03-22T21:48:37.590 | 2011-03-31T22:39:28.480 | 2011-03-31T22:39:28.480 | 3382 | 3382 | null |
8647 | 2 | null | 8608 | 5 | null | Research has shown that people have difficulty reasoning in terms of probabilities but can do so accurately when presented with the same questions in terms of frequencies. So, let's consider a closely related setting where the probabilities are expressed as numbers of occurrences:
- In 100 similar situations, it rain... | null | CC BY-SA 2.5 | null | 2011-03-22T22:45:45.143 | 2011-03-22T22:45:45.143 | null | null | 919 | null |
8648 | 2 | null | 8634 | 9 | null | The midpoint measure $\newcommand{\bx}{\mathbf{x}} \newcommand{\KL}{\mathrm{KL}}M$ is a mixture distribution of the two multivariate normals, so it does not have the form that you give in the original post. Let $\varphi_p(\bx)$ be the probability density function of a $\mathcal{N}(\mu_p, \Sigma_p)$ random vector and $\... | null | CC BY-SA 2.5 | null | 2011-03-22T23:34:52.187 | 2011-03-26T14:30:10.350 | 2017-04-13T12:44:53.513 | -1 | 2970 | null |
8649 | 1 | 8650 | null | 10 | 1158 | I'm using a tutorial I found and plotting mean values along with the standard errors to show my data. But I'm having a problem discussing the results. My plot is as shown below: some of the standard errors (shown as a error bar) vary much and some of them are very close to zero.
![enter image description here](https://... | What is standard error used for? | CC BY-SA 3.0 | null | 2011-03-23T00:50:44.210 | 2014-03-12T11:51:58.750 | 2013-08-16T18:41:32.250 | 601 | 3270 | [
"data-visualization",
"standard-error"
] |
8650 | 2 | null | 8649 | 10 | null | Error bars in general are to convince the plot reader that the differences she/he sees on the plot are statistically significant. In an approximation, you may imagine a small gaussian which $\pm1\sigma$ range is shown as this error bar -- "visual integration" of a product of two such gaussians is more-less a chance tha... | null | CC BY-SA 2.5 | null | 2011-03-23T01:06:09.497 | 2011-03-23T01:06:09.497 | null | null | null | null |
8651 | 1 | null | null | 2 | 342 | Is there a way to find the Spearman correlation between two Weibull distributions? I need it as a parameter in a copula function for the joint Weibull distribution.
I learned that using the Pearson correlation, which I can easily obtain from the variances and cross variances of the given spectra, is not reliable with c... | Spearman correlation of two Weibull distributions | CC BY-SA 3.0 | null | 2011-03-23T02:48:08.760 | 2018-07-22T17:27:49.700 | 2018-07-22T17:27:49.700 | 11887 | 3854 | [
"correlation",
"spearman-rho",
"copula",
"weibull-distribution"
] |
8652 | 2 | null | 8649 | 6 | null | As mbq says, error bars are a way of letting your readers to get a feel if the differences between two groups are significant - i.e. if the variation within each of your groups is small enough to believe that the difference you've found for the mean between your groups.
All else being equal, larger error bars mean mor... | null | CC BY-SA 2.5 | null | 2011-03-23T04:01:23.240 | 2011-03-23T04:01:23.240 | null | null | 3732 | null |
8653 | 1 | null | null | 4 | 94 | I am interested in estimating how many subjects should be included in a brain imaging study. Although the design is a fairly straight forward cross-sectional comparison, there are a number of tweakable image processing steps between the raw image and the processed image in which we carry out pixel-wise comparisons.
I'v... | How to model the relationship between number of subjects per group, derived using standard power analysis methods, and study specific parameters | CC BY-SA 2.5 | null | 2011-03-23T05:45:42.340 | 2011-05-26T21:50:31.813 | null | null | 3855 | [
"modeling",
"model-selection",
"statistical-power",
"cross-section"
] |
8655 | 2 | null | 8604 | 25 | null | One big difference is that regression "controls for" those characteristics in a linear fashion. Matching by propensity scores eliminates the linearity assumption, but, as some observations may not be matched, you may not be able to say anything about certain groups.
For example, if you are studying a worker training pr... | null | CC BY-SA 4.0 | null | 2011-03-23T06:18:03.407 | 2020-04-12T03:57:27.420 | 2020-04-12T03:57:27.420 | 116587 | 401 | null |
8656 | 2 | null | 8632 | 2 | null | Actually if drop measurement and leave only or narrowed question: What's the difference between method and procedure? A nice answer could be found [here](http://wiki.answers.com/Q/What_is_the_difference_between_a_method_and_a_procedure). From this answer we may conclude that method is a wider concept (more abstract) as... | null | CC BY-SA 3.0 | null | 2011-03-23T07:35:19.457 | 2011-06-30T07:27:45.080 | 2011-06-30T07:27:45.080 | 2116 | 2645 | null |
8657 | 1 | 8679 | null | 0 | 81 | which is the % of population with vote rights(above 18yo for example). Can u point me to some papers which talks about voting and ages?
| vote population | CC BY-SA 2.5 | null | 2011-03-23T08:37:03.867 | 2011-03-23T18:45:20.533 | null | null | 3856 | [
"population"
] |
8658 | 2 | null | 8632 | 1 | null | If you are familiar with programming, you could perhaps think of it as a method (not to be confused with a logical part of some code) being a brief description of an algorithm in pseudo-code, whereas a procedure is a specific implementation with exact syntax.
Admitted that this is not a perfect metaphor but I think it ... | null | CC BY-SA 2.5 | null | 2011-03-23T08:41:36.297 | 2011-03-23T08:41:36.297 | null | null | 3014 | null |
8659 | 2 | null | 8642 | 6 | null | This answer may be helpful, and/or it may be annoying. Your welcome and my apologies at the same time :)
One thing to remember when using a normal distribution, is that it has a set of sufficient statistics, namely the mean and variance. What this indicates is that only the mean and variance matter in the inference. ... | null | CC BY-SA 2.5 | null | 2011-03-23T09:18:53.263 | 2011-03-23T09:18:53.263 | null | null | 2392 | null |
8660 | 2 | null | 8502 | 4 | null | This is "taylor made" almost for a Bayesian regression. First of all, there is nothing "fundamentally wrong" with what you suggest. You result may not be optimal by some mathematical standard, but it will almost certainly be optimal time wise. Most other methods will involve much more time than a straight multiplica... | null | CC BY-SA 2.5 | null | 2011-03-23T09:47:40.163 | 2011-03-23T09:47:40.163 | null | null | 2392 | null |
8661 | 1 | 8667 | null | 56 | 195140 | I'm trying to undertake a logistic regression analysis in `R`. I have attended courses covering this material using STATA. I am finding it very difficult to replicate functionality in `R`. Is it mature in this area? There seems to be little documentation or guidance available. Producing odds ratio output seems to requi... | Logistic Regression in R (Odds Ratio) | CC BY-SA 2.5 | null | 2011-03-23T09:59:21.777 | 2022-10-14T12:56:01.700 | 2011-03-23T10:18:41.703 | 2824 | 2824 | [
"r",
"logistic",
"odds-ratio"
] |
8662 | 1 | 8674 | null | 14 | 3929 | I have the sample population of a certain signal's registered amplitude maxima. Population is about 15 million samples. I produced a histogram of the population, but cannot guess the distribution with such a histogram.
EDIT1: File with raw sample values is here: [raw data](http://hotfile.com/dl/111583549/5c73384/TDETQ_... | Need help identifying a distribution by its histogram | CC BY-SA 2.5 | null | 2011-03-23T10:20:57.830 | 2023-03-13T15:24:53.887 | 2011-03-24T04:54:13.743 | 2820 | 2820 | [
"distributions",
"histogram"
] |
8663 | 1 | null | null | 2 | 1274 | I used `summary.formula` from `Hmisc` with continuous `Age` and binary outcome `O` with `test=TRUE`. This returned a p-value for `Age` predicting `O` (if I understand this correctly).
I then ran a `glm` using `Age` and `O` (univariate logistic regression), which returned a different p-value. I thought that the p-valu... | Chi-squared versus logistic regression | CC BY-SA 2.5 | null | 2011-03-23T10:21:41.670 | 2011-03-24T16:12:05.140 | 2011-03-24T16:12:05.140 | null | 2824 | [
"logistic",
"chi-squared-test",
"p-value"
] |
8664 | 1 | null | null | 2 | 2442 | I want to carry out a power analysis on a one group repeated measures experiment using G*power.
I have a group of subjects who tested a set of products. Each subject test one time the products. Hence, I have one observation per cell.
To test the product effect, I used a two anova model with subjects as random effect ... | How to estimate correlation among repeated measures? | CC BY-SA 2.5 | null | 2011-03-23T10:35:06.573 | 2011-05-26T20:50:30.690 | 2011-03-23T13:49:22.730 | 2116 | 3858 | [
"correlation",
"repeated-measures"
] |
8665 | 2 | null | 8649 | 2 | null | Plenty of researchers have trouble interpreting these graphs. See [http://scienceblogs.com/cognitivedaily/2008/07/31/most-researchers-dont-understa-1/](http://scienceblogs.com/cognitivedaily/2008/07/31/most-researchers-dont-understa-1/) for a more detailed elaboration.
| null | CC BY-SA 3.0 | null | 2011-03-23T10:54:10.860 | 2013-08-16T18:13:06.457 | 2013-08-16T18:13:06.457 | -1 | 1048 | null |
8666 | 2 | null | 8661 | 46 | null | You are right that R's output usually contains only essential information, and more needs to be calculated separately.
```
N <- 100 # generate some data
X1 <- rnorm(N, 175, 7)
X2 <- rnorm(N, 30, 8)
X3 <- abs(rnorm(N, 60, 30))
Y <- 0.5*X1 - 0.3*X2 - 0.4*X3 + 10 + rnorm(N, 0, 12)
# dichotomize Y and do ... | null | CC BY-SA 2.5 | null | 2011-03-23T11:27:58.007 | 2011-03-23T11:27:58.007 | 2017-04-13T12:44:56.303 | -1 | 1909 | null |
8667 | 2 | null | 8661 | 47 | null | if you want to interpret the estimated effects as relative odds ratios, just do `exp(coef(x))` (gives you $e^\beta$, the multiplicative change in the odds ratio for $y=1$ if the covariate associated with $\beta$ increases by 1). For profile likelihood intervals for this quantity, you can do
```
require(MASS)
exp(cbind(... | null | CC BY-SA 2.5 | null | 2011-03-23T11:28:45.930 | 2011-03-23T11:28:45.930 | null | null | 1979 | null |
8668 | 2 | null | 8662 | 1 | null | I am not sure why you would want to classify a sample to a specific distribution with such a large sample size; parsimony, comparing it to another sample, looking for physical interpretation of the paramters?
Most statistical packages(R, SAS, Minitab) allow one to plot data on a graph that yields a straight line if the... | null | CC BY-SA 2.5 | null | 2011-03-23T11:43:00.033 | 2011-03-23T11:43:00.033 | null | null | 3805 | null |
8669 | 1 | 9376 | null | 2 | 202 | I currently have two sets of input variables say, $X$ and $Y$ with one output variable $Z$. That is:
$$Z = a_0 + a_1X_1 + a_2X_2... + a_{11}X_{11} = b_0 + b_1Y_1 + b_2Y_2 + b_3Y_3 + b_4Y_4$$
I have the independent $X$ and $Y$ values but don't have the dependent variable $Z$ values.
Is there anyway that I can estimate c... | Two sets of input variables for the same unknown dependent variable | CC BY-SA 2.5 | null | 2011-03-23T12:02:12.467 | 2011-04-09T18:43:50.403 | 2011-03-23T13:47:27.210 | 2116 | 3859 | [
"regression"
] |
8670 | 2 | null | 8669 | 0 | null | This sounds to me like problem where a canonical correlation study might help. In canonical correlation, we are given a random vector W that is partitioned into two sub-random vectors X and Y; and the issue is to find linear combinations of the two subvectors that have maximal correlation and are orthogonal to one anot... | null | CC BY-SA 2.5 | null | 2011-03-23T12:21:50.093 | 2011-03-23T12:21:50.093 | null | null | 3805 | null |
8671 | 2 | null | 8625 | 9 | null | If you assume
$$\frac{1}{N}\sum_{t=1}^Nx_t^{i}=\mu_i$$
Then
$$\sum_{i=1}^C\sum_{t=1}^N(x_t^i-\mu_i)(\mu_i-\mu)^T=\sum_{i=1}^C\left(\sum_{t=1}^N(x_t^i-\mu_i)\right)(\mu_i-\mu)^T=0$$
and formula holds. You deal with the second term in the similar way.
| null | CC BY-SA 2.5 | null | 2011-03-23T14:12:28.437 | 2011-03-23T14:12:28.437 | null | null | 2116 | null |
8672 | 2 | null | 8661 | 23 | null | The UCLA stats page has [a nice walk-through](https://stats.oarc.ucla.edu/r/dae/logit-regression/) of performing logistic regression in R. It includes a brief section on calculating odds ratios.
| null | CC BY-SA 4.0 | null | 2011-03-23T14:39:55.110 | 2022-10-14T12:56:01.700 | 2022-10-14T12:56:01.700 | 370174 | 124 | null |
8673 | 2 | null | 5346 | 3 | null | I found [this article by Algina & Olejnik (1984)](http://epm.sagepub.com/content/44/1/39.short).
The abstract:
>
The Welch-James procedure may be used
to test hypotheses on means, when
independent samples from populations
with heterogeneous variances are
available. Until recently the
complexity of the avail... | null | CC BY-SA 3.0 | null | 2011-03-23T14:55:49.300 | 2016-09-05T14:04:50.877 | 2016-09-05T14:04:50.877 | 100369 | 3861 | null |
8674 | 2 | null | 8662 | 23 | null | Use `fitdistrplus`:
Here's the [CRAN link](http://cran.r-project.org/web/packages/fitdistrplus/index.html) to `fitdistrplus`.
Here's the [old vignette link](https://r-forge.r-project.org/scm/viewvc.php/*checkout*/www/fitdistrplusE.pdf?revision=19&root=riskassessment&pathrev=21) for `fitdistrplus`.
If the vignette link ... | null | CC BY-SA 4.0 | null | 2011-03-23T15:04:06.113 | 2023-03-13T15:24:53.887 | 2023-03-13T15:24:53.887 | 11887 | 2775 | null |
8675 | 2 | null | 8642 | 1 | null | The inferences generated by Wilcoxon vs bootstrapping cannot be compared as they pertain to different data. Wilcoxon is a rank test, thus generates inferences that pertain to ranks. Bootstrapping applies to the raw data, and thus generates inferences that pertain to the raw data. If you dislike bootstrapping but want i... | null | CC BY-SA 2.5 | null | 2011-03-23T15:33:25.353 | 2011-03-23T15:33:25.353 | null | null | 364 | null |
8676 | 2 | null | 8630 | 5 | null | Let the total variance, $T$, in a data set of vectors be the sum of squared errors (SSE) between the vectors in the data set and the mean vector of the data set,
$$T = \sum_{i} (x_i-\bar{x}) \cdot (x_i-\bar{x})$$
where $\bar{x}$ is the mean vector of the data set, $x_i$ is the ith vector in the data set, and $\cdot$ ... | null | CC BY-SA 3.0 | null | 2011-03-23T15:34:35.570 | 2015-01-28T09:23:33.247 | 2015-01-28T09:23:33.247 | 28666 | 3864 | null |
8677 | 1 | 8687 | null | 6 | 1037 | I just came by [a post talking about](http://www.investuotojas.eu/?p=464) networks for displaying correlations:
Is this a known method? Can someone shed some insights into it? (I'm wondering about how useful it might be, and when.)
| References for using networks to display correlations? | CC BY-SA 2.5 | null | 2011-03-23T15:47:58.887 | 2011-03-23T19:55:45.100 | 2011-03-23T19:55:45.100 | 26 | 253 | [
"data-visualization",
"correlation"
] |
8678 | 2 | null | 8677 | 4 | null | Surprisingly, as a [search of Google Images](http://www.google.com/images?q=multiple+correlation) indicates, such graphs do not appear to be in common use to study or explain multiple correlations. That's a pity, because I'm sure much of this theory can be reduced to simple operations on graphs.
Nevertheless, this gra... | null | CC BY-SA 2.5 | null | 2011-03-23T16:36:48.973 | 2011-03-23T16:36:48.973 | null | null | 919 | null |
8679 | 2 | null | 8657 | 3 | null | You can find minimum voting ages in [Wikipedia](http://en.wikipedia.org/wiki/Voting_age). Most large countries use 18, except for Brazil and Indonesia.
You can find country population by age in the [U.S Census Bureau International Data Base](http://www.census.gov/ipc/www/idb/). It does not seem to use 18 as a break po... | null | CC BY-SA 2.5 | null | 2011-03-23T16:42:38.700 | 2011-03-23T18:45:20.533 | 2011-03-23T18:45:20.533 | 930 | 2958 | null |
8680 | 2 | null | 8502 | 1 | null | Maybe you should look into "stacking". Or even "feature-weighed stacking".
The former is using a cross validation method to determine the weights you should use to linearly stack them. The latter is using "meta-parameters" to give even more insight on how to weight the parameters depending on what is being predicted. T... | null | CC BY-SA 2.5 | null | 2011-03-23T16:55:09.020 | 2011-03-23T16:55:09.020 | null | null | 3834 | null |
8681 | 2 | null | 6920 | 1 | null | The problem is more easily solved when you rewrite things a little bit:
Y = y
X = [x, 1 ]
then
Y = A*X
A one time-solution is found by calculating
V = X' * X
and
C = X' * Y
note the V should have size N-by-N and C a size of N-by-M.
The parameters you're looking for are then given by:
A = inv(V) * C
Since both V and C ... | null | CC BY-SA 2.5 | null | 2011-03-23T17:29:07.060 | 2011-03-23T17:29:07.060 | null | null | 3867 | null |
8682 | 2 | null | 8663 | 1 | null | First, you need to know what a p-value is. A p-value is the probability that you would observe results as extreme, or more extreme, than the ones you have, if the null hypothesis was in fact true.
The reason you aren't getting the same p-value in your two tests, is that you aren't examining the same null hypotheses un... | null | CC BY-SA 2.5 | null | 2011-03-23T17:41:08.740 | 2011-03-23T17:41:08.740 | null | null | 3748 | null |
8683 | 2 | null | 7897 | 0 | null | Lasso is indeed a good one. Simple things like starting with none, and adding them one by one sorted on 'usefullness' (via cross-validation) do also work quite well in practice.
This is sometimes called stagewise feedforward selection.
Note that the subset selection problem is fairly independent on the type of classif... | null | CC BY-SA 2.5 | null | 2011-03-23T17:46:42.810 | 2011-03-23T17:46:42.810 | null | null | 3867 | null |
8684 | 2 | null | 8502 | 7 | null | You are retaining $p$ (=3 in this case) values for each regression: the estimated coefficients. If you are willing to retain $p(p+1)$ (=12) values per regression, you can weight your results in a way that is equivalent to having all the data and performing a weighted least squares regression with them en masse.
The an... | null | CC BY-SA 2.5 | null | 2011-03-23T17:56:30.697 | 2011-03-23T17:56:30.697 | null | null | 919 | null |
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