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10249 | 1 | null | null | 0 | 281 | I was reading OkTrends and [came across this](http://blog.okcupid.com/index.php/page/3/):
>
In fact, 32% of successful couples
agreed on all of them—which is 3.7×
the rate of simple coincidence.
So, my question is: what is simple coincidence? How is it calculated?
I can't find a Wikipedia article about simple c... | What is simple coincidence? | CC BY-SA 3.0 | null | 2011-05-02T18:04:51.363 | 2013-09-04T21:17:02.627 | null | null | 4441 | [
"probability"
] |
10250 | 1 | null | null | 10 | 4083 | I have data on the percent of organic matter in lake sediments from 0 cm (i.e., the sediment - water interface) down to 9 cm for approximately 25 lakes. In each lake 2 cores were taken from each location so I have 2 replicate measures of organic matter percentage at each sediment depth for each lake.
I am intereste... | How to summarize and compare non-linear relationships? | CC BY-SA 3.0 | null | 2011-05-02T22:05:57.513 | 2012-06-25T06:33:28.883 | null | null | 4048 | [
"regression",
"nonlinear-regression"
] |
10251 | 1 | 10256 | null | 62 | 29861 | Principal component analysis can use matrix decomposition, but that is just a tool to get there.
How would you find the principal components without the use of matrix algebra?
What is the objective function (goal), and what are the constraints?
| What is the objective function of PCA? | CC BY-SA 3.0 | null | 2011-05-02T23:10:16.580 | 2015-02-07T01:06:13.063 | 2015-02-07T01:06:13.063 | 28666 | 74 | [
"pca"
] |
10253 | 2 | null | 10250 | 2 | null | Check out [Generalized Additive Models](http://en.wikipedia.org/wiki/Generalized_additive_model), which permit fitting non-linear functions without a priori specification of the non-linear form. I'm not sure how one would go about comparing the subsequent fits however. Another similar (in that I believe they both emplo... | null | CC BY-SA 3.0 | null | 2011-05-02T23:20:45.080 | 2011-05-03T02:21:12.163 | 2011-05-03T02:21:12.163 | 364 | 364 | null |
10254 | 2 | null | 10220 | 1 | null | If you know the domain of the random variable and maybe have knowledge of some other properties like the mean, variance, etc. but want to be ignorant in a fair way about all other aspects of the distribution, you can find a distribution by applying the [principle of maximum entropy](http://en.wikipedia.org/wiki/Princip... | null | CC BY-SA 3.0 | null | 2011-05-03T00:58:11.377 | 2011-05-03T01:11:31.460 | 2011-05-03T01:11:31.460 | 4360 | 4360 | null |
10255 | 2 | null | 10251 | 5 | null | See NIPALS ([wiki](http://en.wikipedia.org/wiki/Non-linear_iterative_partial_least_squares)) for one algorithm which doesn't explicitly use a matrix decomposition. I suppose that's what you mean when you say that you want to avoid matrix algebra since you really can't avoid matrix algebra here :)
| null | CC BY-SA 3.0 | null | 2011-05-03T01:50:30.833 | 2011-05-03T01:50:30.833 | null | null | 26 | null |
10256 | 2 | null | 10251 | 58 | null | Without trying to give a full primer on PCA, from an optimization standpoint, the primary objective function is the [Rayleigh quotient](http://en.wikipedia.org/wiki/Rayleigh_quotient). The matrix that figures in the quotient is (some multiple of) the sample covariance matrix
$$\newcommand{\m}[1]{\mathbf{#1}}\newcommand... | null | CC BY-SA 3.0 | null | 2011-05-03T02:27:02.357 | 2011-05-04T07:49:25.030 | 2011-05-04T07:49:25.030 | 4376 | 2970 | null |
10257 | 2 | null | 421 | 5 | null | So many wonderful recommendations! It's not quite what you asked for, but [How to Lie with Statistics](http://en.wikipedia.org/wiki/How_to_Lie_with_Statistics) is short and quite wonderful. It doesn't directly teach the things you want, but it does help point out violation of assumptions and other flaws.
| null | CC BY-SA 3.0 | null | 2011-05-03T02:40:27.687 | 2011-05-03T02:40:27.687 | null | null | 3874 | null |
10258 | 2 | null | 10049 | 0 | null | Based on the output you shared, Maximum # of branches from a node is set at 2. It's possible that raising that limit would give you more options for branches, especially if SAS can take continuous variables and break them up into categories. It's data dredgy, but that's the game we're in, and as long as you crossvali... | null | CC BY-SA 3.0 | null | 2011-05-03T03:14:21.113 | 2011-05-03T03:14:21.113 | null | null | 2669 | null |
10259 | 2 | null | 10049 | 0 | null | If you are using tree-based methods, you can play around with the splitting criterion. For example, at each step, choose the split that gives the highest weighted accuracy (the average of the two classes' accuracies).
This can be used as the basis for a random forest too, which should give you a good classifier.
I once... | null | CC BY-SA 3.0 | null | 2011-05-03T03:56:31.520 | 2011-05-03T03:56:31.520 | null | null | 2067 | null |
10260 | 2 | null | 10251 | 30 | null | The solution presented by cardinal focuses on the sample covariance matrix. Another starting point is the reconstruction error of the data by a q-dimensional hyperplane. If the p-dimensional data points are $x_1, \ldots, x_n$ the objective is to solve
$$\min_{\mu, \lambda_1,\ldots, \lambda_n, \mathbf{V}_q} \sum_{i=1}^n... | null | CC BY-SA 3.0 | null | 2011-05-03T04:20:44.130 | 2011-05-04T06:25:29.590 | 2011-05-04T06:25:29.590 | 4376 | 4376 | null |
10261 | 1 | 10263 | null | 3 | 1347 | While im going through the derivation of E step in EM algorithm for pLSA, i came across the following derivation [at this page](http://www.hongliangjie.com/2010/01/04/notes-on-probabilistic-latent-semantic-analysis-plsa/). Could anyone explain me how the following step is derived.
$\sum_z q(z) log \frac{P(X|z,\theta)P... | Derivation of E step in EM algorithm | CC BY-SA 3.0 | null | 2011-05-03T06:23:35.117 | 2014-04-02T04:29:02.523 | 2011-05-03T12:57:33.020 | 930 | 4290 | [
"expectation-maximization",
"latent-semantic-analysis"
] |
10262 | 2 | null | 6705 | 4 | null | Moran's I statistic is used to explore a specific type of spatial clustering: whether high values are located in proximity to other high values and whether low values are located in proximity to other low values.
The trick then is 1st to get a sense of what you mean by proximity and 2nd formulating this mathematically.... | null | CC BY-SA 3.0 | null | 2011-05-03T06:40:58.367 | 2011-05-03T08:46:51.997 | 2011-05-03T08:46:51.997 | 4329 | 4329 | null |
10263 | 2 | null | 10261 | 4 | null | It looks like [Bayes' formula](http://en.wikipedia.org/wiki/Bayes%27_theorem) :
$\Pr[A \mid B] = \frac{\Pr[B \mid A] \Pr[A]}{\Pr[B]}$
Here, it gives:
$\Pr[X \mid z, \theta] = \frac{\Pr[z \mid X, \theta] \Pr[X \mid \theta]}{\Pr[z \mid \theta]}$
| null | CC BY-SA 3.0 | null | 2011-05-03T06:47:38.257 | 2011-05-03T06:47:38.257 | 2020-06-11T14:32:37.003 | -1 | 3019 | null |
10264 | 2 | null | 3268 | 2 | null | An alternative to multidimentional scaling is making a map of the each group's position to one another as a SOM (Self Organising Maps). Just like you see with a geographic map of the United States with Kansas in the middle, the groups that are positioned near the middle of your SOM map would be the groups that are most... | null | CC BY-SA 3.0 | null | 2011-05-03T06:52:24.267 | 2011-05-03T08:26:00.337 | 2011-05-03T08:26:00.337 | 4329 | 4329 | null |
10265 | 1 | 10278 | null | 5 | 974 | In the paper of [Probabilistic Latent Semantic Analysis](http://www.cs.brown.edu/~th/papers/Hofmann-UAI99.pdf) by Hofmann, the author fits the model for document $\times$ word matrix through EM Algorithm in section 3. I was able to follow the derivation and meaning of the model derived in it.
However in the later sect... | What is a "tempered EM algorithm"? | CC BY-SA 3.0 | null | 2011-05-03T09:12:35.067 | 2019-10-04T09:30:43.383 | 2011-05-03T11:06:56.247 | null | 4290 | [
"expectation-maximization",
"latent-semantic-analysis"
] |
10267 | 1 | null | null | 6 | 5216 | I am building a Box-Jenkins model in Excel using solver. The model is AR(2).
The data that I have contains trend and seasonality both.
I know how to remove seasonality using seasonal indexes and add it back to the forecast.
But, how do I handle trend? If I remove trend from the data, how should I add it back to the f... | Predicting forecasts for next 12 months using Box-Jenkins | CC BY-SA 3.0 | null | 2011-05-03T12:08:37.473 | 2016-05-23T10:12:22.290 | 2016-05-23T10:12:22.290 | 1352 | 4445 | [
"time-series",
"forecasting",
"arima",
"box-jenkins"
] |
10268 | 1 | null | null | 3 | 449 | I've got a question concerning the estimation of a tobit model with the [AER](http://cran.r-project.org/web/packages/AER/index.html) package in R. I observed t-distributed residuals, so that the assumption of normal distributed std errors is violated. Fortunately it's possible to choose "t" as the distribution when fit... | Tobit model with t-distribution | CC BY-SA 3.0 | null | 2011-05-03T10:58:28.040 | 2023-03-31T00:06:30.737 | 2011-05-03T18:42:17.707 | 71 | null | [
"r",
"regression",
"tobit-regression"
] |
10269 | 2 | null | 10267 | 1 | null | Your approach suggests initially adjusting in a deterministic manner the impact of seasonality. This approach may or may not be applicable as the impact of seasonality may be auto-projective in form. The best way to answer this question is to evaluate alternative final models for adequacy in terms of separating the obs... | null | CC BY-SA 3.0 | null | 2011-05-03T12:28:47.897 | 2011-05-03T12:53:10.690 | 2011-05-03T12:53:10.690 | 3382 | 3382 | null |
10270 | 2 | null | 10049 | 4 | null | The problem is more with the choice of the accuracy scoring rule. Make sure that the ultimate goal is classification as opposed to prediction. The proportion classified correctly is a discontinuous improper scoring rule. An improper scoring rule is one that is optimized by a bogus model. With an improper scoring ru... | null | CC BY-SA 3.0 | null | 2011-05-03T13:25:29.633 | 2011-05-03T13:25:29.633 | null | null | 4253 | null |
10271 | 1 | null | null | 14 | 12439 | I am working with a time series of anomaly scores (the background is anomaly detection in computer networks). Every minute, I get an anomaly score $x_t \in [0, 5]$ which tells me how "unexpected" or abnormal the current state of the network is. The higher the score, the more abnormal the current state. Scores close to ... | Automatic threshold determination for anomaly detection | CC BY-SA 3.0 | null | 2011-05-03T13:35:45.367 | 2011-05-19T18:56:20.710 | 2011-05-05T08:59:25.660 | 4446 | 4446 | [
"time-series",
"outliers",
"threshold"
] |
10272 | 5 | null | null | 0 | null | For two or more dependent variables, use [multivariate-regression](/questions/tagged/multivariate-regression).
Linear regression models a variable (the "dependent variable") as varying randomly with respect to a linear combination of other variables (the "independent variables"). Multiple regression includes two or mo... | null | CC BY-SA 4.0 | null | 2011-05-03T13:39:20.200 | 2022-07-25T13:54:29.357 | 2022-07-25T13:54:29.357 | 121522 | 919 | null |
10273 | 4 | null | null | 0 | null | Regression that includes two or more non-constant independent variables. | null | CC BY-SA 4.0 | null | 2011-05-03T13:39:20.200 | 2022-07-25T13:55:51.503 | 2022-07-25T13:55:51.503 | 919 | 919 | null |
10276 | 1 | 10281 | null | 3 | 1775 | I have a response variable measured at three time points per individual (week 0, 18, and 36). I am interested in differences in the change of the response over the 36 weeks within some categorical variable X.
I see two ways of modeling this.
- One way ANOVA with response = week_36_score - week_0_score (this seems li... | Best model for change in scores over three time points | CC BY-SA 3.0 | null | 2011-05-03T14:06:30.987 | 2011-05-04T01:56:24.750 | 2011-05-03T15:38:06.650 | 183 | 2310 | [
"anova",
"modeling",
"repeated-measures",
"panel-data"
] |
10277 | 1 | 10282 | null | 9 | 507 | This is a rather general question (i.e. not necessarily specific to statistics), but I have noticed a trend in the machine learning and statistical literature where authors prefer to follow the following approach:
Approach 1: Obtain a solution to a practical problem by formulating a cost function for which it is possi... | Advantages of approaching a problem by formulating a cost function that is globally optimizable | CC BY-SA 3.0 | null | 2011-05-03T14:46:00.920 | 2016-08-19T23:05:55.560 | 2016-08-19T22:47:54.237 | 22468 | 2798 | [
"optimization",
"function"
] |
10278 | 2 | null | 10265 | 3 | null | I found an answer via Google in a [UTexas Paper](http://www.ma.utexas.edu/users/zmccoy/report.pdf). As I suspected from the name, it combines a temperature that decreases ala Simulated Annealing, changing the E step of the algorithm slightly.
| null | CC BY-SA 3.0 | null | 2011-05-03T15:03:25.993 | 2011-05-03T15:03:25.993 | null | null | 1764 | null |
10279 | 1 | null | null | 0 | 96 | I am analyzing a large of dataset (n>100) of incident rates, with the aim of forming a normal distribution. Then I will know if a future incident rate (x%) is either close to a historical mean or not, and can score/rate it accordingly with an already created formula.
The data is positively-skewed, as most data points ... | Analyzing historical incident rates and rating future performance | CC BY-SA 3.0 | null | 2011-05-03T15:23:07.630 | 2011-05-03T17:17:19.583 | 2011-05-03T17:17:19.583 | null | 4450 | [
"normal-distribution",
"data-transformation",
"skewness"
] |
10280 | 2 | null | 10279 | 4 | null | You don't need to transform to a normal distribution to see if a particular value is the top tenth or top fifth of observations. All you need to do is sort your observations (and count them).
| null | CC BY-SA 3.0 | null | 2011-05-03T16:12:25.170 | 2011-05-03T16:12:25.170 | null | null | 2958 | null |
10281 | 2 | null | 10276 | 2 | null | In general, I would go with a repeated measures design.
There is nothing technically wrong with the first option. However, you are essentially throwing away 1/3 of your data (and 1/2 of your non-baseline data!), which may result in a lost of power. Additionally, since you have a measurement in between baseline and 36 ... | null | CC BY-SA 3.0 | null | 2011-05-03T16:12:50.613 | 2011-05-03T16:12:50.613 | null | null | 2144 | null |
10282 | 2 | null | 10277 | 3 | null | My believe is that the goal should be to optimize the function you are interested in. If that happens to be the number of misclassifications - and not a binomial likelihood, say - then you should try minimizing the number of misclassifications. However, for the number of practical reasons mentioned (speed, implementati... | null | CC BY-SA 3.0 | null | 2011-05-03T17:45:30.897 | 2011-05-04T16:12:42.777 | 2011-05-04T16:12:42.777 | 4376 | 4376 | null |
10283 | 2 | null | 10271 | 1 | null | Do you have any 'labeled' examples of what constitutes an anomaly? i.e. values associated with a network failure, or something like that?
One idea you might consider applying is a ROC curve, which is useful for picking threshholds that meet a specific criteria, like maximizing true positives or minimizing false negativ... | null | CC BY-SA 3.0 | null | 2011-05-03T18:18:53.510 | 2011-05-03T18:18:53.510 | null | null | 2817 | null |
10284 | 2 | null | 10267 | 5 | null | If you are at all familiar with [R](http://www.r-project.org/) (if you're building time series models, you should be), check out the [forecast](http://cran.r-project.org/web/packages/forecast/index.html) package. It's designed to choose parameters for Arima as well as exponential smoothing models, and uses a solid meth... | null | CC BY-SA 3.0 | null | 2011-05-03T18:23:46.203 | 2011-05-04T15:17:58.293 | 2011-05-04T15:17:58.293 | 2817 | 2817 | null |
10285 | 1 | null | null | 5 | 25168 |
### Context
I ran an experiment with `3 x 2` design with three levels of within subjects factor (repeated measures) and two levels to the between subjects factors.
I am interested in examining the changes from baseline and the interaction effect.
### Question
- How do I compute the required sample size for a ... | Sample size required for mixed design ANOVA to achieve adequate statistical power | CC BY-SA 3.0 | null | 2011-05-03T18:31:11.137 | 2011-05-04T16:12:13.730 | 2011-05-04T04:03:35.047 | 183 | 4453 | [
"anova",
"repeated-measures",
"statistical-power"
] |
10289 | 1 | null | null | 171 | 289532 | At work we were discussing this as my boss has never heard of normalization. In Linear Algebra, Normalization seems to refer to the dividing of a vector by its length. And in statistics, Standardization seems to refer to the subtraction of a mean then dividing by its SD. But they seem interchangeable with other possibi... | What's the difference between Normalization and Standardization? | CC BY-SA 3.0 | null | 2011-05-03T20:26:45.730 | 2021-10-09T18:06:13.350 | 2017-11-15T08:39:52.533 | 101426 | 4455 | [
"descriptive-statistics",
"normalization",
"standardization"
] |
10290 | 2 | null | 10121 | 1 | null | If you are trying to generate random correlation matrices, consider sampling from the Wishart distribution. This following question provides information the Wishart distribution as well as advice on how to sample:
[How to efficiently generate random positive-semidefinite correlation matrices?](https://stats.stackexchan... | null | CC BY-SA 3.0 | null | 2011-05-03T20:40:19.043 | 2011-05-03T20:40:19.043 | 2017-04-13T12:44:26.710 | -1 | 2773 | null |
10291 | 2 | null | 10289 | 50 | null | In the business world, "normalization" typically means that the range of values are "normalized to be from 0.0 to 1.0". "Standardization" typically means that the range of values are "standardized" to measure how many standard deviations the value is from its mean. However, not everyone would agree with that. It... | null | CC BY-SA 3.0 | null | 2011-05-03T21:02:01.230 | 2011-05-04T03:51:50.550 | 2011-05-04T03:51:50.550 | 2775 | 2775 | null |
10292 | 2 | null | 10289 | 7 | null | The answer is simple, but you're not going to like it: it depends. If you value 1 standard deviation from both scores equally, then standardization is the way to go (note: in fact, you're [studentizing](http://en.wikipedia.org/wiki/Studentized_range), because you're dividing by an estimate of the SD of the population).... | null | CC BY-SA 3.0 | null | 2011-05-03T21:08:58.627 | 2011-05-03T21:08:58.627 | null | null | 4257 | null |
10293 | 1 | null | null | 3 | 961 | I have some data that a downstream system needs an optimized function of boolean logic for. Essentially I have data similar to:
```
User cat1 cat2 cat3 cat4
1 0 0 0 1
2 1 0 0 1
3 0 1 1 0
```
I must optimize cat4 as a function like this: `(cat1 or cat2 or cat3)`. "Or" is th... | Optimize a boolean function | CC BY-SA 3.0 | null | 2011-05-03T21:47:17.023 | 2017-04-08T18:21:13.197 | 2017-04-08T18:21:13.197 | 11887 | 739 | [
"machine-learning",
"optimization",
"many-categories"
] |
10294 | 2 | null | 10276 | 4 | null | There are difficulties in computing change. This doesn't work on ordinal repsonses, and for continuous responses makes a strong assumption of proper choice of transformations for the variables. I recommend adjusting for baseline and modeling the 2nd and 3rd measurements as longitudinal measurements.
Repeated measur... | null | CC BY-SA 3.0 | null | 2011-05-03T22:08:01.730 | 2011-05-04T01:56:24.750 | 2011-05-04T01:56:24.750 | 4253 | 4253 | null |
10295 | 1 | null | null | 22 | 391 | I am trying to put together a data-mining package for StackExchange sites and in particular, I am stuck in trying to determine the "most interesting" questions. I would like to use the question score, but remove the bias due to the number of views, but I don't know how to approach this rigorously.
In the ideal world, I... | "Interestingness" function for StackExchange questions | CC BY-SA 3.0 | null | 2011-05-03T21:53:26.910 | 2011-05-05T01:16:53.857 | 2011-05-05T00:13:04.400 | 4456 | 4456 | [
"data-mining",
"predictive-models"
] |
10297 | 2 | null | 498 | 1 | null | I have had the exact same problem . . . in fact I'm having right now! It seems to matter whether or not I include the blue-colored labels from the output window. Try copying only the text in black (the table fillin's) and see if that does the trick. It worked for me just now, and then when I go back and try again to... | null | CC BY-SA 3.0 | null | 2011-05-03T22:59:01.330 | 2011-05-03T22:59:01.330 | null | null | 4459 | null |
10298 | 2 | null | 10289 | 122 | null | Normalization rescales the values into a range of [0,1]. This might be useful in some cases where all parameters need to have the same positive scale. However, the outliers from the data set are lost.
$$ X_{changed} = \frac{X - X_{min}}{X_{max}-X_{min}} $$
Standardization rescales data to have a mean ($\mu$) of 0 and... | null | CC BY-SA 3.0 | null | 2011-05-04T00:05:54.767 | 2016-12-31T18:27:47.567 | 2016-12-31T18:27:47.567 | 73527 | 2202 | null |
10299 | 2 | null | 498 | 1 | null | I have the problem sometimes. It seems as long as I do not highlight the whole output window but instead highlight all of it except the last line and leave a little extra space at the end of the line it always works.
If you are facing the problem try copying just the middle section and see if it works, if so then this... | null | CC BY-SA 3.0 | null | 2011-05-04T02:39:22.600 | 2011-05-04T02:39:22.600 | null | null | 2310 | null |
10300 | 2 | null | 7200 | 13 | null | I suppose I'm too late the hero, but I wanted to comment on cardinal's post, and this comment became too big for its intended box.
For this answer, I'm assuming $x >0$; appropriate reflection formulae can be used for negative $x$.
I'm more used to dealing with the error function $\mathrm{erf}(x)$ myself, but I'll try t... | null | CC BY-SA 3.0 | null | 2011-05-04T03:37:21.730 | 2011-05-04T13:33:45.540 | 2011-05-04T13:33:45.540 | 830 | 830 | null |
10301 | 2 | null | 10293 | 1 | null | Think you mean something like which categories you should take into account to union them with the OR operator to get a good probability to predict the last variable?
On your training set, try to develop a model based on minimum binary integer programming (mBIP; as proposed here: [http://www.sce.carleton.ca/faculty/ch... | null | CC BY-SA 3.0 | null | 2011-05-04T04:30:36.277 | 2011-05-04T04:30:36.277 | null | null | 1158 | null |
10302 | 1 | 10340 | null | 57 | 44387 | I came across term perplexity which refers to the log-averaged inverse probability on unseen data. Wikipedia [article](http://en.wikipedia.org/wiki/Perplexity) on perplexity does not give an intuitive meaning for the same.
This perplexity measure was used in [pLSA](http://www.cs.brown.edu/~th/papers/Hofmann-UAI99.pdf)... | What is perplexity? | CC BY-SA 3.0 | null | 2011-05-04T06:04:26.560 | 2021-12-10T23:40:29.340 | 2021-12-10T23:40:29.340 | 11887 | 4290 | [
"intuition",
"information-theory",
"measurement",
"perplexity"
] |
10303 | 1 | 10304 | null | 4 | 108 | Using OLS, I've estimated the following equation:
$y_i = \alpha_0 + \alpha_1 X_i + \epsilon_i$
I know that theoretically, the following should be true:
$y_i = a + (1-e^{-\lambda 60}) X_i$
Is there any way, having an estimate of $\alpha_1$ I can translate it to an estimate of $\lambda$?
As a follow up, if this is not ... | Using an OLS coefficient to estimate a non-linear coefficient | CC BY-SA 3.0 | null | 2011-05-04T06:45:56.653 | 2011-05-04T07:20:52.663 | null | null | 726 | [
"distributions",
"estimation",
"normal-distribution",
"log-linear"
] |
10304 | 2 | null | 10303 | 5 | null | Judging from your equations there is no reason for the OLS estimate of $\alpha_1$ not to be consistent and asymptotically normal. So you can use plug-in estimate for $\lambda$:
$$\hat{\lambda}=-\frac{1}{60}\log(1-\hat{\alpha}_1)$$
Using [delta method](http://en.wikipedia.org/wiki/Delta_method) it would be possible to s... | null | CC BY-SA 3.0 | null | 2011-05-04T07:20:52.663 | 2011-05-04T07:20:52.663 | null | null | 2116 | null |
10305 | 1 | null | null | 4 | 1329 | When does/can one use the likelihood ratio significance test instead of Fisher's exact test or its Pearson $\chi^2$ approximation for comparing two binomial datasets?
Given two binomial datasets (distributions), I'm seeing the LR test being used to compare one distribution against the global (combined) distribution. Us... | Likelihood ratio test vs. $\chi^2$/Z-test for comparing binomial datasets | CC BY-SA 3.0 | null | 2011-05-04T07:38:37.180 | 2017-11-06T12:21:09.443 | 2017-11-06T12:21:09.443 | 101426 | 1720 | [
"hypothesis-testing",
"likelihood-ratio",
"fishers-exact-test"
] |
10306 | 2 | null | 10267 | 3 | null | The time series are usually [decomposed](http://en.wikipedia.org/wiki/Decomposition_of_time_series) into 3 parts, trend, seasonality and irregular. (The link gives 4 parts, but cyclical and seasonality are usually lumped together). Strictly speaking ARIMA type of models are only used for irregular part and by their des... | null | CC BY-SA 3.0 | null | 2011-05-04T08:02:11.270 | 2011-05-04T08:02:11.270 | null | null | 2116 | null |
10307 | 2 | null | 10285 | 8 | null |
- You need to decide what is acceptable statistical power for a given significance test. The rule of thumb of 80% power being reasonable is often bandied about.
However, I think it is more sensible to see sample size selection as an optimisation problem, where statistical power is but one consideration, and the cost o... | null | CC BY-SA 3.0 | null | 2011-05-04T08:25:15.447 | 2011-05-04T16:12:13.730 | 2011-05-04T16:12:13.730 | 183 | 183 | null |
10308 | 1 | null | null | 9 | 2492 | I would like to explore the different ways one can detrend a time series without look ahead bias.
I wanted to use the Hodrick Prescott filter, which seems like a quite good frequency filter, but it is based on an optimization method, and I understand that it may give strange and volatile results at the border.
Wavelet ... | How to remove trend with no look ahead bias? | CC BY-SA 3.0 | null | 2011-05-04T08:30:37.473 | 2013-10-09T06:01:19.347 | 2011-05-04T11:02:09.977 | 1709 | 1709 | [
"time-series",
"econometrics",
"trend"
] |
10309 | 1 | null | null | 6 | 9142 | I am carrying out a statistical analysis for my research. I am using a Friedman's test with post hoc analysis. At present I am using the function `friedman.test.with.post.hoc` available for R software. This function reports "maxT" value instead of the chi-square value. Someone can explain me what is maxT and its relati... | Friedman's test and post-hoc analysis | CC BY-SA 3.0 | null | 2011-05-04T09:07:08.330 | 2011-05-13T09:55:18.950 | 2011-05-04T09:46:04.787 | 930 | 4461 | [
"hypothesis-testing",
"anova",
"nonparametric",
"repeated-measures",
"permutation-test"
] |
10310 | 2 | null | 10308 | 0 | null | De-trending requires a pre-specification of of how many values do you require before declaring that a new trend has started. Given this specification , say n values then one has to be concerned with distinguishing between Level Shifts ( i.e. intercept changes ) and time trend changes. If you assume that there are no Le... | null | CC BY-SA 3.0 | null | 2011-05-04T09:12:27.980 | 2011-05-04T09:12:27.980 | null | null | 3382 | null |
10311 | 1 | 10312 | null | 7 | 730 | I analyze a set of multivariate measurements. It is known that several pairs of independent variables show high linear correlation. The graph below shows a scatterplot of one such pair (X and Y, upper pane), as well the residuals as a function of Y (lower left pane) and the histogram of these residuals (lower right pan... | Weird residuals in linear regression | CC BY-SA 3.0 | null | 2011-05-04T11:03:30.273 | 2012-07-25T15:10:14.870 | 2012-07-25T15:10:14.870 | 3748 | 1496 | [
"regression",
"dataset",
"outliers",
"residuals"
] |
10312 | 2 | null | 10311 | 9 | null | What is the value of the residual that shows such a high count? It does not appear to be zero (slightly to the right of 0), so maybe 1? In any case, there may be something about that value that may provide you with some insight about the underlying mechanism. For example, if X and Y are measurements taken by observers,... | null | CC BY-SA 3.0 | null | 2011-05-04T12:03:26.133 | 2011-05-04T12:03:26.133 | null | null | 1934 | null |
10313 | 2 | null | 10305 | 3 | null | Generally speaking, the likelihood ratio and the ordinary Pearson $\chi^2$ tests are more accurate than Fisher's "exact" test. But for your situation you need an extremely heavy multiplicity adjustment thrown in, not matter which statistical test is used. Decision trees such as the one you are building require amazin... | null | CC BY-SA 3.0 | null | 2011-05-04T12:42:45.710 | 2011-05-04T12:42:45.710 | null | null | 4253 | null |
10314 | 2 | null | 9040 | 4 | null | checkout Gephi, this software has some very good layout algorithms to handle the spaghetti problem: [http://gephi.org/features/](http://gephi.org/features/)
Especially, try the ForceAtlas layout: [http://forum.gephi.org/viewtopic.php?f=26&t=926](http://forum.gephi.org/viewtopic.php?f=26&t=926)
The software let you cont... | null | CC BY-SA 3.0 | null | 2011-05-04T13:50:39.353 | 2011-05-04T13:50:39.353 | null | null | 4443 | null |
10315 | 2 | null | 9435 | 1 | null | Gephi, an open source network visualization software, can do that: [http://gephi.org](http://gephi.org)
See [this recent discussion](http://forum.gephi.org/viewtopic.php?f=29&t=1016) on what a user was able to do with a bipartite graph, which is what you have. It is also called a bipartite network, or a two-mode netwo... | null | CC BY-SA 3.0 | null | 2011-05-04T13:57:44.040 | 2011-05-04T13:57:44.040 | null | null | 4443 | null |
10316 | 1 | null | null | 13 | 43904 | I'm working on a multiple logistic regression in R using `glm`. The predictor variables are continuous and categorical. An extract of the summary of the model shows the following:
```
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.451e+00 2.439e+00 1.005 0.3150
Age 5.74... | Interpreting logistic regression output in R | CC BY-SA 3.0 | null | 2011-05-04T14:49:20.663 | 2018-02-07T22:11:40.747 | 2013-10-24T13:32:53.260 | 28740 | 2824 | [
"r",
"logistic",
"interpretation",
"p-value"
] |
10317 | 2 | null | 10316 | 8 | null | There are a host of questions here on the site that will help with the interpretation of the models output (here are three different examples, [1](https://stats.stackexchange.com/q/3628/1036) [2](https://stats.stackexchange.com/q/8106/1036) [3](https://stats.stackexchange.com/q/6740/1036) , and I am sure there are more... | null | CC BY-SA 3.0 | null | 2011-05-04T15:15:26.640 | 2018-02-07T22:11:40.747 | 2018-02-07T22:11:40.747 | 194258 | 1036 | null |
10318 | 2 | null | 10308 | 3 | null | There is no way to get rid of the end effects.
Like any interpolation technique, the HP method depends on data before and after the current location to provide a filtered point/line for that location. As you approach either end of the data series and drop below the required number of future (or past) points, you eith... | null | CC BY-SA 3.0 | null | 2011-05-04T15:42:02.307 | 2011-05-04T15:42:02.307 | null | null | 2775 | null |
10320 | 1 | null | null | 5 | 154 | Context
I have a set of data that was collected from several inertial measurement units (orientation and acceleration data). I want to determine to what extent an inference method degrades when the data becomes noisy (or, should I say "noisier").
Questions
How do I determine what type of noise to use? Is there a way in... | Determing noise type and level of noise | CC BY-SA 3.0 | null | 2011-05-04T15:56:25.277 | 2011-05-04T15:56:25.277 | null | null | 3052 | [
"regression",
"white-noise"
] |
10321 | 2 | null | 10058 | 3 | null | Following Nick Sabbe's answer, here is the simplest GLMM solution I can come up with:
```
dej$location <- factor(rep(1:25,2))
library(lme4)
glmer(count ~ type1 + type2*species +
perc.for.100m + perc.dry.100m + perc.wet.100m +
(1|location), family = poisson, data = dej)
```
It would be a good idea to check for ... | null | CC BY-SA 3.0 | null | 2011-05-04T16:50:48.563 | 2011-05-04T16:50:48.563 | null | null | 2126 | null |
10322 | 1 | 10327 | null | 4 | 338 | My girlfriend is an Actuarial Analyst at a large insurance company in the Netherlands and because we'll soon have our two year anniversary, I thought of gifts for her.
On [Proof: Math is beautiful](http://proofmathisbeautiful.tumblr.com/post/5104877044/baseln-statistical-distribution-plushies-am) I discovered these [Di... | What distribution pluffy to buy for an aspiring econometrician? | CC BY-SA 3.0 | null | 2011-05-04T18:25:56.640 | 2013-05-06T21:09:32.367 | 2013-05-06T21:09:32.367 | 25315 | 4468 | [
"distributions"
] |
10323 | 2 | null | 10322 | 0 | null | From the list I would pick standard normal. After all regression is the main tool of econometrician and usually econometrician can rely only on asymptotic results, hence standard normal rules them all :)
Having said that I would not like to get a standard normal distribution pluffy (I am not a girl, but can be conside... | null | CC BY-SA 3.0 | null | 2011-05-04T18:39:13.447 | 2011-05-04T18:39:13.447 | null | null | 2116 | null |
10324 | 2 | null | 10322 | 6 | null | You're in big trouble if you're asking us for gift advice.
| null | CC BY-SA 3.0 | null | 2011-05-04T19:10:31.520 | 2011-05-04T19:10:31.520 | null | null | 2775 | null |
10325 | 1 | null | null | 7 | 1700 | In the book “Programming Collective Intelligence” Segaran explains the Fisher method for categorizing text as an alternative to Naive Bayes classifier. The Fisher method uses inverse-chi-square-distribution, which I do not really understand.
I watched this video found on stats.stackexchange about chi-square-distributio... | What does inverse-chi-square in Fisher method (classifying) exactly do? | CC BY-SA 3.0 | 0 | 2011-05-04T19:24:03.320 | 2021-02-06T17:05:42.443 | 2021-02-06T17:05:42.443 | 11887 | 4350 | [
"text-mining",
"chi-squared-distribution",
"combining-p-values",
"inverse-gamma-distribution"
] |
10326 | 2 | null | 10322 | 2 | null | Insurance is all about skewed distributions with long tails: think amount of loss. These also typically have only positive values. The log-normal distribution looks most like one of those. Another good option is the Gumbel distribution, which comes up in extreme value theory.
| null | CC BY-SA 3.0 | null | 2011-05-04T19:31:35.703 | 2011-05-04T19:31:35.703 | null | null | 279 | null |
10327 | 2 | null | 10322 | 13 | null | You gotta get her one with some Kurtosis. Maybe the t-distribution. And be sure and write a loving note along the lines of, "Baby, when I think of fat tails, I think of you. Your kurtosis makes you non-normal."
My wife digs it when I get sappy like that. I have the scars to prove it.
| null | CC BY-SA 3.0 | null | 2011-05-04T20:10:51.810 | 2011-05-04T20:10:51.810 | null | null | 29 | null |
10328 | 1 | 10336 | null | 7 | 8117 | I know that I, and others, sometimes get confused by the hypergeometric distribution (HD) as it pertains to overlapping lists. This is because the HD is usually described with the "balls in an urn" metaphor and not using "overlapping lists."
What is the proper way to calculate the p-value, according to the hypergeometr... | Using R's phyper to get the probability of list overlap | CC BY-SA 3.0 | null | 2011-05-04T15:13:26.520 | 2011-05-05T07:29:10.630 | 2011-05-05T07:29:10.630 | null | 3561 | [
"r"
] |
10329 | 1 | 10333 | null | 8 | 495 | What functionality should exist in a [CAS](http://en.wikipedia.org/wiki/Computer_algebra_system) that was specifically geared toward Statistics?
Symbolic algebra systems like Mathematica and Maple are often used for calculus, logic, and physics problems but are rarely used for statistics. Why is this?
What statistical ... | Symbolic computer algebra for statistics | CC BY-SA 3.0 | null | 2011-05-04T20:32:27.960 | 2019-01-19T23:04:45.170 | 2019-01-19T23:04:45.170 | 99274 | 3830 | [
"python",
"computational-statistics",
"mathematica",
"maple"
] |
10330 | 2 | null | 10322 | 2 | null | Aren't [econometricians](http://en.wikipedia.org/wiki/What%27s_that_got_to_do_with_the...?) concerned with the [price of t (distributions) in China](http://supertart.com/priceofteainchina/index.php)? It has the large (on occasion, infinite) kurtosis recommended by @JD Long, too.
| null | CC BY-SA 3.0 | null | 2011-05-04T20:52:28.667 | 2011-05-04T20:52:28.667 | null | null | 919 | null |
10331 | 1 | 10332 | null | 5 | 1210 | I'm working on some practice test problems, and one of them says to design a rejection sampling algorithm to produce draws from a unit exponential using draws from a Gamma(2,1).
I don't understand how this is possible, because I am under the impression that the "envelope function" g(x) needs to be scalable in such a ... | How to use rejection sampling to generate draws from Unit Exponential | CC BY-SA 3.0 | null | 2011-05-04T21:17:49.260 | 2011-05-05T21:09:49.177 | 2011-05-05T21:09:49.177 | 8 | 2984 | [
"self-study",
"monte-carlo",
"simulation"
] |
10332 | 2 | null | 10331 | 5 | null | Try a location shift on the Gamma(2,1)
EDIT:
[Illustration](http://www.wolframalpha.com/input/?i=3%2a%28x%2b1%29%20%2a%20exp%28-%28x%2b1%29%29%20vs%20exp%28-x%29%20from%200%20to%206)
| null | CC BY-SA 3.0 | null | 2011-05-04T23:17:36.520 | 2011-05-05T17:07:45.637 | 2011-05-05T17:07:45.637 | 3567 | 3567 | null |
10333 | 2 | null | 10329 | 9 | null | Support for matrix algebra. The vast majority of practiced statistics is multivariate and involves matrices, and often simplifying matrix forms requires special rules that aren't easily translated from a univariate case, so good matrix support would be really helpful.
| null | CC BY-SA 3.0 | null | 2011-05-04T23:29:40.427 | 2011-05-04T23:29:40.427 | null | null | 2839 | null |
10334 | 2 | null | 10295 | 1 | null | This is my theory. I think there are two kinds of questions: those that remain mostly within SE (which usually have fewer views), and those that are viewed by outsiders because it was linked from somewhere else (usually have more views).
For the questions that remain mostly within SE, votes are a good measure of inter... | null | CC BY-SA 3.0 | null | 2011-05-05T01:09:16.273 | 2011-05-05T01:09:16.273 | null | null | 2965 | null |
10335 | 2 | null | 10295 | 3 | null | One might define an interesting question as one that has received comparatively many votes given the number of views. To this end, you can create a baseline curve that reflects the expected number of votes given the views. Curves that attracted a lot more votes than the baseline were considered particularly interesting... | null | CC BY-SA 3.0 | null | 2011-05-05T01:16:53.857 | 2011-05-05T01:16:53.857 | null | null | 198 | null |
10336 | 2 | null | 10328 | 10 | null | Trying to translate this into a statistical question, it seems you have a population with $a$ members and you take two random samples without replacement sized $b$ and $c$, and you want the distribution of $X$, the number appearing in both samples.
As an illustration, suppose $a=5$, $b=2$ and $c=3$. There are 100 ways ... | null | CC BY-SA 3.0 | null | 2011-05-05T01:29:25.800 | 2011-05-05T01:29:25.800 | null | null | 2958 | null |
10337 | 1 | 22173 | null | 8 | 34425 | What would be $\operatorname{Var}(X^2)$, if $\operatorname{Var}(X)=\sigma^2$?
| $\operatorname{Var}(X^2)$, if $\operatorname{Var}(X)=\sigma^2$ | CC BY-SA 4.0 | null | 2011-05-05T03:42:06.087 | 2018-12-18T23:35:56.927 | 2018-12-18T23:35:56.927 | 5176 | 3903 | [
"mathematical-statistics",
"variance"
] |
10338 | 1 | null | null | 3 | 1980 | I want to perform bootstrapping for calculation of efficiency score from data envelopment analysis (DEA) using R.
- Are there any examples of data and results for this type of analysis in R to enable me to check my results?
- Are there any online or other resources that might assist my task?
| Bootstrapping data envelopment analysis efficiency score using R | CC BY-SA 3.0 | null | 2011-05-05T05:47:30.180 | 2012-10-09T09:41:04.573 | 2011-05-05T08:03:28.140 | 183 | 4472 | [
"r",
"bootstrap",
"efficiency"
] |
10339 | 2 | null | 10337 | 13 | null | As a simple example of the responses of @user2168 and @mpiktas:
The variance of the set of values 1,2,3 is 0.67, while the variance of its square is 10.89. On the other hand, the variance of 2,3,4 is also 0.67, but the variance of the squares is 24.22.
These are just variances for finite sets of data, but the idea exte... | null | CC BY-SA 3.0 | null | 2011-05-05T06:35:12.517 | 2011-05-05T06:35:12.517 | null | null | 4257 | null |
10340 | 2 | null | 10302 | 25 | null | You have looked at the [Wikipedia article on perplexity](http://en.wikipedia.org/wiki/Perplexity). It gives the perplexity of a discrete distribution as
$$2^{-\sum_x p(x)\log_2 p(x)}$$
which could also be written as
$$\exp\left({\sum_x p(x)\log_e \frac{1}{p(x)}}\right)$$
i.e. as a weighted geometric average of the... | null | CC BY-SA 3.0 | null | 2011-05-05T07:07:56.453 | 2011-05-05T07:07:56.453 | null | null | 2958 | null |
10342 | 1 | null | null | 4 | 3131 | Given a panel of countries over time, a fixed effects estimator makes sense to control for country-specific effects. My intuition tells me that if the dependent variable is correlated with lags of the independent variables, then bias will be introduced into the estimator. However, I'm having difficulty rigorously under... | Panel Data: In a fixed effects model, does auto-correlation introduce bias? | CC BY-SA 3.0 | null | 2011-05-05T08:06:46.827 | 2011-05-05T08:06:46.827 | null | null | 726 | [
"autocorrelation",
"panel-data",
"fixed-effects-model"
] |
10343 | 1 | 10351 | null | 8 | 2393 | I am totally confused: On the one hand you can read all kinds of explanations why you have to divide by n-1 to get an unbiased estimator for the (unknown) population variance (degrees of freedom, not defined for sample size 1 etc.) - see e.g. [here](http://en.wikipedia.org/wiki/Bessel%27s_correction) or [here](https://... | When estimating variance, why do unbiased estimators divide by n-1 yet maximum likelihood estimates divide by n? | CC BY-SA 4.0 | null | 2011-05-05T08:11:02.280 | 2019-03-02T22:56:03.470 | 2018-09-12T08:27:00.863 | 11887 | 230 | [
"normal-distribution",
"variance",
"unbiased-estimator"
] |
10344 | 2 | null | 10343 | 7 | null | The MLE is indeed found through division by n. However, MLE's are not guaranteed to be unbiased. So there is no contradiction in the fact that the unbiased estimator (divided by n-1) is used.
In practice, for reasonable sample sizes, it should not make a big difference anyway.
| null | CC BY-SA 3.0 | null | 2011-05-05T08:18:56.053 | 2011-05-05T08:18:56.053 | null | null | 4257 | null |
10345 | 2 | null | 423 | 54 | null | 
Found this one in the [comments on Andrew Gelman's blog](http://www.stat.columbia.edu/~cook/movabletype/archives/2011/04/worst_statistic.html#comment-2512168).
| null | CC BY-SA 3.0 | null | 2011-05-05T09:27:32.327 | 2011-05-05T09:27:32.327 | null | null | 442 | null |
10346 | 1 | 10348 | null | 4 | 102 | I am running a logistic regression with customer event data with multiple predictors.
However, one variable is extremely important, alone predicting 60% of the customers for the event. When this main predictor is included in the model, other predictors add very little to prediction over and above this main predictor.
... | Is it problematic if one predictor in a set accounts for almost all the prediction? | CC BY-SA 3.0 | null | 2011-05-05T09:34:46.440 | 2011-05-05T10:32:03.557 | 2011-05-05T10:31:15.537 | 183 | 1763 | [
"logistic",
"modeling"
] |
10347 | 1 | null | null | 6 | 8846 | I have made a heatmap based upon a regular data matrix in R, the package I use is `pheatmap`. Regular clustering of my samples is performed by the `distfun` function within the package.
Now I want to attach a precomputed distance matrix (generated by Unifrac) to my previously generated matrix/heatmap. Is this possible... | Making a heatmap with a precomputed distance matrix and data matrix in R | CC BY-SA 3.0 | null | 2011-05-05T09:39:26.173 | 2019-01-15T23:26:36.430 | 2011-05-05T11:18:43.750 | 930 | 4473 | [
"r",
"data-visualization"
] |
10348 | 2 | null | 10346 | 2 | null | I understand your gut feeling. But depending on the type of response and predictor, this has not to be unsual (example: response = "weight", predictors "height" and others with presumably less meaning like "state", "favorite movie" etc.).
However, you should check that for the creation of the predictor only information... | null | CC BY-SA 3.0 | null | 2011-05-05T10:32:03.557 | 2011-05-05T10:32:03.557 | null | null | 264 | null |
10349 | 2 | null | 10347 | 4 | null | Ok, so you can just look at the code by typing the name of the function at the R prompt, or use `edit(pheatmap)` to see it in your default editor.
Around line 14 and 23, you'll see that another function is called for computing the distance matrices (for rows and columns), given a distance function (R `dist`) and a meth... | null | CC BY-SA 3.0 | null | 2011-05-05T11:02:11.647 | 2011-05-05T11:02:11.647 | null | null | 930 | null |
10350 | 1 | 10352 | null | 6 | 673 | I am working on insurance data in which a customer has a field named `customer_no_dependent` (customer's number of dependent). Its coming out to be a significant variable( just that it has $p<0.0001$).
This variable has almost 20% missing values. For imputation, I thought to determine proxy indicators for number of de... | Advice on missing value imputation | CC BY-SA 3.0 | null | 2011-05-05T11:20:13.343 | 2012-12-19T20:10:02.963 | 2011-05-05T12:10:32.370 | 2116 | 1763 | [
"data-imputation"
] |
10351 | 2 | null | 10343 | 6 | null | The answer to your question is contained within your question.
When choosing an estimator for a parameter, you should ask yourself,
what property would you like your estimator to have:
- Robustness
- Unbiasedness
- Have the distributional properties of a MLE
- Consistency
- Asymptotically normal
- You know the p... | null | CC BY-SA 3.0 | null | 2011-05-05T12:15:11.133 | 2011-05-05T12:15:11.133 | null | null | 3805 | null |
10352 | 2 | null | 10350 | 5 | null | First of all: it is not clear from your explanation whether or not you have done multiple imputation. If not: please do so: single imputation could be worse than simple complete case analysis, and can both lead to severely biased results.
Next, if I understand correctly, your problem is that you don't know which variab... | null | CC BY-SA 3.0 | null | 2011-05-05T12:24:24.350 | 2011-05-05T12:24:24.350 | null | null | 4257 | null |
10353 | 1 | null | null | 8 | 2124 |
### Context:
My question concerns a typical design in my area – a researcher takes a group of subjects (say 10) and then applies three different conditions to them to measure the change in a response variable, e.g. vertical jump height performed after drinking a glucose drink, coloured plain water, and fruit juice (... | How to analyse repeated measure ANOVA with three or more conditions presented in randomised order? | CC BY-SA 3.0 | null | 2011-05-05T12:26:43.317 | 2014-12-06T05:40:54.650 | 2013-05-03T13:18:51.167 | 6029 | 4474 | [
"hypothesis-testing",
"anova",
"repeated-measures"
] |
10354 | 2 | null | 9653 | -1 | null | Another consequence of a small sample is the increase of type 2 error.
Nunnally demonstrated in the paper "The place of statistics in psychology", 1960, that small samples generally fail to reject a point null hypothesis. These hypothesis are hypothesis having some parameters equals zero, and are known to be false in t... | null | CC BY-SA 3.0 | null | 2011-05-05T12:28:58.153 | 2011-05-05T12:28:58.153 | null | null | 4443 | null |
10356 | 1 | null | null | 10 | 7830 | I am building a propensity model using logistic regression for a utility client.
My concern is that out of the total sample my 'bad' accounts are just 5%, and the rest are all good.
I am predicting 'bad'.
- Will the result be biassed?
- What is optimal 'bad to good proportion' to build a good model?
| Is a logistic regression biased when the outcome variable is split 5% - 95%? | CC BY-SA 3.0 | null | 2011-05-05T14:03:29.927 | 2017-08-30T17:11:38.003 | 2011-05-06T14:07:42.000 | 495 | 4478 | [
"logistic",
"modeling"
] |
10357 | 2 | null | 10356 | 1 | null | In theory, you will be able to discriminate better if the proportions of "good" and "bad" are roughly similar in size. You might be able to move towards this by stratified sampling, oversampling bad cases and then reweighting to return to the true proportions later.
This carries some risks. In particular your model... | null | CC BY-SA 3.0 | null | 2011-05-05T14:42:39.460 | 2011-05-05T14:42:39.460 | null | null | 2958 | null |
10358 | 2 | null | 10271 | 2 | null | You might find [this paper](http://www.stat.duke.edu/~mw/Smith+West1983.pdf) of interest. See also more detailed presentation of similar models in [West & Harrison](http://rads.stackoverflow.com/amzn/click/0387947256). There are other examples of this sort of monitoring as well, many which are more recent, but this isn... | null | CC BY-SA 3.0 | null | 2011-05-05T14:44:07.957 | 2011-05-05T14:49:44.440 | 2011-05-05T14:49:44.440 | 26 | 26 | null |
10359 | 1 | 10555 | null | 6 | 1190 | How can I generate dependent time series from a given marginal distribution? I want to be able to adjust the level of dependence, to influence the predictability of the series, which will be given as input to a Monte Carlo simulation. The dependence parameter can be the correlation, the mutual information, or something... | Generating dependent time series from a given distribution? | CC BY-SA 3.0 | null | 2011-05-05T14:48:53.297 | 2011-05-09T15:45:40.490 | 2011-05-06T14:42:03.297 | 4479 | 4479 | [
"time-series",
"monte-carlo",
"simulation",
"non-independent",
"prediction-interval"
] |
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