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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
9367 | 2 | null | 9331 | 1 | null | I learnt about the use of splines in regression from the book "Regression Modeling Strategies" by Frank Harrell. Harrell's R package rms allows you to easily fit regression models in which some predictor variables are represented as splines.
| null | CC BY-SA 3.0 | null | 2011-04-08T20:21:18.013 | 2011-04-08T20:21:18.013 | null | null | 3835 | null |
9368 | 2 | null | 9312 | 1 | null | You should use the point estimate of the reliability, not the lower bound or whatsoever. I guess by lb/up you mean the 95% CI for the ICC (I don't have SPSS, so I cannot check myself)? It's unfortunate that we also talk of Cronbach's alpha as a "lower bound for reliability" since this might have confused you.
It should... | null | CC BY-SA 3.0 | null | 2011-04-08T20:40:25.080 | 2011-04-08T20:40:25.080 | null | null | 930 | null |
9369 | 2 | null | 9365 | 12 | null | It's a bit difficult for me to see what paper might be of interest to you, so let me try and suggest the following ones, from the psychometric literature:
>
Borsboom, D. (2006). The attack of
the psychometricians.
Psychometrika, 71, 425-440.
for dressing the scene (Why do we need to use statistical models that b... | null | CC BY-SA 3.0 | null | 2011-04-08T21:02:38.377 | 2011-04-08T21:02:38.377 | null | null | 930 | null |
9370 | 2 | null | 9365 | 12 | null | Here are five highly-cited papers from the last 40 years of the [Journal of the Royal Statistical Society, Series C: Applied Statistics](http://www.blackwellpublishing.com/journal.asp?ref=0035-9254) with a clear application in the title that caught my eye while scanning through the Web of Knowledge search results:
- S... | null | CC BY-SA 3.0 | null | 2011-04-08T21:33:23.330 | 2011-04-13T06:57:43.827 | 2011-04-13T06:57:43.827 | 183 | 449 | null |
9371 | 2 | null | 9329 | 3 | null | First idea is just to mimic the knock-out strategy from variable importance and just test how mixing each attribute will degenerate the forest confidence in object classification (on OOB and with some repetitions obviously). This requires some coding, but is certainly achievable.
However, I feel it is just a bad idea ... | null | CC BY-SA 3.0 | null | 2011-04-08T23:21:36.193 | 2011-04-08T23:21:36.193 | null | null | null | null |
9372 | 1 | null | null | 2 | 374 | How would I go about finding out the confidence intervals around a set of distinct bianary occurrences where each occurence has a different associated probability and each occurence is weighted?
To be more specific, we typically determine a milestone budget by assigning reasonable probabilities to each milestone. Each... | How do I determine confidence intervals around weighted, probilized events? | CC BY-SA 3.0 | null | 2011-04-08T23:28:23.310 | 2011-04-09T00:41:18.617 | 2011-04-09T00:27:18.753 | null | null | [
"probability"
] |
9373 | 2 | null | 9276 | 4 | null | There is one general and "in-universe" criterion for goodness of Monte Carlo -- convergence.
Stick to one M and check how the PG behaves with the number of juries -- it should converge, so will show you a number of repetitions for which you will have a reasonable (for your application) number of significant digits. Re... | null | CC BY-SA 3.0 | null | 2011-04-09T00:04:02.477 | 2011-04-09T00:04:02.477 | null | null | null | null |
9374 | 2 | null | 9372 | 2 | null | Basically you would need to make a [probability tree](http://www.google.com/search?q=probability+tree) with resulting penalty sums in leafs and sum the whole thing up for a criterion of your choice, possibly using some software for more milestones than few (there will be $2^N$ leafs for $N$ milestones). You will be abl... | null | CC BY-SA 3.0 | null | 2011-04-09T00:26:56.037 | 2011-04-09T00:26:56.037 | null | null | null | null |
9375 | 2 | null | 9372 | 1 | null | It rather depends on how many milestones you have. But if this (call it $n$) is small enough and each one either happens or does not, then you can work out the $2^n$ possibilities, working out the probabilities by multiplying the inidividual probabilies.
So for example the probability of paying out $20,000,000$ is $... | null | CC BY-SA 3.0 | null | 2011-04-09T00:41:18.617 | 2011-04-09T00:41:18.617 | null | null | 2958 | null |
9376 | 2 | null | 8669 | 0 | null | Canonical Correlation Analysis was one way to go and it works! Credits to @schenectady for this. Thanks a lot for your help.
I want to write this for future reference and for others who might have a similar query, if you want to perform a regression analysis in such a situation, you should attempt to minimize the squar... | null | CC BY-SA 3.0 | null | 2011-04-09T07:49:30.520 | 2011-04-09T18:43:50.403 | 2011-04-09T18:43:50.403 | 930 | 3859 | null |
9377 | 1 | 9380 | null | 7 | 841 | In the paper
>
M. Avellaneda and J. H. Lee, Statistical arbitrage in the U.S. equities market, July 2008,
in the Appendix on page 46, how does he get equilibrium standard deviation as following:
$$\sigma_{eq} = \sqrt{\frac{\text{Variance}(\zeta)}{1 − b^2}}$$
If anyone knows the paper, please explain.
Much appreciat... | Origin of strange formula for equilibrium standard deviation | CC BY-SA 3.0 | null | 2011-04-09T11:29:27.753 | 2011-04-19T13:04:46.690 | 2011-04-19T13:04:46.690 | 2970 | 862 | [
"regression",
"probability",
"variance",
"stochastic-processes"
] |
9378 | 1 | 9401 | null | 4 | 4826 | I'm working with a CSV which contains approximately 220,000 entries. My aim is to predict one of the attributes (ATT1) using the other 3 (ATT2, ATT3, ATT4).
I've been able to do this using NaiveBayes, but now I feel unsatisfied with the result. The reason is that ATT1 can be one of 6 values (VAL1-6), but these are not ... | How to choose a data subset in RapidMiner? | CC BY-SA 3.0 | null | 2011-04-09T12:49:36.187 | 2017-05-19T12:31:50.697 | 2017-05-19T12:31:50.697 | 101426 | 1522 | [
"dataset",
"rapidminer"
] |
9380 | 2 | null | 9377 | 15 | null | The authors are providing a simple means for estimating the parameters of a mean-reverting Orstein-Uhlenbeck process via a regression on returns at discretized points in time.
The model they are considering has a representation as a stochastic differential equation of the form [pg. 16, Eq. (12)]
$$
\newcommand{\rd}{\m... | null | CC BY-SA 3.0 | null | 2011-04-09T13:32:30.010 | 2011-04-11T02:17:49.223 | 2011-04-11T02:17:49.223 | 2970 | 2970 | null |
9381 | 1 | null | null | 4 | 149 | I was wondering from the view of dividing the topics of statistical theory into inference part and non-inference part, what inference topics and non-inference topics statistical theory is covering?
By inference, I mean the task in logic to reach some conclusion from some premises. Probabilistic inference is a way of lo... | Inference and noninference parts of statistical theory | CC BY-SA 3.0 | null | 2011-04-09T14:00:11.930 | 2017-03-15T19:34:48.247 | 2017-04-13T12:44:33.550 | -1 | 1005 | [
"inference"
] |
9383 | 1 | null | null | 2 | 666 | I'm doing 10-fold cross validation on a dataset. But in some folds there are edge cases that the denominator in precision-recall calculation is zero (tp + fp =0).
What are the correct values for precision and recall in this case? And what is the correct way of doing cross-validation (should I include these results when... | What are the correct edge case values of precision and recall and how to integrate them into cross validation? | CC BY-SA 3.0 | null | 2011-04-09T15:24:45.393 | 2011-04-09T20:05:43.907 | 2017-04-13T12:44:39.283 | -1 | 4091 | [
"cross-validation",
"precision-recall"
] |
9384 | 2 | null | 9342 | 11 | null | The Ward clustering algorithm is a hierarchical clustering method that minimizes an 'inertia' criteria at each step. This inertia quantifies the sum of squared residuals between the reduced signal and the initial signal: it is a measure of the variance of the error in an l2 (Euclidean) sens. Actually, you even mention ... | null | CC BY-SA 3.0 | null | 2011-04-09T15:57:28.113 | 2011-04-09T15:57:28.113 | null | null | 1265 | null |
9385 | 1 | null | null | 7 | 11210 | I am using the Holt-Winters' exponential smoothing technique to forecast expenditure data 2 years into the furture. The monthly data has an increasing trend and annual seasonality.
I'm using MS Excel with the Solver add-in to calculate the optimal values of $\alpha$, $\beta$ and $\gamma$ to give the smallest MSE for th... | Forecasting beyond one season using Holt-Winters' exponential smoothing | CC BY-SA 3.0 | null | 2011-04-09T15:59:58.363 | 2013-01-16T11:28:44.610 | 2013-01-16T11:28:44.610 | 1352 | 4092 | [
"time-series",
"forecasting",
"excel",
"exponential-smoothing"
] |
9387 | 2 | null | 9365 | 9 | null | On a wider level I would recommend the ["Statistical Modeling: The Two Cultures"][1] paper by Leo Breiman in 2001 (cited 515) I know it was covered by the journal club recently and I found it to be really interesting. I've c&p'd the abstract.
>
Abstract. There are two cultures in
the use of statistical modeling to
... | null | CC BY-SA 4.0 | null | 2011-04-09T16:49:38.487 | 2019-03-02T12:41:01.320 | 2019-03-02T12:41:01.320 | 166514 | 3597 | null |
9388 | 2 | null | 3898 | 1 | null | If the problem at issue consists of testing for the optimal number of factors, Jushan Bai and Serena Ng in several articles provide a test based on AIC/BIC that minimizes, for different options, the variance of the error. They supply to my knowledge the most updated approach to resolve this issue. See also Alexei Onats... | null | CC BY-SA 3.0 | null | 2011-04-09T18:09:15.317 | 2011-04-09T18:09:15.317 | null | null | 4093 | null |
9390 | 1 | null | null | 9 | 2126 | An English soccer team plays a series of matches against different opponents of varying ability. A bookmaker offers odds for each match as to whether it will be a home win, away win, or draw. Part-way through the season, the team has played $n$ matches and has drawn $k$ of them, which is more than might be expected fro... | What's the probability that a bookmaker is mispricing odds on soccer games? | CC BY-SA 4.0 | null | 2011-04-09T18:24:23.090 | 2018-06-04T01:09:24.283 | 2018-06-04T01:09:24.283 | 116107 | null | [
"probability",
"games",
"gambling"
] |
9391 | 2 | null | 9390 | 0 | null | Bookmakers use an overround so they don't actually care what the result is because they win whatever. That is why you never meet a poor bookie. If a bookmaker is mispricing draws your ability to make profit would depend on the odds the bookmaker was offering and whether the profits generated would cover the times you l... | null | CC BY-SA 3.0 | null | 2011-04-09T18:49:59.820 | 2011-04-09T18:49:59.820 | null | null | 3597 | null |
9392 | 1 | null | null | 0 | 237 | My data looks like this (F=Features)
```
F1 F2 F3 F4 F5 F6 F7 F8....
ID1 0.67 0.76 0.3 0.54 0.21 0.88 0.97 0.45....
ID2 0.76 0.68 0.10 0.45 0.12 0.44 0.79 0.54....
ID3 0.67 0.76 0.3 0.54 0.21 0.88 0.68 0.76....
ID4 0.67 0.... | How to get scored combination of features | CC BY-SA 3.0 | null | 2011-04-09T18:54:22.423 | 2011-04-13T02:59:16.640 | 2011-04-11T20:52:30.830 | 3111 | 3111 | [
"feature-selection",
"text-mining"
] |
9393 | 2 | null | 9383 | 1 | null | Instead of using CV to estimate precision and recall, use it to obtain the expected TP, TN, FP, and FN rate. Then use those values to compute the expected precision and recall and the standard errors. (Taylor expansions come in handy for the latter.)
| null | CC BY-SA 3.0 | null | 2011-04-09T20:05:43.907 | 2011-04-09T20:05:43.907 | null | null | 3567 | null |
9394 | 2 | null | 9365 | 8 | null | From a genetic epidemiology perspective, I would now recommend the following series of papers about [genome-wide association studies](http://en.wikipedia.org/wiki/Genome-wide_association_study):
- Cordell, H.J. and Clayton, D.G. (2005). Genetic association studies. Lancet 366, 1121-1131.
- Cantor, R.M., Lange, K., an... | null | CC BY-SA 3.0 | null | 2011-04-09T20:39:39.403 | 2011-04-09T20:39:39.403 | null | null | 930 | null |
9395 | 1 | null | null | 8 | 429 | I don't know what is the appropriate term for my question. The scenario is described as following.
In the analysis there one dependent variable Y and two independent variable X1 and X2.
All three variables are continuous.
I converted X1 into a categorical variable which has three levels A, B, and C. It was found that Y... | How can I demonstrate non-linearity without categorising a predictor? | CC BY-SA 4.0 | null | 2011-04-09T23:20:10.947 | 2020-02-17T00:16:09.207 | 2020-02-17T00:16:09.207 | 11887 | 400 | [
"regression",
"categorical-data",
"data-visualization",
"nonlinear-regression",
"continuous-data"
] |
9396 | 1 | null | null | 6 | 2895 | In the paper
>
M. Avellaneda and J. H. Lee, Statistical arbitrage in the U.S. equities market, July 2008,
in the Appendix on page 44, I have some questions.
First he runs the regression of stock-return ($R_n^S$) with index/ETF-return ($R_n^I$).
$R_n^S = \beta_0 + \beta R_n^I + \epsilon_n, ~~~~~n=1,2,...,60$
Then h... | Why are cumulative residuals from regression on stock and index returns mean reverting | CC BY-SA 3.0 | null | 2011-04-09T23:24:44.063 | 2011-07-09T18:29:14.500 | null | null | 862 | [
"regression",
"mean",
"stochastic-processes"
] |
9397 | 2 | null | 9395 | 5 | null | Converting a continuous variable into categorical may be a bad idea, but may be a good idea as well, this depends on the problem. When the relationships of the variable can be best described using thresholds, categorisation may be one of the best options.
You wrote that in different categories of X1 the correlation bet... | null | CC BY-SA 3.0 | null | 2011-04-09T23:49:17.103 | 2011-04-09T23:49:17.103 | null | null | 3911 | null |
9398 | 1 | 9406 | null | 15 | 3042 | Suppose you get to observe "matches" between buyers and sellers in a market. You also get to observe characteristics of both buyers and sellers which you would like to use to predict future matches & make recommendations to both sides of the market.
For simplicity, assume there are N buyers and N sellers and that e... | Supervised learning with "rare" events, when rarity is due to the large number of counter-factual events | CC BY-SA 3.0 | null | 2011-04-09T23:31:25.733 | 2011-04-10T17:54:19.620 | 2011-04-10T17:54:19.620 | 919 | 4095 | [
"machine-learning"
] |
9399 | 2 | null | 9398 | 1 | null | Concerning (1). You need to keep positive and negative observations if you want meaningful results.
(2) There is no wiser method of subsampling than uniform distribution if you don't have any a priori on your data.
| null | CC BY-SA 3.0 | null | 2011-04-09T23:47:33.450 | 2011-04-10T00:28:15.327 | null | null | 3896 | null |
9400 | 1 | 9403 | null | 6 | 387 | My actual project is a bit complicated, but I'll explain by analogy (which I hope facilitates response):
I have 3 substances, say water, motor oil, and ethanol. For each substance, I have 5 samples in a beaker (total 15 beakers). I heat all the beakers on a hot-plate up to 70 degrees Celsius, and over the next hour, I ... | How can I compute regression for several longitudinal data sets (thus, with auto-correlated error)? | CC BY-SA 3.0 | null | 2011-04-10T00:58:48.477 | 2011-04-10T17:25:16.590 | 2011-04-10T03:07:18.390 | 3911 | 4096 | [
"regression",
"autocorrelation",
"nonlinear-regression",
"panel-data",
"exponential-distribution"
] |
9401 | 2 | null | 9378 | 4 | null | Use the Sample operator with the Balance checkbox. You can set the sample size per class that way (to a balanced one)
@steffen, the mandate for this site covers stats AND stats software. There are tons of R questions on here, so it's fair to ask questions about other software too.
| null | CC BY-SA 3.0 | null | 2011-04-10T01:21:50.127 | 2011-04-10T01:21:50.127 | null | null | 74 | null |
9402 | 2 | null | 9342 | 2 | null | Another way of thinking about this, which might lend itself to an adaptation for $\ell_1$ is that choice of the mean comes from the fact that the mean is the point that minimizes the sum of squared Euclidean distances. If you're using $\ell_1$ to measure the distance between time series, then you should be using a cent... | null | CC BY-SA 3.0 | null | 2011-04-10T02:10:19.477 | 2011-04-10T02:10:19.477 | null | null | 139 | null |
9403 | 2 | null | 9400 | 3 | null | As we have strong reasons to believe that the cooling will follow the $y(t) = a + e^{-kt}$ function for each beaker I would first check if this model fits the data well indeed.
If it does I wouldn't bother with analysing the autocorrelation at all, but focus on the estimation of $k_1$, $k_2$ and $k_3$, and testing the ... | null | CC BY-SA 3.0 | null | 2011-04-10T02:37:10.260 | 2011-04-10T13:02:01.177 | 2011-04-10T13:02:01.177 | 3911 | 3911 | null |
9404 | 2 | null | 9390 | 9 | null | The answer to your question depends intricately on what information and assumptions you are going to use. This is because the result of a game is an extraordinarily complicated process. It can become arbitrarily complicated depending on what information you have about:
- Players in the particular team - perhaps even... | null | CC BY-SA 3.0 | null | 2011-04-10T03:24:53.640 | 2011-04-14T03:21:44.263 | 2011-04-14T03:21:44.263 | 2392 | 2392 | null |
9405 | 1 | null | null | 0 | 2305 | I have two random poisson variables $x_1$ and $x_2$ with value 10 and 25 respectively. I am interested to use likelihood ratio test to test the null hypothesis: $\lambda_1=\lambda_2$, versus alernate hypthesis $\lambda_1$ not equal to $\lambda_2$.
I want to use simulation to calculate power and alpha values. I would wa... | Simulation of maximum likelihood ratio test to test two poisson random variables | CC BY-SA 3.0 | null | 2011-04-10T05:15:44.633 | 2011-04-11T17:48:03.737 | 2011-04-10T12:18:59.777 | 3911 | 4098 | [
"r",
"maximum-likelihood",
"poisson-distribution",
"statistical-power",
"likelihood-ratio"
] |
9406 | 2 | null | 9398 | 13 | null | If I understand correctly, you have a two class classification problem, where the positive class (matches) is rare. Many classifiers struggle with such a class imbalance, and it is common practice to sub-sample the majority class in order to obtain better performance, so the answer to the first question is "yes". How... | null | CC BY-SA 3.0 | null | 2011-04-10T08:29:11.100 | 2011-04-10T08:29:11.100 | null | null | 887 | null |
9407 | 1 | 9408 | null | 4 | 13484 | I'm trying to compute the minimum sample size for a psychometric test based on 7 point Likert scales. I'd like to run ANOVA on each scale to look for differences between groups.
Most online survey sample size calculators seem to be designed for polls, e.g. Yes/No, Agree/Disagree. They take as input population size, a c... | Statistical power and minimum sample size for ANOVA with likert scale as dependent variable | CC BY-SA 3.0 | null | 2011-04-10T09:58:24.353 | 2011-04-12T06:59:48.757 | 2011-04-11T05:55:35.603 | 183 | 4099 | [
"anova",
"likert",
"statistical-power",
"finite-population"
] |
9408 | 2 | null | 9407 | 5 | null | The commonly used statistical methods assume that you take a sample of an infinite or very large population. ANOVA, too, has this assumption. When the subjects of your survey can be viewed as a representative sample of an existing or hypothetical much larger population, you do not need the finite population methods.
Th... | null | CC BY-SA 3.0 | null | 2011-04-10T11:29:49.783 | 2011-04-10T11:29:49.783 | null | null | 3911 | null |
9409 | 2 | null | 9405 | 2 | null | This is a particularly ill-formed question.
If by "alpha" you mean Type I error, you need to go back to Square One and get definitions straight. Type I error is not something inherent in the data, or even in the hypothesis; it's a subjectively and externally applied measure of risk. And without the Type I error, you ... | null | CC BY-SA 3.0 | null | 2011-04-10T12:07:16.413 | 2011-04-10T12:07:16.413 | null | null | 5792 | null |
9410 | 2 | null | 9405 | 2 | null | For the simulation let's first choose sample sizes N1 and N2 for the two Poisson samples:
```
require(lmtest)
N1 = 20; N2 = 15
```
Generate a random sample and run a likelihood ratio test:
```
# CODE BLOCK "A"
x = c(rpois(N1, 10), rpois(N2, 25))
group = factor(c(rep('a', N1), rep('b', N2)))
m1 = glm(x ~ 1, family=pois... | null | CC BY-SA 3.0 | null | 2011-04-10T12:09:13.697 | 2011-04-11T17:48:03.737 | 2011-04-11T17:48:03.737 | 3911 | 3911 | null |
9411 | 2 | null | 9400 | 4 | null | If I understand your question correctly, you should be able to achieve what you want to do using a non-linear mixed-effects model. If you use R, you can use the `nlme` package. Basically as fixed factors you have a covariate (a) and a factor (substance or $i$ in $k_{i}$). You also have a random effect (individual measu... | null | CC BY-SA 3.0 | null | 2011-04-10T13:09:08.573 | 2011-04-10T17:25:16.590 | 2011-04-10T17:25:16.590 | 2020 | 2020 | null |
9412 | 2 | null | 9330 | 1 | null | I believe that this is an experiment where it is safe to assume a monotone relationship: for a longer exposition time the infection probability can not be smaller. So you can run monotone/isotonic regression.
You can even incorporate into your model that the infection probability at time=0 is 0.
| null | CC BY-SA 3.0 | null | 2011-04-10T13:13:06.367 | 2011-04-13T02:40:18.117 | 2011-04-13T02:40:18.117 | 3911 | 3911 | null |
9413 | 2 | null | 2715 | 11 | null | Think hard about the underlying data generating process (DGP). If the model you want to use doesn't reflect the DGP, you need to find a new model.
| null | CC BY-SA 4.0 | null | 2011-04-10T14:26:46.080 | 2018-06-29T02:38:26.897 | 2018-06-29T02:38:26.897 | 164061 | 3265 | null |
9414 | 2 | null | 2 | 3 | null | Other answers have covered what is normality and suggested normality test methods. Christian highlighted that in practice perfect normality barely exists.
I highlight that observed deviation from normality does not necessarily mean that methods assuming normality may not be used, and normality test may not be very usef... | null | CC BY-SA 4.0 | null | 2011-04-10T14:30:50.133 | 2022-11-23T13:01:37.863 | 2022-11-23T13:01:37.863 | 362671 | 3911 | null |
9415 | 1 | 9418 | null | 14 | 7267 | Covariance between two random variables defines a measure of how closely are they linearly related to each other. But what if the joint distribution is circlular? Surely there is structure in the distribution. How is this structure extracted?
| Measuring non-linear dependence | CC BY-SA 3.0 | null | 2011-04-10T14:46:33.510 | 2012-12-05T10:30:41.000 | null | null | 4101 | [
"covariance-matrix"
] |
9416 | 1 | null | null | 1 | 253 | I have done an experiment to find the effective n/w bandwidth.
The data I got in kbps is
223, 221, 510, 220, 471, 229, 222, 221, 220, 221
How can I find the effective bandwidth? Averaging gives 275.8. But if I have done only first 4 rounds then the average is 293.5. How can I find out a more reasonable value as the e... | How to find the effective bandwidth correctly using statistics? | CC BY-SA 3.0 | null | 2011-04-10T14:55:57.757 | 2011-04-11T07:51:46.153 | 2011-04-11T07:38:25.130 | 183 | 4102 | [
"estimation"
] |
9417 | 2 | null | 9415 | 5 | null | [Mutual information](http://en.wikipedia.org/wiki/Mutual_information) has properties somewhat analogous to covariance. Covariance is a number which is 0 for independent variables and nonzero for variables which are linearly dependent. In particular, if two variables are the same, then the covariance is equal to varianc... | null | CC BY-SA 3.0 | null | 2011-04-10T16:30:36.543 | 2011-04-11T18:37:15.167 | 2011-04-11T18:37:15.167 | 3369 | 3369 | null |
9418 | 2 | null | 9415 | 10 | null | By "circular" I understand that the distribution is concentrated on a circular region, as in this contour plot of a pdf.
If such a structure exists, even partially, a natural way to identify and measure it is to average the distribution cir... | null | CC BY-SA 3.0 | null | 2011-04-10T16:51:07.647 | 2011-04-11T14:20:58.970 | 2011-04-11T14:20:58.970 | 919 | 919 | null |
9420 | 1 | null | null | 2 | 637 | Contingency tables are typically formatted as tables similar to matrices in mathematics, see [this example](http://en.wikipedia.org/wiki/Contingency_table#Example).
Is the equation below an accepted notation of expressing the probabilities of the outcomes as a matrix? If not, what would be the accepted way? Are there a... | Notation of probability matrix corresponding to a contingency table | CC BY-SA 4.0 | null | 2011-04-10T17:20:13.737 | 2022-07-11T16:46:21.610 | 2022-07-11T16:46:21.610 | 282433 | 3911 | [
"contingency-tables",
"matrix",
"notation"
] |
9421 | 2 | null | 9420 | 1 | null | It looks reasonable to me. Though realize that with an N larger than say 1,000, your table of probabilities won't represent exact counts because you'll end up truncating to three or so decimal places.
| null | CC BY-SA 3.0 | null | 2011-04-10T19:42:39.777 | 2011-04-10T19:42:39.777 | null | null | 1499 | null |
9422 | 1 | 9443 | null | 6 | 203 | Suppose I have survey responses that look like this:
```
N=60000, Population
n=1000, Total sample
n=800, Users of Company X
n=200, Randomly chosen from 800 and asked about their Future Use of Company X
n=100, Planning to use Company X less in the future
```
The reason that only 200 of 800 users were asked about futu... | Resampling within a survey to account for missing data | CC BY-SA 3.0 | null | 2011-04-11T01:00:28.697 | 2011-04-13T04:57:11.190 | 2011-04-13T04:57:11.190 | 776 | 776 | [
"sampling",
"inference",
"resampling"
] |
9423 | 2 | null | 7019 | 3 | null | If you really only have one data point greater than 1000, it would be easiest just to delete that point from the graph. You can make a note in the caption or as a text box that there is an outlier.
| null | CC BY-SA 3.0 | null | 2011-04-11T01:13:15.487 | 2011-04-11T01:13:15.487 | null | null | 1569 | null |
9424 | 2 | null | 9416 | 2 | null | From what I can understand, I think you have the following options:
- Sample more! n = 10 is hardly enough for drawing conclusions
- If you don't/can't do "enough" sampling, you can always try to do some Monte Carlo type study with bootstrapping
| null | CC BY-SA 3.0 | null | 2011-04-11T07:51:46.153 | 2011-04-11T07:51:46.153 | null | null | 3014 | null |
9425 | 1 | null | null | 25 | 112345 | Reading Field's Discovering Statistics Using SPSS (3rd Edition) I was struck by a bit about post-hoc tests in ANOVA. For those wanting to control the Type I error rate he suggests Bonferroni or Tukey and says (p. 374):
>
Bonferroni has more power when the
number of comparisons is small,
whereas Tukey is more power... | Bonferroni or Tukey? When does the number of comparisons become large? | CC BY-SA 3.0 | null | 2011-04-11T12:08:13.933 | 2015-12-04T16:04:40.800 | 2015-12-04T16:04:40.800 | 28666 | 3597 | [
"anova",
"multiple-comparisons",
"post-hoc",
"bonferroni",
"tukey-hsd-test"
] |
9426 | 2 | null | 9420 | 2 | null | I'm not sure I can justify what I'm about to say, but I would be uneasy about expressing probabilities in a form like this. The structure is too reminiscent of other things that don't properly apply and suggests you should be able to do stuff like matrix multiplication that wouldn't mean anything here.
If the goal is t... | null | CC BY-SA 3.0 | null | 2011-04-11T12:23:03.697 | 2011-04-11T12:23:03.697 | null | null | 174 | null |
9427 | 1 | 9428 | null | 5 | 6929 | >
Possible Duplicate:
Probability distribution value exceeding 1 is OK?
I'm a bit confused how I am getting probabilities greater than 1 when calculating p(x | mu, sigma) when x = mu. For example, if I run:
```
>> gaussProb(0, 0, 0.1)
ans =
1.2616
```
where gaussProb is a matlab function from the PMTK toolbox... | Interpreting Gaussian probabilities greater than 1 | CC BY-SA 3.0 | null | 2011-04-11T12:58:01.893 | 2011-04-11T13:01:11.077 | 2017-04-13T12:44:36.927 | -1 | 4108 | [
"normal-distribution",
"matlab"
] |
9428 | 2 | null | 9427 | 11 | null | The code in the question returns the values of [probability density function](http://en.wikipedia.org/wiki/Probability_density_function). The values of probability density function can be greater than one. The actual probability $P(X<x)$ for random variable $X$ with probability density function $p(x)$ is integral $\int... | null | CC BY-SA 3.0 | null | 2011-04-11T13:01:11.077 | 2011-04-11T13:01:11.077 | null | null | 2116 | null |
9429 | 1 | 10415 | null | 6 | 3431 | I am trying to fit a multilevel longitudinal model and i have a question regarding how to specify it.
The data consist of about 8k observations collected from about 3k individuals at four time points. Individuals are nested in groups and there are about 200 groups.
I have two different types of fixed effects: (a) repea... | Correct specification of longitudinal model in lme4 | CC BY-SA 3.0 | null | 2011-04-11T13:42:47.047 | 2011-05-06T15:27:14.023 | 2020-06-11T14:32:37.003 | -1 | 1871 | [
"r",
"multilevel-analysis",
"panel-data"
] |
9430 | 2 | null | 9385 | 6 | null | I am not very familiar with Holt-Winters, however I have this [excellent book](http://www.amazon.co.uk/Forecasting-Exponential-Smoothing-Approach-Statistics/dp/3540719164/ref=sr_1_1?ie=UTF8&s=books&qid=1302529451&sr=8-1) by @Rob Hyndman. The package forecast (which is based on the book) of statistical package R gives t... | null | CC BY-SA 3.0 | null | 2011-04-11T13:45:02.443 | 2011-04-11T13:51:21.390 | 2011-04-11T13:51:21.390 | 2116 | 2116 | null |
9431 | 1 | 9528 | null | 9 | 1662 | I'm running a binary logit regression where I know the dependent variable is miscoded in a small percentage of cases. So I'm trying to estimate $\beta$ in this model:
$prob(y_i) = 1/(1 + e^{-z_i})$
$z_i = \alpha + X_i\beta$
But instead of the vector $Y$, I have $\tilde{Y}$, which includes some random errors (i.e. $y_i... | How can I correct for measurement error in the dependent variable in a logit regression? | CC BY-SA 3.0 | null | 2011-04-11T14:03:13.367 | 2011-04-13T18:13:24.987 | 2011-04-11T14:38:25.397 | 3911 | 4110 | [
"logistic",
"measurement-error"
] |
9432 | 2 | null | 1610 | 0 | null | This is how I remember the difference between Type I and Type II errors
Type I is a false POSITIVE
Type II is a false NEGATIVE
Type I is so POSITIVE it jumps out of bed first, runs downstairs and finds a significant breakfast while Type II is so NEGATIVE it stays in bed all day so when it eventually crawls out all the ... | null | CC BY-SA 3.0 | null | 2011-04-11T14:31:06.300 | 2011-04-11T14:31:06.300 | null | null | 3597 | null |
9434 | 2 | null | 9431 | 2 | null | This situation is often referred to as misclassification error. [This paper](http://www.ncbi.nlm.nih.gov/pubmed/20552681) my help you correctly estimating $\beta$. EDIT: I found relevant-looking papers using [http://www.google.com/search?q=misclassification+of+dependent+variable+logistic](http://www.google.com/search?q... | null | CC BY-SA 3.0 | null | 2011-04-11T14:41:17.657 | 2011-04-11T15:34:28.983 | 2011-04-11T15:34:28.983 | 3911 | 3911 | null |
9435 | 1 | null | null | 4 | 695 | I'm trying to create something similar to this.
So, 3 different Node classes, and a whole bunch of relationships between them. In my case, there should be roughly half of the number of nodes present at most.
What I'm looking for is recommendations as ... | Displaying relationships between nodes | CC BY-SA 4.0 | null | 2011-04-11T14:46:09.620 | 2019-03-02T00:40:43.843 | 2019-03-02T00:40:43.843 | 11887 | 4112 | [
"data-visualization"
] |
9436 | 2 | null | 9429 | 3 | null | You have a large number of groups, so I speculate that (depending on the setting) you may think about group as a random effect, so your `(…|grp)` terms are probably justified. It may also be reasonable to associate random effects with individuals (`(…|id)` terms). However you have `time` as a covariate in all your mode... | null | CC BY-SA 3.0 | null | 2011-04-11T15:09:01.237 | 2011-04-11T17:57:45.803 | 2011-04-11T17:57:45.803 | 3911 | 3911 | null |
9437 | 1 | null | null | 2 | 224 | I am quite a newbie in this area:
- What are the boosting methods for regression systems? I know about Gradient boosting; are there any other approaches?
- Are there textbooks or tutorials devoted to this area?
| Boosting for regression systems | CC BY-SA 3.0 | null | 2011-04-11T15:10:56.120 | 2011-04-12T06:49:35.103 | 2011-04-12T04:46:37.933 | 183 | 976 | [
"regression",
"boosting"
] |
9438 | 2 | null | 9276 | 0 | null | It seems to me that the problem here is whether the model is too complex to look out without using Monte Carlo simulation.
If the model is all relatively simple then it should be possible to look at it through conventioanl statistics and derive a solution to the question being asked, without re-running the model multip... | null | CC BY-SA 3.0 | null | 2011-04-11T16:37:21.353 | 2011-04-11T16:37:21.353 | null | null | 210 | null |
9441 | 2 | null | 8898 | 2 | null | The ROC curve (Receiver Operating Characteristics) is one of the techniques available. You can check the questions with the tag roc on this site for further details. The wikipedia article
[http://en.wikipedia.org/wiki/Receiver_operating_characteristic](http://en.wikipedia.org/wiki/Receiver_operating_characteristic) an... | null | CC BY-SA 3.0 | null | 2011-04-11T17:33:41.190 | 2011-04-11T18:21:18.727 | 2011-04-11T18:21:18.727 | 4116 | 4116 | null |
9442 | 2 | null | 9190 | 2 | null | [Visual explanations](http://www.edwardtufte.com/tufte/books_visex) or anything else by Tufte is inspirational.
| null | CC BY-SA 3.0 | null | 2011-04-11T18:45:10.307 | 2011-04-11T18:45:10.307 | null | null | 2817 | null |
9443 | 2 | null | 9422 | 2 | null | Your question is above my pay grade, as it were, but I can suggest a first look at [the R survey package](http://faculty.washington.edu/tlumley/survey/), which might implement some of the routines that you'd use to answer your questions.
| null | CC BY-SA 3.0 | null | 2011-04-11T18:52:07.253 | 2011-04-11T18:52:07.253 | null | null | 1764 | null |
9444 | 2 | null | 9220 | 2 | null | Why not test it out?
```
set.seed(347)
x <- rnorm(10000)
y <- rnorm(10000)
x2 <- rnorm(10000)
y2 <- rnorm(10000)
qdf <- data.frame(x,y,x2,y2)
qdf <- data.frame(qdf,(x-x2)^2+(y-y2)^2)
colnames(qdf)[5] <- "euclid"
plot(c(x,y),c(x2,y2))
plot(qdf$euclid)
hist(qdf$euclid)
plot(dentist(qdf$euclid))
```
![Plot 1](https:... | null | CC BY-SA 3.0 | null | 2011-04-11T19:19:33.157 | 2011-04-11T19:19:33.157 | null | null | 776 | null |
9446 | 1 | null | null | 3 | 2765 |
### Context:
My research aims to assess whether parents who have been involved in a relationship with a social worker are at higher risk of child abuse/neglect.
I am trying to establish whether there exists a relationship between social workers' attitude toward the parents that reflect an overall outcome of the case... | How to carry out a Likert scale analysis? | CC BY-SA 3.0 | null | 2011-04-11T19:37:39.740 | 2011-04-12T04:28:22.427 | 2011-04-12T04:28:22.427 | 183 | 4119 | [
"self-study",
"psychometrics",
"scales",
"likert"
] |
9447 | 1 | null | null | 6 | 383 | I would like to predict the average number of days in a year for which two conditions are true:
- daily average temperature is below zero celsius
- the day was preceded by at least four days with daily average temperature below zero celsius
I've historical daily average temperature data for the location available f... | Estimating event probability from historical time series with clear seasonality | CC BY-SA 3.0 | null | 2011-04-11T20:01:06.680 | 2011-04-12T04:32:44.390 | 2011-04-12T04:32:44.390 | 183 | 4120 | [
"time-series",
"predictive-models",
"seasonality"
] |
9448 | 2 | null | 9447 | 2 | null | I think the joint distribution of temperature data on successive days could be reasonably modelled using a multi-variate Gaussian (Gaussian distributions are often used in statistical downscaling of temperature). What I would try would be to regress the mean and covariance matrix of the temperature time series on sine... | null | CC BY-SA 3.0 | null | 2011-04-11T20:28:41.743 | 2011-04-11T20:35:07.073 | 2011-04-11T20:35:07.073 | 887 | 887 | null |
9449 | 1 | 9451 | null | 9 | 11446 | How do you compute confidence intervals for positive predictive value?
The standard error is:
$$SE = \sqrt{ \frac{PPV(1-PPV)}{TP+FP}} $$
Is that right? (here my concern is the denominator)
Does that formula work for any similar ratio in a 2x2 table. E.g. for sensitivity, it would be
$$SE = \sqrt{ \frac{SENS(1-SENS)}{F... | How do you compute confidence intervals for positive predictive value? | CC BY-SA 3.0 | null | 2011-04-11T20:33:14.453 | 2011-04-12T02:45:56.907 | 2011-04-12T02:45:56.907 | 3911 | 3186 | [
"confidence-interval",
"binomial-distribution",
"contingency-tables"
] |
9450 | 2 | null | 9392 | 2 | null | As I understand (see comments to the original question) you want to select a subset of the features by two criteria:
- the subset covers most of the information content of the dataset,
- the subset includes as few features as possible.
The paper [Variable selection in large environmental data sets using principal c... | null | CC BY-SA 3.0 | null | 2011-04-11T22:43:54.183 | 2011-04-13T02:59:16.640 | 2011-04-13T02:59:16.640 | 3911 | 3911 | null |
9451 | 2 | null | 9449 | 12 | null | Your first SE formula is correct. The second SE formula which concerns sensitivity should have the total number of positive cases in the denominator:
$$SE_\text{sensitivity} = \sqrt{ \frac{SENS(1-SENS)}{TP+FN}} $$
The logic is that sensitivity = $\frac{TP}{TP+FN}$, and the denominator in the SE formula is the same.
A... | null | CC BY-SA 3.0 | null | 2011-04-11T23:22:22.870 | 2011-04-11T23:22:22.870 | null | null | 3911 | null |
9452 | 2 | null | 9447 | 0 | null | I know little about meteorology, so my following assumptions may be wrong: today's temperature is similar to yesterday's and the day before yesterday's (maybe more days going back), and also similar to temperature a year age, two years ago, three years ago, etc.
If these assumptions got reinforcement I would use an ARM... | null | CC BY-SA 3.0 | null | 2011-04-11T23:46:32.527 | 2011-04-11T23:46:32.527 | null | null | 3911 | null |
9454 | 1 | 9458 | null | 10 | 8090 |
### Context:
I have two data sets from the same questionnaire run over two years. Each question is measured using a 5-Likert scale.
### Q1: Coding scheme
At the moment, I have coded my responses on a [0, 1] interval, with 0 meaning "most negative response", 1 meaning "most positive response", and other responses ... | Statistical significance of changes over time on a 5-point Likert item | CC BY-SA 3.0 | null | 2011-04-12T02:44:24.163 | 2011-04-13T03:23:53.867 | 2011-04-12T04:01:04.667 | 183 | 4123 | [
"statistical-significance",
"likert"
] |
9456 | 1 | 32961 | null | 7 | 844 | Over the years, I picked up many Statistics Concepts in by a variety of situations and means. I studied some Statistics maybe for a couple semesters almost 10 years back. But I also picked up concepts while doing Machine Learning work. I only understood things in a narrow scope - I always tried to get away knowing only... | Hard exemplary problem sets to work through to solidify my understanding of statistical concepts? | CC BY-SA 3.0 | null | 2011-04-12T03:14:53.957 | 2015-11-11T11:28:53.737 | 2015-11-11T11:28:53.737 | 22468 | 24040 | [
"regression",
"anova",
"t-test",
"references",
"structural-equation-modeling"
] |
9457 | 1 | 9460 | null | 9 | 17075 | This is a question of definition, does the stats community differentiate these terms?
| Is there a difference between seasonality / cyclicality / periodicity | CC BY-SA 3.0 | null | 2011-04-12T03:25:49.320 | 2023-03-10T14:20:04.163 | null | null | 1709 | [
"seasonality"
] |
9458 | 2 | null | 9454 | 7 | null |
### 1. Coding scheme
In terms of assessing statistical significance using a t-test, it is the relative distances between the scale points that matters. Thus, (0, 0.25, 0.5, 0.75, 1) is equivalent to (1, 2, 3, 4, 5).
From my experience an equal distance coding scheme, such as those mentioned previously are the most c... | null | CC BY-SA 3.0 | null | 2011-04-12T04:16:34.013 | 2011-04-13T03:23:53.867 | 2017-04-13T12:44:54.643 | -1 | 183 | null |
9459 | 2 | null | 9457 | 5 | null | Yes, there is a difference.
A classic time series decomposition model is $$ Y = T + S + C + I, $$
where
\begin{align}
Y & = \text{data,} \\
T & = \text{trend,} \\
S & = \text{seasonal,} \\
C & = \text{cyclical,} \\
I & = \text{irregular (i.e. error left over).}
\end{align}
'seasonal' refers to REGULAR patterns that occ... | null | CC BY-SA 4.0 | null | 2011-04-12T04:41:37.783 | 2023-03-10T14:20:04.163 | 2023-03-10T14:20:04.163 | 285236 | 3919 | null |
9460 | 2 | null | 9457 | 3 | null | Perhaps. Though my take could easily be construed as a bit too anal retentive:
I tend to use the term seasonality as a metaphor for the 'seasons' of the year: i.e. Spring, Summer, Fall, Winter (or 'Almost Winter', Winter, 'Still Winter', and 'Construction' if you live in Pennsylvania...). In other words, I would expe... | null | CC BY-SA 3.0 | null | 2011-04-12T05:41:23.373 | 2011-04-12T05:41:23.373 | null | null | 1499 | null |
9461 | 1 | null | null | 5 | 201 | I'm wondering how to test the significance of factor(s) and/or covariate(s) along with modeling the causal relationship among responses.
Let me explain this with a concrete example.
### Example:
Suppose a researcher observed four responses Y1, Y2, Y3, and Y4 along with three covariates X1, X2, and X3 from an experim... | Testing significance of factors and covariates along with modeling causality among responses | CC BY-SA 3.0 | null | 2011-04-12T05:42:54.500 | 2011-09-30T01:33:27.793 | 2011-04-12T06:48:16.857 | 3903 | 3903 | [
"multivariate-analysis",
"experiment-design",
"structural-equation-modeling"
] |
9462 | 2 | null | 9422 | 2 | null |
- Standard formulas for standard errors of a proportion would be suitable. With regards to your question about which companies the "n=100 sample" plan to use in the future, these standard errors would be based on n = 100. If this yields standard errors that are too large for your liking, then you need to increase your... | null | CC BY-SA 3.0 | null | 2011-04-12T05:54:54.803 | 2011-04-13T03:30:18.077 | 2011-04-13T03:30:18.077 | 183 | 183 | null |
9463 | 2 | null | 9437 | 4 | null | While I haven't seen anything specifically, I doubt that it would achieve much, at least for linear regression. Each regression equation is just a linear combination of the predictors:
$$\hat{y}^{(j)} = \sum_i \hat{\beta}_i^{(j)} x_i$$
Most boosting algorithms in turn combine multiple predictors by taking a weighted av... | null | CC BY-SA 3.0 | null | 2011-04-12T06:49:35.103 | 2011-04-12T06:49:35.103 | null | null | 1569 | null |
9464 | 1 | 9466 | null | 4 | 495 | I'm trying to fully understand the confidence interval formal given on [this site](http://www.iro.umontreal.ca/~lisa/twiki/bin/view.cgi/Public/DeepVsShallowComparisonICML2007#Results):
$$\hat{\mu}\pm z_{1-\alpha/2}\sqrt{\frac{\hat{\mu}(1-\hat{\mu})}{n}}$$
so I can reproduce the same type of intervals for my own data. ... | Can someone give me details about a particular confidence interval formula? | CC BY-SA 3.0 | null | 2011-04-12T06:54:42.497 | 2012-01-10T15:00:45.907 | 2012-01-10T15:00:45.907 | 919 | 4127 | [
"confidence-interval",
"binomial-distribution"
] |
9465 | 2 | null | 9407 | 4 | null |
### 1. Power analysis for one-way ANOVA:
Download [G-Power 3](http://www.psycho.uni-duesseldorf.de/abteilungen/aap/gpower3/download-and-register). It allows you to do for a range of statistical tests including ANOVA
- a priori power analysis (sample required given effect size, desired power, and alpha), and
- pos... | null | CC BY-SA 3.0 | null | 2011-04-12T06:59:48.757 | 2011-04-12T06:59:48.757 | null | null | 183 | null |
9466 | 2 | null | 9464 | 2 | null | If $\hat{\mu}$ is the mean error rate computed averaging $N$ error rates from different $N$ tests, an explanation could be:
Let $X$ be the number of errors on $N$ tests, so $X$ is a binomial distributed random variable with mean $N\hat{\mu}$ and variance $N\hat{\mu}(1-\hat{\mu})$ (it is sum of $N$ Bernoulli random var... | null | CC BY-SA 3.0 | null | 2011-04-12T07:45:02.760 | 2011-07-21T13:21:28.917 | 2011-07-21T13:21:28.917 | 2719 | 2719 | null |
9467 | 1 | 9473 | null | 9 | 568 | Following on from my [earlier question](https://stats.stackexchange.com/questions/9341/regularized-fit-from-summarized-data), the solution to the normal equations for ridge regression is given by:
$$\hat{\beta}_\lambda = (X^TX+\lambda I)^{-1}X^Ty$$
Could you offer any guidance for choosing the regularization parameter ... | Regularized fit from summarized data: choosing the parameter | CC BY-SA 3.0 | null | 2011-04-12T08:21:07.817 | 2011-04-12T13:05:16.340 | 2017-04-13T12:44:52.660 | -1 | 439 | [
"regression",
"regularization",
"ridge-regression"
] |
9468 | 1 | 9471 | null | 4 | 422 | Suppose I have a model of stock prices developed using Brownian motion. I have a second time series derived from the first. At each price point in the first time series, I take the arithmetic mean up to that point and that is the data point for my second time series. The volatility of the second time series is lower... | How do I model the volatility of an arithmetic mean? | CC BY-SA 3.0 | null | 2011-04-12T10:33:33.660 | 2011-07-11T14:10:11.577 | null | null | 4128 | [
"standard-deviation"
] |
9469 | 2 | null | 9454 | 3 | null | Wilcoxon Ranksum Test aka Mann-Whitney is the way to go in the case of ordinal data. The bootstrapping solution is also elegant albeit not the "classic" way to go. The Bootstrapping method might also be valuable in case you aim for other things like factor analysis. In case of regression analysis you might chose ordere... | null | CC BY-SA 3.0 | null | 2011-04-12T10:39:25.950 | 2011-04-12T10:39:25.950 | null | null | null | null |
9470 | 1 | null | null | 1 | 813 | I'm trying to replicate the paper of Blundell et al. (2008) to separate permanent and transitory shocks on income on a panel dataset.
He solves the non-linear system of equations using Chamberlain's minimum distance estimator (it is shown in the appendix of the paper), but I haven't found a library, nor in R, nor in ST... | Minimum distance estimator | CC BY-SA 3.0 | null | 2011-04-12T12:12:56.453 | 2012-12-07T23:03:30.100 | 2011-04-12T12:23:58.203 | 2116 | null | [
"nonlinear-regression"
] |
9471 | 2 | null | 9468 | 2 | null | Let's say your stock price series is $S_t$ so that your arithmetic average series is $X_t = \frac{1}{t}\sum_{j=1}^t S_j$. The series of returns that you use to calculate the volatility can be defined in several ways, the most popular being the log-returns series
$R_t = \frac{1}{\delta t} \log \frac{S_t}{S_{t-1}}$
and t... | null | CC BY-SA 3.0 | null | 2011-04-12T12:57:20.417 | 2011-04-12T13:32:31.587 | 2011-04-12T13:32:31.587 | 2425 | 2425 | null |
9472 | 2 | null | 9464 | 3 | null | Answering the following part of your question:
>
I don't quite understand what the
parameters such as alpha and Z mean
$\alpha$ is the parameter that defines the confidence level of the interval. Specifically, the confidence level will be $100(1-\alpha)$%, so to get a 95% confidence interval, set $\alpha=0.05$.
$Z... | null | CC BY-SA 3.0 | null | 2011-04-12T13:01:17.443 | 2011-04-12T13:01:17.443 | null | null | 279 | null |
9473 | 2 | null | 9467 | 7 | null | My answer will be based on a nice review of the problem by Anders Bjorkstorm [Ridge regression and inverse problems](http://www2.math.su.se/matstat/reports/seriea/2000/rep5/report.pdf) (I would recommend to read the whole article).
Part 4 in this review is dedicated to the selection of a parameter $\lambda$ in ridge re... | null | CC BY-SA 3.0 | null | 2011-04-12T13:05:16.340 | 2011-04-12T13:05:16.340 | null | null | 2645 | null |
9474 | 1 | 9527 | null | 1 | 450 | I'm looking for an implementation of a fast maximum rank correlation (MRC) estimator. This will be applied to large-ish sparse matrices (~100,000 by 10,000) in a text-mining application.
I'm working in python and R, so it would be nice to find something in those languages. Failing that, I could probably convert code fr... | Is there a library that implements a fast maximum rank correlation estimator? | CC BY-SA 3.0 | null | 2011-04-12T13:42:54.853 | 2011-04-13T18:07:07.070 | 2017-05-23T12:39:26.523 | -1 | 4110 | [
"r",
"estimation",
"algorithms",
"python"
] |
9475 | 1 | null | null | 11 | 5239 | I have many time series in this format 1 column in which I have date (d/m/yr) format and many columns that represent different time series like here:
```
DATE TS1 TS2 TS3 ...
24/03/2003 0.00 0.00 ...
17/04/2003 -0.05 1.46
11/05/2003 0.46 -3.86
04/06/2003 -2.21 -1.08
28/06/2003 -1.1... | Time series clustering | CC BY-SA 3.0 | null | 2011-04-12T14:33:45.357 | 2020-05-05T04:21:24.840 | 2011-11-28T09:32:21.230 | 2116 | 4133 | [
"r",
"time-series",
"clustering"
] |
9476 | 2 | null | 9385 | 5 | null | The formulae for Holt-Winters' method include forecasting the seasonal component. You don't need $\gamma=0$. See a forecasting textbook for the details.
| null | CC BY-SA 3.0 | null | 2011-04-12T14:48:00.777 | 2011-04-12T14:48:00.777 | null | null | 159 | null |
9477 | 1 | 9480 | null | 13 | 40144 | My attempts:
- I couldn't get confidence intervals in interaction.plot()
- and on the other hand plotmeans() from package 'gplot' wouldn't display two graphs. Furthermore, I couldn't impose two plotmeans() graphs one on top of the other because by default the axis are different.
- I had some success using plotCI() ... | How to draw an interaction plot with confidence intervals? | CC BY-SA 3.0 | null | 2011-04-12T16:07:15.017 | 2011-12-09T15:59:19.010 | 2011-12-09T15:59:19.010 | 930 | 1084 | [
"r",
"data-visualization",
"confidence-interval",
"interaction"
] |
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