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2367 | 2 | null | 2352 | 18 | null | You need to specify the purpose of the model before you can say whether Shao's results are applicable. For example, if the purpose is prediction, then LOOCV makes good sense and the inconsistency of variable selection is not a problem. On the other hand, if the purpose is to identify the important variables and explain... | null | CC BY-SA 2.5 | null | 2010-09-04T03:12:13.830 | 2010-09-14T02:33:16.480 | 2010-09-14T02:33:16.480 | 159 | 159 | null |
2368 | 2 | null | 2296 | 3 | null | You should take a look at some of the nonparametric Bayesian approaches (see [this paper](https://users.umiacs.umd.edu/%7Ehal/docs/daume08ihfrm.pdf) and [this paper](https://web.archive.org/web/20130426174725/http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.133.2649&rep=rep1&type=pdf)) to factor analysis which ... | null | CC BY-SA 4.0 | null | 2010-09-04T03:58:10.850 | 2023-01-06T04:42:12.417 | 2023-01-06T04:42:12.417 | 362671 | 881 | null |
2369 | 2 | null | 2356 | 9 | null | Frequentist confidence intervals bound the rate of false positives (Type I errors), and guarantee their coverage will be bounded below by the confidence parameter, even in the worst case. Bayesian credibility intervals don't.
So if the thing you care about is false positives and you need to bound them, confidence inter... | null | CC BY-SA 2.5 | null | 2010-09-04T04:22:51.527 | 2010-09-04T05:26:37.343 | 2010-09-04T05:26:37.343 | 1122 | 1122 | null |
2370 | 1 | null | null | 8 | 418 | I am trying fit a Cox Proportional Hazard model with coxph with a cmoputer of 12GB of RAM. It keeps running out of memory. Is there a biglm equivalent of coxph?
| Is there a biglm equivalent for coxph? | CC BY-SA 2.5 | null | 2010-09-04T05:19:51.860 | 2010-10-08T16:07:41.500 | 2010-10-08T16:07:41.500 | 8 | 1126 | [
"r",
"survival",
"large-data"
] |
2371 | 2 | null | 2370 | 5 | null | Maybe take a look at the [DatABEL](http://cran.r-project.org/web/packages/DatABEL/) package. I know it is used in genomic studies with large data that may be stored on the HD instead of RAM. From what I read in the help file, you can then apply different kind of model, including survival model.
| null | CC BY-SA 2.5 | null | 2010-09-04T07:17:51.167 | 2010-09-04T11:18:53.117 | 2010-09-04T11:18:53.117 | null | 930 | null |
2372 | 2 | null | 2272 | 18 | null | I disagree with Srikant's answer on one fundamental point. Srikant stated this:
"Inference Problem:
Your inference problem is: What values of θ are reasonable given the observed data x?"
In fact this is the BAYESIAN INFERENCE PROBLEM. In Bayesian statistics we seek to calculate P(θ| x) i.e the probability of the param... | null | CC BY-SA 2.5 | null | 2010-09-04T10:22:19.790 | 2010-09-04T10:28:57.360 | 2010-09-04T10:28:57.360 | 521 | 521 | null |
2373 | 2 | null | 2272 | 7 | null | As I understand it: A credible interval is a statement of the range of values for the statistic of interest that remain plausible given the particular sample of data that we have actually observed. A confidence interval is a statement of the frequency with which the true value lies in the confidence interval when the ... | null | CC BY-SA 2.5 | null | 2010-09-04T11:07:44.643 | 2010-09-04T11:07:44.643 | null | null | 887 | null |
2374 | 1 | 2375 | null | 18 | 26857 | I used to analyse items from a psychometric point of view. But now I am trying to analyse other types of questions on motivation and other topics. These questions are all on Likert scales. My initial thought was to use factor analysis, because the questions are hypothesised to reflect some underlying dimensions.
- But... | Factor analysis of questionnaires composed of Likert items | CC BY-SA 3.0 | null | 2010-09-04T11:15:48.317 | 2014-05-14T23:35:57.370 | 2011-10-04T07:04:04.897 | 930 | 1154 | [
"factor-analysis",
"scales",
"psychometrics",
"likert",
"psychology"
] |
2375 | 2 | null | 2374 | 23 | null | From what I've seen so far, FA is used for attitude items as it is for other kind of rating scales. The problem arising from the metric used (that is, "are Likert scales really to be treated as numeric scales?" is a long-standing debate, but providing you check for the bell-shaped response distribution you may handle t... | null | CC BY-SA 2.5 | null | 2010-09-04T12:21:17.757 | 2010-09-07T10:18:19.340 | 2010-09-07T10:18:19.340 | 930 | 930 | null |
2376 | 2 | null | 2348 | 2 | null | [MH sampling](http://en.wikipedia.org/wiki/Metropolis%E2%80%93Hastings_algorithm) is used when it's difficult to sample from the target distribution (e.g., when the prior isn't [conjugate](http://en.wikipedia.org/wiki/Conjugate_prior) to the likelihood). So you use a proposal distribution to generate samples and accept... | null | CC BY-SA 2.5 | null | 2010-09-04T21:25:49.863 | 2010-09-04T21:25:49.863 | null | null | 881 | null |
2377 | 1 | 2392 | null | 12 | 5104 | I am curious if there is a transform which alters the skew of a random variable without affecting the kurtosis. This would be analogous to how an affine transform of a RV affects the mean and variance, but not the skew and kurtosis (partly because the skew and kurtosis are defined to be invariant to changes in scale). ... | A transform to change skew without affecting kurtosis? | CC BY-SA 2.5 | null | 2010-09-04T23:00:04.117 | 2011-05-20T09:56:36.523 | 2010-09-08T08:09:20.603 | 183 | 795 | [
"data-transformation",
"random-variable",
"moments"
] |
2378 | 2 | null | 2348 | 1 | null | In physics, statistical physics in particular, Metropolis-type algorithm(s) are used extensively. There are really countless variants of these, and the new ones are being actively developed. It's much too broad topic to give any sort of expanation here, so if you're interested you can start e.g. from [these lecture not... | null | CC BY-SA 2.5 | null | 2010-09-05T03:23:37.750 | 2010-09-05T03:23:37.750 | null | null | 1197 | null |
2379 | 1 | 2415 | null | 86 | 17030 | Mathematics has its famous [Millennium Problems](http://en.wikipedia.org/wiki/Millennium_Prize_Problems) (and, historically, [Hilbert's 23](http://en.wikipedia.org/wiki/Hilbert%27s_problems)), questions that helped to shape the direction of the field.
I have little idea, though, what the Riemann Hypotheses and P vs. NP... | What are the 'big problems' in statistics? | CC BY-SA 2.5 | null | 2010-09-05T04:16:29.570 | 2019-10-13T08:40:34.290 | 2014-07-19T12:45:28.427 | 22468 | 1106 | [
"history"
] |
2380 | 2 | null | 2379 | 6 | null | As an example of the general spirit (if not quite specificity) of answer I'm looking for, I found a "Hilbert's 23"-inspired lecture by David Donoho at a "Math Challenges of the 21st Century" conference:
[High-Dimensional Data Analysis: The Curses and Blessings of Dimensionality](http://www-stat.stanford.edu/~donoho/Lec... | null | CC BY-SA 2.5 | null | 2010-09-05T05:23:25.660 | 2010-09-05T05:36:49.673 | 2010-09-05T05:36:49.673 | 1106 | 1106 | null |
2381 | 2 | null | 322 | 2 | null | Jaynes [shows](http://omega.albany.edu:8008/ETJ-PDF/cc11g.pdf) how to derive Shannon's entropy from basic principles in his [book](http://omega.albany.edu:8008/JaynesBookPdf.html).
One idea is that if you approximate $n!$ by $n^n$, entropy is the rewriting of the following quantity
$$\frac{1}{n}\log \frac{n!}{(n p_1)!\... | null | CC BY-SA 2.5 | null | 2010-09-05T06:49:22.653 | 2010-09-05T06:49:22.653 | null | null | 511 | null |
2382 | 2 | null | 2379 | 4 | null | Mathoverflow has a similar question about [big problems in probability theory](https://mathoverflow.net/questions/37151/what-are-the-big-problems-in-probability-theory).
It would appear from that page that the biggest questions are to do with self avoiding random walks and percolations.
| null | CC BY-SA 2.5 | null | 2010-09-05T08:36:31.083 | 2010-09-05T08:59:37.680 | 2017-04-13T12:58:32.177 | -1 | 352 | null |
2383 | 2 | null | 7 | 2 | null | [Here's another list](https://web.archive.org/web/20151223171454/https://sites.google.com/site/munaga71/Home/datasetlinks) that might be of help.
| null | CC BY-SA 4.0 | null | 2010-09-05T09:22:57.927 | 2022-11-22T03:05:03.507 | 2022-11-22T03:05:03.507 | 362671 | 976 | null |
2384 | 1 | 2387 | null | 2 | 356 | Say I have a series of forecasts and observations like this:
```
EntityF EntityO
2004 120 125
2006 166 173
2008 150 167
2010 152 -
```
And assume that the (i) entity is the same and (ii) the forecasting methodology is constant.
I'd like to
- Produce a meaningful metric of the f... | Testing prediction time series against real data | CC BY-SA 2.5 | null | 2010-09-05T11:15:57.607 | 2010-09-16T06:33:13.167 | 2010-09-16T06:33:13.167 | null | 722 | [
"time-series",
"forecasting"
] |
2385 | 1 | 2389 | null | 6 | 368 | I have a very large data set which I would like to summarise in as small a space as possible, preferably one side of A4.
The data are from a customer satisfaction survey and are Likert-type scales, 5 scales for each work area, with 190 work areas in total. I would also like to represent the response rate on the visuali... | Data visualisation- summarise 190 means and response rates | CC BY-SA 2.5 | null | 2010-09-05T11:37:47.670 | 2010-10-08T16:06:54.717 | 2010-10-08T16:06:54.717 | 8 | 199 | [
"data-visualization",
"large-data"
] |
2386 | 2 | null | 2379 | 2 | null | My answer would be the struggle between frequentist and Bayesian statistics. When people ask you which you "believe in", this is not good! Especially for a scientific discipline.
| null | CC BY-SA 2.5 | null | 2010-09-05T11:43:35.673 | 2010-09-05T11:43:35.673 | null | null | 561 | null |
2387 | 2 | null | 2384 | 5 | null |
- You could use Mean Absolute Error
(mean of $|F-O|$) or Mean Squared
Error (mean of $(F-O)^2$)
- If your forecast method is unbiased, then the best estimate of a future forecast error is 0 and the variance of the forecast error can be estimated by the MSE.
| null | CC BY-SA 2.5 | null | 2010-09-05T12:50:11.763 | 2010-09-05T12:50:11.763 | null | null | 159 | null |
2388 | 2 | null | 2385 | 2 | null | I would suggest you check out either box-plots (if you have an intro text to R, box plots always seem to be one of the first plots they use), or you can plot the means of each group on the Y axis and use the X-axis to represent each of your 190 work areas (and then maybe put error bars representing a confidence interva... | null | CC BY-SA 2.5 | null | 2010-09-05T13:22:42.097 | 2010-09-05T13:22:42.097 | null | null | 1036 | null |
2389 | 2 | null | 2385 | 6 | null | I find a heatmap to be one of the most effective ways of summarizing large amounts of multi-dimensional data in a confined space. The LearnR blog has [a nice example](http://learnr.wordpress.com/2010/01/26/ggplot2-quick-heatmap-plotting/) of creating one in ggplot2.
| null | CC BY-SA 2.5 | null | 2010-09-05T15:12:53.097 | 2010-09-05T15:12:53.097 | null | null | 5 | null |
2390 | 1 | 2394 | null | 7 | 4938 | I'm familiar with what the 2nd moment (variance) indicates as well as what the 3rd moment (skewness) indicates. I know that on a histogram the 4th moment (kurtosis) indicates the "peeked-ness" of the data. My question asks what are the practical implications/interpretations of a kurtotic distribution. I'm asking thi... | What practical implications/interpretations are there of a kurtotic distribution? | CC BY-SA 2.5 | null | 2010-09-05T15:59:43.343 | 2013-11-27T09:55:50.423 | null | null | 196 | [
"definition",
"interpretation",
"kurtosis"
] |
2391 | 1 | 2424 | null | 68 | 78679 | Suppose that I have three populations with four, mutually exclusive characteristics. I take random samples from each population and construct a crosstab or frequency table for the characteristics that I am measuring. Am I correct in saying that:
- If I wanted to test whether there is any relationship between the pop... | What is the relationship between a chi squared test and test of equal proportions? | CC BY-SA 3.0 | null | 2010-09-05T16:35:45.123 | 2018-11-27T21:26:23.710 | 2018-11-27T21:26:23.710 | 28666 | 1195 | [
"chi-squared-test",
"proportion",
"contingency-tables",
"z-test"
] |
2392 | 2 | null | 2377 | 6 | null | My answer is the beginnings of a total hack, but I am not aware of any established way to do what you ask.
My first step would be to rank order your dataset you can find the proportional position in your dataset and then transform it to a normal distribution, this method was used in Reynolds & Hewitt, 1996. See samp... | null | CC BY-SA 2.5 | null | 2010-09-05T17:07:00.207 | 2010-09-05T17:07:00.207 | null | null | 196 | null |
2394 | 2 | null | 2390 | 9 | null | The kurtosis also indicates the "fat tailedness" of the distribution. A distribution with high kurtosis will have many extreme events (events far away from the center) and many "typical" events (events near the center). A distribution with low kurtosis will have events a moderate distance from the center.
This picture... | null | CC BY-SA 2.5 | null | 2010-09-05T18:55:00.790 | 2010-09-05T18:55:00.790 | null | null | 1146 | null |
2395 | 2 | null | 2379 | 4 | null | You might check out Harvard's ["Hard Problems in the Social Sciences' colloquium](http://socialscience.fas.harvard.edu/hardproblems) held earlier this year. Several of these talks offer issues in the use of statistics and modeling in the social sciences.
| null | CC BY-SA 2.5 | null | 2010-09-05T19:18:58.237 | 2010-09-05T19:18:58.237 | null | null | 401 | null |
2396 | 2 | null | 2379 | 13 | null | I'm not sure how big they are, but there is a [Wikipedia page](http://en.wikipedia.org/wiki/Unsolved_problems_in_Statistics) for unsolved problems in statistics. Their list includes:
>
Inference and testing
Systematic errors
Admissability of the Graybill–Deal estimator
Combining dependent p-values in Meta-analysis... | null | CC BY-SA 3.0 | null | 2010-09-05T19:19:03.197 | 2018-01-13T14:36:38.283 | 2018-01-13T14:36:38.283 | 7290 | 196 | null |
2397 | 1 | 2408 | null | 11 | 7380 | I'm looking for the limiting distribution of multinomial distribution over d outcomes. IE, the distribution of the following
$$\lim_{n\to \infty} n^{-\frac{1}{2}} \mathbf{X_n}$$
Where $\mathbf{X_n}$ is a vector value random variable with density $f_n(\mathbf{x})$ for $\mathbf{x}$ such that $\sum_i x_i=n$, $x_i\in \math... | Asymptotic distribution of multinomial | CC BY-SA 3.0 | null | 2010-09-05T19:52:53.743 | 2020-08-06T19:10:04.343 | 2017-04-13T12:19:38.853 | -1 | 511 | [
"asymptotics",
"multinomial-distribution"
] |
2398 | 2 | null | 2397 | 2 | null | It looks to me like Wasserman's covariance matrix is singular, to see, multiply it by a vector of $d$ ones, i.e. $[1,1,1,\dots,1]^\prime$ of length $d$.
[Wikipedia](http://en.wikipedia.org/wiki/Multinomial_distribution) gives the same covariance matrix anyway. If we restrict ourselves to just a binomial distribution th... | null | CC BY-SA 2.5 | null | 2010-09-05T22:04:14.960 | 2010-09-05T22:20:30.703 | 2010-09-05T22:20:30.703 | 352 | 352 | null |
2399 | 2 | null | 2390 | 0 | null | There is the [Kurtosis risk](http://en.wikipedia.org/wiki/Kurtosis_risk) which isn't explained fantastically well at that link.
In general, measures of normality (or deviation therefrom) are crucial if you are using analyses that assume normality. For example, the standard workhorse Pearson-r correlation coefficient i... | null | CC BY-SA 2.5 | null | 2010-09-05T22:41:39.633 | 2010-09-05T22:41:39.633 | null | null | 869 | null |
2400 | 2 | null | 322 | 3 | null | Grünwald and Dawid's paper [Game theory, maximum entropy, minimum discrepancy and robust Bayesian decision theory](http://projecteuclid.org/euclid.aos/1091626173) discuss generalisations of the traditional notion of entropy. Given a loss, its associated entropy function is the mapping from a distribution to the minimal... | null | CC BY-SA 2.5 | null | 2010-09-05T23:52:45.783 | 2010-09-05T23:52:45.783 | null | null | 1201 | null |
2401 | 1 | 2409 | null | 12 | 2591 | I've never liked how people typically analyze data from Likert scales as if error were continuous & Gaussian when there are reasonable expectations that these assumptions are violated at least at the extremes of the scales. What do you think of the following alternative:
If the response takes value $k$ on an $n$-point ... | Is it appropriate to treat n-point Likert scale data as n trials from a binomial process? | CC BY-SA 3.0 | null | 2010-09-06T00:58:21.607 | 2020-11-15T08:12:12.180 | 2020-11-15T08:11:25.820 | 930 | 364 | [
"binomial-distribution",
"likert",
"scales",
"psychometrics",
"psychology"
] |
2402 | 2 | null | 2401 | 9 | null | If you really wish to abandon the assumption of interval level data for likert scales I would suggest that you assume the data to be a ordered logit or probit instead. Likert scales usually measure strength of response and hence higher values should indicate a stronger response on the underlying item of interest.
Supp... | null | CC BY-SA 2.5 | null | 2010-09-06T01:15:20.940 | 2010-09-06T01:21:13.017 | 2010-09-06T01:21:13.017 | null | null | null |
2403 | 2 | null | 2377 | 1 | null | Another possible interesting technique has come to mind, though this doesn't quite answer the question, is to transform a sample to have a fixed sample L-skew and sample L-kurtosis (as well as a fixed mean and L-scale). These four constraints are linear in the order statistics. To keep the transform monotonic on a samp... | null | CC BY-SA 2.5 | null | 2010-09-06T02:32:10.617 | 2010-09-06T02:32:10.617 | null | null | 795 | null |
2404 | 2 | null | 2390 | 2 | null | I seem to remember that the median has a smaller standard error than the mean when the samples are drawn from a leptokurtic distribution, but the mean has a smaller standard error when the distribution is platykurtic. I think I read this in one of Wilcox' books. Thus the kurtosis may dictate which kinds of locational t... | null | CC BY-SA 2.5 | null | 2010-09-06T02:58:30.540 | 2010-09-06T02:58:30.540 | null | null | 795 | null |
2405 | 1 | 2406 | null | 5 | 653 | Suppose that I have a market research survey with the question "What brand of television are you planning on buying" and then also have a choice that is "I don't plan on buying a television." Respondents may choose more than one brand, but they may not make any other choices if they select that they do not plan on buy... | Survey questionnaire rebasing after removal of a selection | CC BY-SA 2.5 | null | 2010-09-06T04:14:20.773 | 2010-09-16T12:43:13.977 | 2010-09-16T12:43:13.977 | null | 1195 | [
"survey"
] |
2406 | 2 | null | 2405 | 4 | null | The short answer: what you propose to do sounds reasonable.
It often occurs in survey research that a question only applies to a subset of the population.
In such situations you typically want to say:
- "Of American/Australian/French/etc. adults where this question is applicable, $X\%$ believes/intends to do/thinks/et... | null | CC BY-SA 2.5 | null | 2010-09-06T06:45:09.040 | 2010-09-06T08:06:05.930 | 2010-09-06T08:06:05.930 | 183 | 183 | null |
2407 | 2 | null | 2385 | 3 | null | To give you a few more things to look at:
- Principal components - look at some previous answers about PC. In particular, this answer may be helpful.
- Cluster analysis. This page gives quite a nice overall in R.
I would recommend trying as many things as possible and see what comes out. Once you have your data in ... | null | CC BY-SA 2.5 | null | 2010-09-06T07:48:01.307 | 2010-09-06T07:48:01.307 | 2017-04-13T12:44:33.237 | -1 | 8 | null |
2408 | 2 | null | 2397 | 7 | null | The covariance is still non-negative definite (so is a valid [multivariate normal distribution](http://en.wikipedia.org/wiki/Multivariate_normal_distribution)), but not positive definite: what this means is that (at least) one element of the random vector is a linear combination of the others.
As a result, any draw fro... | null | CC BY-SA 2.5 | null | 2010-09-06T10:36:39.710 | 2010-09-06T11:28:12.913 | 2010-09-06T11:28:12.913 | 495 | 495 | null |
2409 | 2 | null | 2401 | 17 | null | I don't know of any articles related to your question in the psychometric literature. It seems to me that ordered logistic models allowing for random effect components can handle this situation pretty well.
I agree with @Srikant and think that a proportional odds model or an ordered probit model (depending on the link ... | null | CC BY-SA 3.0 | null | 2010-09-06T10:43:15.640 | 2013-01-17T14:37:11.723 | 2017-04-13T12:44:37.793 | -1 | 930 | null |
2410 | 1 | 2411 | null | 14 | 1942 | A colleague in my office said to me today "Tree models aren't good because they get caught by extreme observations".
A search here resulted in [this thread](https://stats.stackexchange.com/questions/1292/what-is-the-weak-side-of-decision-trees) that basically supports the claim.
Which leads me to the question - under w... | Can CART models be made robust? | CC BY-SA 2.5 | null | 2010-09-06T14:59:09.557 | 2022-04-27T13:37:17.247 | 2017-04-13T12:44:37.583 | -1 | 253 | [
"regression",
"classification",
"robust",
"cart"
] |
2411 | 2 | null | 2410 | 15 | null | No, not in their present forms.
The problem is that convex loss functions cannot be made to be robust to contamination by outliers (this is a well known fact since the 70's but keeps being rediscovered periodically, see for instance this paper for one recent such re-discovery):
[http://www.cs.columbia.edu/~rocco/Public... | null | CC BY-SA 4.0 | null | 2010-09-06T15:20:06.863 | 2019-06-01T09:03:30.480 | 2020-06-11T14:32:37.003 | -1 | 603 | null |
2412 | 2 | null | 2410 | 6 | null | You might consider using [Breiman's](https://en.wikipedia.org/wiki/Leo_Breiman) bagging or [random forests](https://en.wikipedia.org/wiki/Random_forest). One good reference is [Breiman "Bagging Predictors"](https://doi.org/10.1007/BF00058655) (1996). Also summarized in Clifton Sutton's ["Classification and Regression... | null | CC BY-SA 4.0 | null | 2010-09-06T15:20:57.673 | 2022-04-27T13:37:17.247 | 2022-04-27T13:37:17.247 | 79696 | 5 | null |
2413 | 2 | null | 328 | 4 | null | You should check out [http://area51.stackexchange.com/proposals/117/quantitative-finance?referrer=b3Z9BBygZU6P1xPZSakPmQ2](http://area51.stackexchange.com/proposals/117/quantitative-finance?referrer=b3Z9BBygZU6P1xPZSakPmQ2), they are trying to start one on stackexhange.com
| null | CC BY-SA 2.5 | null | 2010-09-06T16:26:44.860 | 2010-09-06T16:26:44.860 | null | null | 1137 | null |
2415 | 2 | null | 2379 | 48 | null | A big question should involve key issues of statistical methodology or, because statistics is entirely about applications, it should concern how statistics is used with problems important to society.
This characterization suggests the following should be included in any consideration of big problems:
- How best to con... | null | CC BY-SA 2.5 | null | 2010-09-06T17:27:01.890 | 2010-09-06T17:27:01.890 | null | null | 919 | null |
2416 | 5 | null | null | 0 | null | Overview
[Mixed models](http://en.wikipedia.org/wiki/Mixed_model) are linear models that include both fixed effects and random effects*. They are used to model longitudinal or nested data; such data do not have independent errors and mixed models can account for the arising correlations. Mixed models are also known as ... | null | CC BY-SA 3.0 | null | 2010-09-06T18:24:38.507 | 2015-10-25T00:57:09.050 | 2015-10-25T00:57:09.050 | 28666 | null | null |
2417 | 4 | null | null | 0 | null | Mixed (aka multilevel or hierarchical) models are linear models that include both fixed effects and random effects. They are used to model longitudinal or nested data. | null | CC BY-SA 3.0 | null | 2010-09-06T18:24:38.507 | 2015-12-15T01:07:11.833 | 2015-12-15T01:07:11.833 | 28666 | null | null |
2419 | 1 | 282321 | null | 15 | 11914 | Is there a a good python library for training boosted decision trees ?
| Boosted decision trees in python? | CC BY-SA 3.0 | null | 2010-09-06T19:00:03.070 | 2022-06-18T07:57:59.773 | 2012-03-11T09:58:27.203 | null | 961 | [
"python",
"cart",
"boosting"
] |
2420 | 1 | null | null | 2 | 366 | I have some data values of type date/time (the last date that a resource was accessed) and I wish to chart this data on the y-axis against the different categories of resource on the x-axis.
What would be a sensible type of chart for displaying this sort of data ? For example is a histogram / bar chart satisfactory o... | Date/Time data on the y-axis | CC BY-SA 2.5 | null | 2010-09-06T19:00:42.580 | 2010-09-07T08:21:20.630 | 2010-09-07T07:49:42.283 | 414 | 414 | [
"data-visualization"
] |
2421 | 2 | null | 2419 | 12 | null | My first look would be at [Orange](http://www.ailab.si/orange/), which is a fully-featured app for ML, with a backend in Python. See e.g. [orngEnsemble](http://www.ailab.si/orange/doc/modules/orngEnsemble.htm).
Other promising projects are [mlpy](https://mlpy.fbk.eu/) and the [scikit.learn](http://scikit-learn.sourcefo... | null | CC BY-SA 2.5 | null | 2010-09-06T19:28:19.213 | 2010-09-06T19:35:13.690 | 2010-09-06T19:35:13.690 | 930 | 930 | null |
2422 | 2 | null | 2420 | 7 | null | I presume that you have only a few resources that you are interested in? If so, then histograms are fine, or you could also try box-plots:
```
#Some R code
#Create random dates for resource A & resource B
dates.a = as.Date(rnorm(100, 200, 100), origin="2008-01-01")
dates.b = as.Date(rnorm(100, 300, 50), origin="2008-01... | null | CC BY-SA 2.5 | null | 2010-09-06T20:25:58.380 | 2010-09-06T20:25:58.380 | null | null | 8 | null |
2423 | 1 | 2426 | null | 13 | 4455 | Can anyone recommend some books that are considered to be standard references for classical (frequentist) statistics? IE, fairly comprehensive, and also, been around for a while so that typos and mistakes in formulas had a chance to be checked and corrected
| Standard reference for classical mathematical statistics? | CC BY-SA 2.5 | null | 2010-09-06T20:28:53.090 | 2013-08-29T13:47:48.317 | 2010-09-08T06:40:30.013 | 183 | 511 | [
"references",
"mathematical-statistics"
] |
2424 | 2 | null | 2391 | 33 | null | Very short answer:
The chi-Squared test (`chisq.test()` in R) compares the observed frequencies in each category of a contingency table with the expected frequencies (computed as the product of the marginal frequencies). It is used to determine whether the deviations between the observed and the expected counts are to... | null | CC BY-SA 4.0 | null | 2010-09-06T20:39:55.953 | 2018-06-14T13:55:01.687 | 2018-06-14T13:55:01.687 | 77478 | 930 | null |
2425 | 2 | null | 2423 | 9 | null | I have found Statistical Inference by Casella and Berger to be a relatively comprehensive introduction.
| null | CC BY-SA 2.5 | null | 2010-09-06T21:47:23.083 | 2010-09-06T21:47:23.083 | null | null | 743 | null |
2426 | 2 | null | 2423 | 5 | null | E. L. Lehmann, Theory of Point Estimation, 1983, and its companion book, Testing Statistical Hypotheses.
(NB: The latest edition of TPE, coauthored with George Casella, has not been getting good reviews on Amazon, but the original is still a classic.)
| null | CC BY-SA 3.0 | null | 2010-09-06T22:05:28.123 | 2013-08-29T13:47:48.317 | 2013-08-29T13:47:48.317 | 22047 | 919 | null |
2427 | 1 | 2442 | null | 13 | 4019 | I would like to solve [Project Euler 213](http://projecteuler.net/index.php?section=problems&id=213) but don't know where to start because I'm a layperson in the field of Statistics, notice that an accurate answer is required so the Monte Carlo method won't work. Could you recommend some statistics topics for me to rea... | How should one approch Project Euler problem 213 ("Flea Circus")? | CC BY-SA 3.0 | null | 2010-09-06T22:44:39.583 | 2016-12-01T21:53:26.533 | 2016-12-01T10:07:28.067 | 28666 | 18 | [
"self-study",
"monte-carlo",
"markov-process"
] |
2428 | 2 | null | 2427 | 1 | null | I suspect that some knowledge of discrete-time [Markov chains](http://en.wikipedia.org/wiki/Markov_chain) could prove useful.
| null | CC BY-SA 2.5 | null | 2010-09-06T23:09:16.800 | 2010-09-06T23:09:16.800 | null | null | 495 | null |
2429 | 2 | null | 2423 | 5 | null | I'd recommend [Theory of Statistics](http://rads.stackoverflow.com/amzn/click/0387945466) by Mark Schervish.
| null | CC BY-SA 2.5 | null | 2010-09-07T02:04:47.937 | 2010-09-07T02:04:47.937 | null | null | 881 | null |
2430 | 1 | null | null | 3 | 1738 | I am trying to identify approximate 3% of the population for some characteristic feature. Standard decision tree or logistic regression gives too many false positives. Is there a chance that rules based classifier can improve performance? I would like to get approx 75% of recall with 95% of precission (i.e. FalsePositi... | When does rules based classifier outperforms decision trees? | CC BY-SA 2.5 | null | 2010-09-07T03:12:09.853 | 2022-04-30T12:57:37.987 | 2010-09-07T08:05:20.140 | null | null | [
"machine-learning",
"classification"
] |
2431 | 2 | null | 2423 | 3 | null | [All of Statistics](http://rads.stackoverflow.com/amzn/click/0387402721)
| null | CC BY-SA 2.5 | null | 2010-09-07T03:19:29.143 | 2010-09-07T03:19:29.143 | null | null | 183 | null |
2432 | 1 | 2445 | null | 15 | 2515 |
- Is there a modelling technique like LOESS that allows for zero, one, or more discontinuities, where the timing of the discontinuities are not known apriori?
- If a technique exists, is there an existing implementation in R?
| LOESS that allows discontinuities | CC BY-SA 2.5 | null | 2010-09-07T03:24:59.747 | 2017-11-01T11:32:17.717 | 2017-11-01T11:32:17.717 | 28666 | 183 | [
"r",
"regression",
"curve-fitting",
"change-point",
"loess"
] |
2433 | 2 | null | 2432 | 6 | null | Here are some methods and associated R packages to solve this problem
Wavelet thresolding estimation in regression allows for discontonuities. You may use the package wavethresh in R.
A lot of tree based methods (not far from the idea of wavelet) are usefull when you have disconitnuities. Hence package treethresh, p... | null | CC BY-SA 2.5 | null | 2010-09-07T05:12:15.380 | 2011-02-17T17:10:30.397 | 2011-02-17T17:10:30.397 | 223 | 223 | null |
2434 | 2 | null | 2427 | 7 | null | Could you not iterate through the probabilities of occupation of the cells for each flea. That is, flea k is initially in cell (i(k),j(k)) with probability 1. After 1 iteration, he has probability 1/4 in each of the 4 adjacent cells (assuming he's not on an edge or in a corner). Then the next iteration, each of those q... | null | CC BY-SA 2.5 | null | 2010-09-07T07:18:25.347 | 2010-09-07T07:23:41.247 | 2010-09-07T07:23:41.247 | 805 | 805 | null |
2435 | 2 | null | 2430 | 5 | null | Probably you just have unbalanced classes (3% to 97%, if I understood well) -- try balancing them (get this 3% of true ones and about equal number of false ones) and check the classifier build on this case. If you are worried that you have thrown out most of your data, iterate it few times and connect them with some si... | null | CC BY-SA 2.5 | null | 2010-09-07T08:04:24.807 | 2010-09-07T08:04:24.807 | null | null | null | null |
2437 | 2 | null | 2420 | 1 | null | My personal preference would be for box plots. If the distributions of date/time in one or more categories are skewed, then box plots would definitely be more informative than bars.
| null | CC BY-SA 2.5 | null | 2010-09-07T08:21:20.630 | 2010-09-07T08:21:20.630 | null | null | 266 | null |
2438 | 2 | null | 7 | 9 | null | NIST provides a [Reference Dataset archive](http://www.itl.nist.gov/div898/strd/general/dataarchive.html).
| null | CC BY-SA 2.5 | null | 2010-09-07T08:58:26.777 | 2010-09-07T08:58:26.777 | null | null | 830 | null |
2439 | 1 | 2440 | null | 9 | 798 | A cursory search reveals that [Latin squares](http://en.wikipedia.org/wiki/Latin_square) are fairly extensively used in the design of experiments. During my PhD, I have studied various theoretical properties of Latin squares (from a combinatorics point-of-view), but do not have a deep understanding of what is it about... | Desirable and undesirable properties of Latin squares in experiments? | CC BY-SA 2.5 | null | 2010-09-07T11:48:49.003 | 2022-05-15T03:50:45.243 | 2022-05-15T03:50:45.243 | 11887 | 386 | [
"experiment-design",
"latin-square"
] |
2440 | 2 | null | 2439 | 8 | null | Imagine:
- you were interested in the effect of word type (nouns, adjectives, adverbs, and verbs) on recall.
- you wanted to include word type as a within-subjects factor (i.e., all participants were exposed to all conditions)
Such a design would raise the issue of carry over effects. I.e., the order of the condit... | null | CC BY-SA 2.5 | null | 2010-09-07T13:03:36.843 | 2010-09-07T13:47:53.373 | 2010-09-07T13:47:53.373 | 183 | 183 | null |
2441 | 2 | null | 2427 | 5 | null | An analytical approach may be tedious and I have not thought through the intricacies but here is an approach that you may want to consider. Since you are interested in the expected number of cells that are empty after 50 rings you need to define a markov chain over the "No of the fleas in a cell" rather than the positi... | null | CC BY-SA 2.5 | null | 2010-09-07T14:37:45.337 | 2010-09-07T14:37:45.337 | 2017-04-13T12:44:21.160 | -1 | null | null |
2442 | 2 | null | 2427 | 12 | null | You're right; Monte Carlo is impracticable. (In a naive simulation--that is, one that exactly reproduces the problem situation without any simplifications--each iteration would involve 900 flea moves. A crude estimate of the proportion of empty cells is $1/e$, implying the variance of the Monte-Carlo estimate after $... | null | CC BY-SA 2.5 | null | 2010-09-07T14:51:35.883 | 2010-09-07T14:51:35.883 | null | null | 919 | null |
2443 | 2 | null | 2391 | 34 | null | A chi-square test for equality of two proportions is exactly the same thing as a $z$-test. The chi-squared distribution with one degree of freedom is just that of a normal deviate, squared. You're basically just repeating the chi-squared test on a subset of the contingency table. (This is why @chl gets the exact same $... | null | CC BY-SA 3.0 | null | 2010-09-07T15:12:36.180 | 2013-09-07T16:27:36.933 | 2013-09-07T16:27:36.933 | 7290 | 1122 | null |
2445 | 2 | null | 2432 | 15 | null | It sounds like you want to perform multiple changepoint detection followed by independent smoothing within each segment. (Detection can be online or not, but your application is not likely to be online.) There's a lot of literature on this; Internet searches are fruitful.
- DA Stephens wrote a useful introduction ... | null | CC BY-SA 2.5 | null | 2010-09-07T15:45:24.470 | 2010-09-08T22:31:40.630 | 2010-09-08T22:31:40.630 | 919 | 919 | null |
2446 | 1 | null | null | 8 | 522 | In order to correlate or compare means of two dependent variables.
In my case, I need to correlate individual (e.g. subjects=30) slope values from different conditions (e.g. conditions=4), and each slope value summarizes the relation between the dependent variable (e.g. measured 4 times in each level of the independen... | How to choose df for comparisons between summary statistics (e.g. slope values)? | CC BY-SA 2.5 | null | 2010-09-07T16:01:51.877 | 2010-09-21T20:33:56.847 | 2010-09-15T17:04:17.370 | 1084 | 1084 | [
"correlation",
"regression",
"statistical-significance",
"degrees-of-freedom"
] |
2447 | 2 | null | 2432 | 7 | null | do it with koencker's broken line regression, see page 18 of this vignette
[http://cran.r-project.org/web/packages/quantreg/vignettes/rq.pdf](http://cran.r-project.org/web/packages/quantreg/vignettes/rq.pdf)
In response to Whuber last comment:
This estimator is defined like this.
$x\in\mathbb{R}$, $x_{(i)}\geq x_{(i-1)... | null | CC BY-SA 2.5 | null | 2010-09-07T16:03:44.463 | 2010-09-10T17:36:27.873 | 2010-09-10T17:36:27.873 | 603 | 603 | null |
2448 | 2 | null | 2430 | 4 | null | This is only valid for the logit: you can use another link function (complementary log-log or cloglog in short). This is a variation of the classical logit function that allows for assymetry (when one tail of the link function does not go to 0 at the same speed as the other tail goes to 1). I had a very good experience... | null | CC BY-SA 4.0 | null | 2010-09-07T16:09:53.610 | 2022-04-30T12:57:37.987 | 2022-04-30T12:57:37.987 | 79696 | 603 | null |
2449 | 2 | null | 2430 | 2 | null | One strategy would be to use margin based methods with uneven margins (see [this paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.130.4424&rep=rep1&type=pdf)). Or you can use [active learning](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.155.1168&rep=rep1&type=pdf) to provide the learner with... | null | CC BY-SA 2.5 | null | 2010-09-07T16:45:12.883 | 2010-09-07T17:14:37.033 | 2010-09-07T17:14:37.033 | 881 | 881 | null |
2450 | 2 | null | 2423 | 4 | null | A comprehensive and authoratative reference is Kendall's Advanced Theory of Statistics
- Volume 1 Distribution Theory
- Volume 2A Classical Inference and Linear Models
There is also a Volume 2B but it is Bayesian Inference.
Other than those, I agree the Casella and Berger is an excellent reference at the graduate ... | null | CC BY-SA 2.5 | null | 2010-09-07T17:02:12.730 | 2010-09-07T17:02:12.730 | null | null | 1107 | null |
2452 | 2 | null | 1964 | 3 | null | One place to start would be Silverman's [nearest-neighbor estimator](http://nedwww.ipac.caltech.edu/level5/March02/Silverman/Silver2_5.html), but to add in the weights somehow. (I am not sure exactly what your weights are for here.) The nearest neighbor method can evidently be formulated in terms of distances. I believ... | null | CC BY-SA 2.5 | null | 2010-09-07T21:40:03.113 | 2010-09-07T21:40:03.113 | null | null | 795 | null |
2453 | 2 | null | 4 | 10 | null | I'm going to ask the consultant's dumb question. Why do you want to know if these distributions are different in a statistically significant way?
Is it that the data that you are using are representative samples from populations or processes, and you want to assess the evidence that those populations or processes di... | null | CC BY-SA 2.5 | null | 2010-09-07T23:43:17.103 | 2010-09-07T23:43:17.103 | null | null | 187 | null |
2454 | 2 | null | 2432 | 2 | null | It should be possible to code a solution in R using the non-linear regression function nls, b splines (the bs function in the spline package, for example) and the ifelse function.
| null | CC BY-SA 2.5 | null | 2010-09-07T23:47:38.133 | 2010-09-07T23:47:38.133 | null | null | 187 | null |
2455 | 1 | 2456 | null | 14 | 18969 | Age pyramid looks like this:
I would like to make something similar, namely a 2 barplots (not histograms) with same categories, rotated vertically and extending to both sides as in pyramid.
Is it a simple way to do this in R?
It would be also nice to control the colour o... | How to make age pyramid like plot in R? | CC BY-SA 2.5 | null | 2010-09-08T00:31:39.353 | 2016-07-17T19:59:33.040 | null | null | null | [
"r",
"data-visualization"
] |
2456 | 2 | null | 2455 | 21 | null | You can do this with [the pyramid.plot() function](http://rss.acs.unt.edu/Rdoc/library/plotrix/html/pyramid.plot.html) from the `plotrix` package. Here's an example:
```
library(plotrix)
xy.pop<-c(3.2,3.5,3.6,3.6,3.5,3.5,3.9,3.7,3.9,3.5,3.2,2.8,2.2,1.8,
1.5,1.3,0.7,0.4)
xx.pop<-c(3.2,3.4,3.5,3.5,3.5,3.7,4,3.8,3.9,... | null | CC BY-SA 2.5 | null | 2010-09-08T00:39:29.500 | 2010-09-08T00:45:25.083 | 2010-09-08T00:45:25.083 | 5 | 5 | null |
2457 | 1 | 2463 | null | 27 | 16911 | I would just like someone to confirm my understanding or if I'm missing something.
The definition of a markov process says the next step depends on the current state only and no past states. So, let's say we had a state space of a,b,c,d and we go from a->b->c->d. That means that the transition to d could only depend on... | Markov Process that depends on present state and past state | CC BY-SA 4.0 | null | 2010-09-08T01:57:18.193 | 2020-04-10T14:15:18.650 | 2020-04-10T14:15:18.650 | 268072 | 1208 | [
"markov-process"
] |
2459 | 2 | null | 2427 | 3 | null | if you are going to go the numerical route, a simple observation: the problem appears to be subject to red-black parity (a flea on a red square always moves to a black square, and vice-versa). This can help reduce your problem size by a half (just consider two moves at a time, and only look at fleas on the red squares,... | null | CC BY-SA 2.5 | null | 2010-09-08T02:11:18.783 | 2010-09-08T02:11:18.783 | null | null | 795 | null |
2462 | 2 | null | 2457 | 10 | null | The definition of a markov process says the next step depends on the current state only and no past states.
That is the Markov property and it defines a first order MC, which is very tractable mathematically and quite easy to present/explain. Of course you could have $n^{th}$ order MC (where the next state depends on t... | null | CC BY-SA 3.0 | null | 2010-09-08T02:17:56.713 | 2015-05-02T15:34:42.320 | 2015-05-02T15:34:42.320 | -1 | 603 | null |
2463 | 2 | null | 2457 | 33 | null | Technically, both the processes you describe are markov chains. The difference is that the first one is a first order markov chain whereas the second one is a second order markov chain. And yes, you can transform a second order markov chain to a first order markov chain by a suitable change in state space definition. L... | null | CC BY-SA 2.5 | null | 2010-09-08T02:20:47.227 | 2010-09-08T02:28:25.460 | 2010-09-08T02:28:25.460 | null | null | null |
2464 | 2 | null | 4 | 2 | null | One measure of the difference between two distribution is the "maximum mean discrepancy" criteria, which basically measures the difference between the empirical means of the samples from the two distributions in a Reproducing Kernel Hilbert Space (RKHS). See this paper ["A kernel method for the two sample problem"](htt... | null | CC BY-SA 2.5 | null | 2010-09-08T03:00:19.690 | 2010-09-08T03:00:19.690 | null | null | 881 | null |
2465 | 2 | null | 2446 | 2 | null | Here's how I have understood your question:
- you have two groups of participants
- Five observations per participant
- Based on the five observations, you can extract a single summary statistic (e.g., if the five observations were performance over five time points, the summary statistic might be the slope of the re... | null | CC BY-SA 2.5 | null | 2010-09-08T03:34:47.097 | 2010-09-08T03:34:47.097 | null | null | 183 | null |
2466 | 1 | 2482 | null | 13 | 725 | Say I have a population of 50 million unique things, and I take 10 million samples (with replacement)... The first graph is I've attached shows how many times I sample the same "thing", which is relatively rare as the population is larger than my sample.
However if my population is only 10 million things, and I take 10... | Estimate the size of a population being sampled by the number of repeat observations | CC BY-SA 2.5 | null | 2010-09-08T04:44:53.493 | 2015-10-05T23:56:22.660 | 2015-10-05T23:56:22.660 | 12359 | 1210 | [
"r",
"sampling",
"expectation-maximization"
] |
2467 | 1 | 2473 | null | 5 | 7051 | I have analysed several dimensions in a survey. Each part of the survey represents a theoretical dimension and is analysed with factorial analysis.
I want to use scores from factor analysis to do a classification.
- The first factors represents a large part of the variance. Can I keep only first factor or do I need... | Classification after factor analysis | CC BY-SA 3.0 | null | 2010-09-08T04:49:55.197 | 2016-11-17T12:15:57.527 | 2016-11-17T12:15:57.527 | 29949 | 1154 | [
"classification",
"clustering",
"factor-analysis",
"psychometrics"
] |
2468 | 2 | null | 2466 | 5 | null | You can estimate via a binomial distribution. If there are $n$ draws, with replacement, from $k$ objects (with $k$ unknown), the probability of an object being drawn once in a single draw is $P = \frac{1}{k}$. Think of this as a coinflip now. The probability of exactly $m$ heads (i.e. $m$ duplicates) from $n$ trials is... | null | CC BY-SA 2.5 | null | 2010-09-08T05:03:32.267 | 2010-09-08T16:55:59.350 | 2010-09-08T16:55:59.350 | 795 | 795 | null |
2469 | 1 | 2472 | null | 13 | 32054 | Question:
What is a good method for conducting post hoc tests of differences between group means after adjusting for the effect of a covariate?
Prototypical example:
- Four groups, 30 participants per group (e.g., four different clinical psychology populations)
- Dependent Variable is numeric (e.g., intelligence scor... | Post hoc tests in ANCOVA | CC BY-SA 2.5 | null | 2010-09-08T05:41:48.627 | 2014-02-11T13:41:58.733 | null | null | 183 | [
"anova",
"multiple-comparisons",
"ancova"
] |
2470 | 2 | null | 2469 | 2 | null | Combining simple methods that you can easily access from R and general principles you could use Tukey's HSD simply enough. The error term from the ANCOVA will provide the error term for the confidence intervals.
In R code that would be...
```
#set up some data for an ANCOVA
n <- 30; k <- 4
y <- rnorm(n*k)
a <- factor(... | null | CC BY-SA 3.0 | null | 2010-09-08T07:00:04.273 | 2012-06-09T17:14:56.070 | 2012-06-09T17:14:56.070 | 601 | 601 | null |
2471 | 2 | null | 2466 | 8 | null | This sounds like a form of 'mark and recapture' aka 'capture-recapture', a well-known technique in ecology (and some other fields such as epidemiology). Not my area but [the Wikipedia article on mark and recapture](http://en.wikipedia.org/wiki/Mark_and_recapture) looks reasonable, though your situation is not the one t... | null | CC BY-SA 2.5 | null | 2010-09-08T07:09:54.603 | 2010-09-08T11:29:12.837 | 2010-09-08T11:29:12.837 | 449 | 449 | null |
2472 | 2 | null | 2469 | 13 | null | Multiple testing following ANCOVA, or more generally any GLM, but the comparisons now focus on the adjusted group/treatment or marginal means (i.e. what the scores would be if groups did not differ on the covariate of interest). To my knowledge, Tukey HSD and Scheffé tests are used. Both are quite conservative and will... | null | CC BY-SA 2.5 | null | 2010-09-08T07:17:02.653 | 2010-09-08T07:17:02.653 | null | null | 930 | null |
2473 | 2 | null | 2467 | 4 | null | One solution to your 1. question is to use cross-validation. You compute classification accuracy for models with different number of components and then pick one with the highest classification accuracy. You can check the references below:
[PLS Dimension Reduction for Classification with Microarray Data](http://www.bep... | null | CC BY-SA 2.5 | null | 2010-09-08T07:36:36.987 | 2010-09-08T07:36:36.987 | null | null | 609 | null |
2474 | 2 | null | 2467 | 2 | null | One approach that side-steps cross-validation to determine the optimal number of factors is to use the nonparametric Bayesian approaches for factor analysis. These approaches let the number of factors to be unbounded and eventually decided by the data. See [this paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=... | null | CC BY-SA 2.5 | null | 2010-09-08T07:42:53.730 | 2010-09-08T07:42:53.730 | null | null | 881 | null |
2476 | 1 | 2478 | null | 23 | 10824 | To cluster (text) documents you need a way of measuring similarity between pairs of documents.
Two alternatives are:
- Compare documents as term vectors using Cosine Similarity - and TF/IDF as the weightings for terms.
- Compare each documents probability distribution using f-divergence e.g. Kullback-Leibler divergen... | Measuring Document Similarity | CC BY-SA 2.5 | null | 2010-09-08T10:23:59.423 | 2015-03-25T06:54:55.450 | 2010-09-17T20:20:49.300 | null | 1212 | [
"information-retrieval"
] |
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