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11798 | 2 | null | 11795 | 3 | null | Agree with Mpiktas ..
way 1 to think it:
In generalized way is that
$E(f(x)) = \int f(x)p(x) = \int(f(x)p(x)dx)$ while
$E(f'(x) ) = \int ( \frac{df(x)}{dx}p(x)dx)$..
Thinking it mathematically also a $d/dx$ operator comes inside the integral to cancel some part of the integral effect. It makes sense then to think tha... | null | CC BY-SA 3.0 | null | 2011-06-10T10:10:07.917 | 2011-06-10T17:56:54.840 | 2011-06-10T17:56:54.840 | 2902 | 1763 | null |
11799 | 1 | null | null | 0 | 200 | I want to derive the equation
$\dot P = K'P+PK'^T + Cov(Kv-Gw)$
from the system
$\dot x=Fx+Gw \ \ \ \ \ \ w \sim N(0,Q)$
$z=Hx+v \ \ \ \ \ \ v \sim N(0,R)$
given
$\dot {\hat x}=K'\hat x+Kz$
$K'=F-KH$
$\dot {\hat x}=F\hat x+K(z-H\hat x)$
$d\tilde x/dt = K'\tilde x + Kv -Gw$
$\tilde x = \hat x - x$
$P(t)=E[\tilde x\til... | How to derive $P$ in continuous Kalman filter? | CC BY-SA 3.0 | null | 2011-06-10T10:54:07.903 | 2011-06-12T14:50:20.747 | 2011-06-12T14:50:20.747 | null | 4898 | [
"kalman-filter"
] |
11800 | 1 | 11804 | null | 4 | 2975 | Say we want to perform a logistical regression analysis (although my question pertains to regressions in general) on sports results to determine the effects of various factors on who wins and who loses. We have the background information we want on the teams and players and now just need a random sample.
So we decide ... | How should we convert sports results data to perform a valid logistical regression? | CC BY-SA 3.0 | null | 2011-06-10T12:36:21.297 | 2012-07-07T13:29:50.977 | 2012-07-07T13:29:50.977 | 2970 | 4968 | [
"regression",
"dataset",
"logistic",
"games"
] |
11801 | 2 | null | 11800 | 1 | null | I would be tempted to use a resampling approach, were in each iteration the presentation of each observation is chosen randomly. That way the data for each model is still i.i.d. and the uncertainty due to the presentation of the observations is taken into account by the averaging over the resampled datasest. You can ... | null | CC BY-SA 3.0 | null | 2011-06-10T12:41:39.333 | 2011-06-10T12:41:39.333 | null | null | 887 | null |
11803 | 2 | null | 11800 | 4 | null | Rather using trying logistic regression, I would consider trying the techniques in
>
Dixon, M.J. and S.G. Coles, 1997.
Modelling Association Football Scores
and Inefficiencies in the Football
Betting Market. Applied
Statistics.
In this paper, they use Poisson regression to model football scores. Basically, t... | null | CC BY-SA 3.0 | null | 2011-06-10T12:57:54.083 | 2011-06-10T12:57:54.083 | null | null | 8 | null |
11804 | 2 | null | 11800 | 3 | null | A simple solution is to incorporate the hometown advantage (that is if your data holds this info). This makes it possible to give a definite meaning to your outcome. So if you have that data, it'll likely be a better model and solves your problem: go there!
Right now, your outcome's definition depend on the order, but ... | null | CC BY-SA 3.0 | null | 2011-06-10T13:05:39.187 | 2011-06-10T13:05:39.187 | null | null | 4257 | null |
11806 | 2 | null | 11749 | 2 | null | The exponential distribution might be a good starting point for the waiting time between new posts. This would be equivalent to assuming a Poisson distributed number of posts in a given time period. There are some pretty strong assumptions behind a model like that, but it might make sense for your application.
| null | CC BY-SA 3.0 | null | 2011-06-10T16:21:24.647 | 2011-06-10T16:21:24.647 | null | null | 26 | null |
11807 | 1 | null | null | 11 | 1945 | I'm working with some large data sets using the gbm package in R. Both my predictor matrix and my response vector are pretty sparse (i.e. most entries are zero). I was hoping to build decision trees using an algorithm that takes advantage of this sparseness, as was done [here](http://pubs.acs.org/doi/full/10.1021/ci99... | Are there any libraries available for CART-like methods using sparse predictors & responses? | CC BY-SA 3.0 | null | 2011-06-10T18:50:10.903 | 2014-12-26T23:17:13.293 | 2011-06-14T03:29:05.067 | 4862 | 4862 | [
"r",
"regression",
"machine-learning",
"classification",
"cart"
] |
11808 | 1 | 11819 | null | 12 | 7012 | In a previous question, I inquired about fitting distributions to some non-Gaussian empirical data.
It was suggested to me offline, that I might try the assumption that the data is Gaussian and fit a Kalman filter first. Then, depending on the errors, decide if it is worth developing something fancier. That makes sen... | How to estimate parameters for a Kalman filter | CC BY-SA 3.0 | null | 2011-06-10T19:55:41.443 | 2011-07-11T00:09:56.020 | 2011-06-10T20:09:21.307 | 2116 | 2566 | [
"kalman-filter"
] |
11809 | 1 | null | null | 7 | 408 | When we perform a principal components analysis (PCA) on a multivariate data set we are interested in finding orthogonal components that explain maximal variance in the data set. We can form a biplot of the data using the scores and the loadings, and the locations of the sample points in the biplot are an approximation... | What, if any, dissimilarity is preserved in partial least squares (PLS)? | CC BY-SA 3.0 | null | 2011-06-10T20:03:31.707 | 2011-06-17T09:41:42.640 | null | null | 1390 | [
"pca",
"data-transformation",
"partial-least-squares",
"distance",
"biplot"
] |
11811 | 2 | null | 11808 | 1 | null | The usual method is to use [Maximum Likelihood Estimation](http://en.wikipedia.org/wiki/Maximum_likelihood). Basically, you need a Likelihood function and then run a standard optimizer (such as `optim`) to maximize your Likelihood.
| null | CC BY-SA 3.0 | null | 2011-06-10T20:43:35.040 | 2011-06-10T20:43:35.040 | null | null | 1764 | null |
11812 | 1 | 11814 | null | 26 | 22551 | I am using a ranksum test to compare the median of two samples ($n=120000$) and have found that they are significantly different with: `p = 1.12E-207`. Should I be suspicious of such a small $p$-value or should I attribute it to the high statistical power associated with having a very large sample? Is there any such th... | Sanity check: how low can a p-value go? | CC BY-SA 4.0 | null | 2011-06-10T21:04:04.857 | 2018-05-09T21:03:03.700 | 2018-05-09T19:52:46.167 | 28666 | 4054 | [
"hypothesis-testing",
"p-value",
"sample-size",
"statistical-power"
] |
11814 | 2 | null | 11812 | 32 | null | P-values on standard computers (using IEEE double precision floats) can get as low as approximately $10^{-303}$. These can be legitimately correct calculations when effect sizes are large and/or standard errors are low. Your value, if computed with a T or normal distribution, corresponds to an effect size of about 31... | null | CC BY-SA 3.0 | null | 2011-06-10T21:17:52.253 | 2011-06-10T21:17:52.253 | null | null | 919 | null |
11815 | 1 | null | null | 3 | 744 | I have some data on the number of times each of my machines turned off (due to an error) in a particular time period. There are about 6 different classes of machines being used to construct a total population of 50 machines. I wanted to analyze the stability of the 6 classes of machine relative to each other.
An acqua... | What is heavy hitter analysis? | CC BY-SA 3.0 | null | 2011-06-10T21:27:25.890 | 2011-10-03T23:32:09.577 | null | null | 2164 | [
"clustering",
"multivariate-analysis",
"dataset",
"large-data"
] |
11816 | 2 | null | 11812 | 17 | null | There is nothing suspicious -- extremely low p-values like yours are pretty common when sample sizes are large (as yours is for comparing medians). As whuber mentioned, normally such p-values are reported as being less than some threshold (e.g. <0.001).
One thing to be careful about is that p-values only tells you whet... | null | CC BY-SA 3.0 | null | 2011-06-10T21:57:52.757 | 2011-06-10T21:57:52.757 | null | null | 2973 | null |
11817 | 2 | null | 11762 | 2 | null | The concepts of slowly varying, regular varying and second order regular varying functions are used in extreme value statistics to provide regularity conditions on the behavior of the tail of a distribution function to be able to prove theorems. They can be thought of as smoothness conditions for the tail at infinity. ... | null | CC BY-SA 3.0 | null | 2011-06-10T23:35:23.300 | 2011-06-11T05:43:42.933 | 2011-06-11T05:43:42.933 | 4376 | 4376 | null |
11818 | 2 | null | 11707 | 57 | null | You don't need normality. All you need is that
$$s^2 = \frac{1}{n-1} \sum_{i=1}^n(x_i - \bar{x})^2$$
is an unbiased estimator of the variance $\sigma^2$. Then use that the square root function is strictly concave such that (by a strong form of [Jensen's inequality](http://en.wikipedia.org/wiki/Jensen%27s_inequality#Pr... | null | CC BY-SA 3.0 | null | 2011-06-10T23:54:30.733 | 2011-06-10T23:54:30.733 | null | null | 4376 | null |
11819 | 2 | null | 11808 | 7 | null | Max Welling has a nice [tutorial](http://www.cs.ucl.ac.uk/staff/S.Prince/4C75/WellingKalmanFilter.pdf) that describes all of the Kalman Filtering and Smoothing equations as well as parameter estimation. This may be a good place to start.
| null | CC BY-SA 3.0 | null | 2011-06-10T23:56:35.130 | 2011-06-10T23:56:35.130 | null | null | 1913 | null |
11820 | 2 | null | 11768 | 1 | null | Without some extra context the question is difficult to answer. What is your real-world data? Models (a theoretical distribution for your data) come from applications, not vacuums. There isn't one best way to approximate an unknown distribution in practice. There isn't even one "best". As a general comment, you can get... | null | CC BY-SA 3.0 | null | 2011-06-11T07:13:06.747 | 2011-06-11T07:13:06.747 | null | null | 26 | null |
11821 | 1 | 11828 | null | 6 | 2091 | Assume that you have a regression with a whole set of variables and you know that the residuals are not normal distributed. So you just estimate a regression using OLS to find the best linear fit. For this you disclaim the assumption of normal distributed error terms. After the estimation you have 2 "significant" coeff... | Interpret t-values when not assuming normal distribution of the error term | CC BY-SA 3.0 | null | 2011-06-11T09:55:12.953 | 2011-06-11T20:22:45.043 | 2011-06-11T10:24:46.670 | 2116 | 4496 | [
"regression",
"linear-model"
] |
11822 | 2 | null | 11821 | 1 | null | If the errors are not normally distributed, asymptotic results can be used. Suppose your model is
$$y_i=x_i'\beta+\varepsilon_i$$
where $(y_i,x_i',\varepsilon_i)$, $i=1,...,n$ is an iid sample. Assume
\begin{align*}
E(\varepsilon_i|x_i)&=0 \\
E(\varepsilon_i^2|x_i)&=\sigma^2
\end{align*}
and
$$rank(Ex_ix_i')=K,$$
whe... | null | CC BY-SA 3.0 | null | 2011-06-11T10:36:59.673 | 2011-06-11T20:22:45.043 | 2011-06-11T20:22:45.043 | 2116 | 2116 | null |
11823 | 1 | 11824 | null | 4 | 2884 | I have tested a regression framework's robustness to noise and I have noticed in some cases that adding noise improves the prediction performance and in other cases the performance degrades.
What could be the reasons for this? If there are multiple reasons, how to I determine which is the cause?
Edit:
Some more details... | Why does noisy data result in better prediction performance? | CC BY-SA 3.0 | null | 2011-06-11T11:51:39.733 | 2013-02-09T07:22:06.400 | 2011-06-11T14:16:58.987 | 3052 | 3052 | [
"regression",
"white-noise"
] |
11824 | 2 | null | 11823 | 8 | null | Your description is quite sketchy. Adding noise can (seem to) improve prediction if the method of developing the predictions is overfitting. Likewise if you are overfitting you can improve prediction by deleting progressively more of your data. Depending on your sample size, "improvements" are best demonstrated by ... | null | CC BY-SA 3.0 | null | 2011-06-11T12:14:35.133 | 2011-06-11T12:14:35.133 | null | null | 4253 | null |
11828 | 2 | null | 11821 | 3 | null | If the residuals are not normal (and note that this applies to the theoretical residuals rather than the observed residuals), but not overly skewed or with outliers then the Central Limit Theorem applies and the inference on the slopes (t-tests, confidence intervals) will be approximately correct. The quality of the a... | null | CC BY-SA 3.0 | null | 2011-06-11T14:45:14.073 | 2011-06-11T14:45:14.073 | null | null | 4505 | null |
11829 | 1 | 11851 | null | 4 | 7375 | Is it possible to take the log of an independent variable in a Poisson regression? What to I have to be aware of, when doing so? (The results are getting better, when assuming that the independent variable is with log link.)
| Take the log of an independent variable in a Poisson regression | CC BY-SA 3.0 | null | 2011-06-11T15:10:43.740 | 2011-06-12T19:58:56.200 | 2011-06-11T17:44:07.480 | 919 | 4496 | [
"regression",
"poisson-distribution",
"count-data"
] |
11830 | 2 | null | 11829 | 7 | null | There is no problem with taking the log or other transform of predictor/independent variables in a poisson regression so long as the transformation is possible (no 0's or negative numbers) and makes sense given the science.
| null | CC BY-SA 3.0 | null | 2011-06-11T16:08:46.110 | 2011-06-11T16:08:46.110 | null | null | 4505 | null |
11832 | 1 | 11844 | null | 2 | 1880 | I just need a little bit of a push in the right direction. I'm working my way through Hayashi's Econometrics and hit a snag in section 1.4. Review question 7 asks:
>
Show that, under Assumptions 1.1-1.5,
$Var(s^2|X)=\frac{2\sigma^4}{n-K}$
Hint: If a random variable is
distributed as $\chi^2(m)$, then its
mean is ... | How do you derive the conditional variance for $s^2$, the OLS estimator of $\sigma^2$? | CC BY-SA 3.0 | null | 2011-06-11T17:00:14.327 | 2021-02-07T12:04:26.980 | 2021-02-07T12:04:26.980 | 11887 | 2251 | [
"distributions",
"variance",
"econometrics",
"conditional-probability",
"chi-squared-distribution"
] |
11833 | 1 | null | null | 11 | 626 | I teach an introductory economic geography course. To help my students develop a better understanding of the kinds of countries found in the contemporary world economy and an appreciation of data reduction techniques, I want to construct an assignment that creates a typology of different kinds of countries (e.g, high-i... | Data reduction technique to identify types of countries | CC BY-SA 3.0 | null | 2011-06-11T17:37:52.547 | 2023-01-06T04:45:36.723 | 2011-12-05T21:13:34.090 | 930 | 4980 | [
"pca",
"factor-analysis",
"dimensionality-reduction"
] |
11834 | 2 | null | 11833 | 10 | null | As an exploratory method, PCA is a good first choice for an assignment like this IMO. It'd also be nice for them to get exposed to it; it sounds like many of them won't have seen principal components before.
In terms of data I'd also point you to the World Bank Indicators, which are remarkably complete: [http://data.w... | null | CC BY-SA 3.0 | null | 2011-06-11T17:50:10.890 | 2011-06-11T17:50:10.890 | null | null | 26 | null |
11835 | 1 | null | null | 7 | 15987 | I have consulted two texts on how to calculate to calculate confidence intervals when N is small and the population standard deviation is unknown. There are some differences in the formulas they give and the end result varies depending on which text I follow (although not by a large amount).
Text one says:
- Calculate... | Formula for confidence intervals for small samples and unknown population standard deviation | CC BY-SA 3.0 | null | 2011-06-11T19:33:57.497 | 2018-08-23T13:47:08.133 | 2011-06-11T22:02:25.383 | 4498 | 4498 | [
"confidence-interval"
] |
11836 | 1 | null | null | 4 | 140 | I am trying to determine the probability of a "mixed panel" assignment (i.e., a panel of judges w/at least 1 woman).
Consider the following: A court has a total of 20 judges, 8 of whom are women. Panels of 5 judges are randomly drawn to decide any given case. What is the probability of drawing a panel on which at leas... | Probability of panel assignment | CC BY-SA 3.0 | null | 2011-06-11T20:25:24.340 | 2011-06-12T17:27:43.327 | null | null | 4982 | [
"probability"
] |
11837 | 2 | null | 11833 | 5 | null | I agree with JMS, and PCA seems like a good idea after examining the initial correlations and scatterplots between the variables for each county. [This thread](https://stats.stackexchange.com/q/2691/1036) has some useful suggestions to introduce PCA in non-mathematical terms.
I would also suggest utilizing small multip... | null | CC BY-SA 4.0 | null | 2011-06-11T20:25:39.440 | 2023-01-06T04:45:36.723 | 2023-01-06T04:45:36.723 | 362671 | 1036 | null |
11838 | 2 | null | 11788 | 3 | null | I don't think the Fligner-Killeen test (nor the Brown-Forsythe) test is appropriate since you don't know the median in the published data (if you do have it and simply didn't mention it then never mind).
I wouldn't suggest simulation of the data either unless you're sure the samples follow a specific distribution.
Sinc... | null | CC BY-SA 3.0 | null | 2011-06-11T21:17:49.697 | 2011-06-11T21:40:33.070 | 2011-06-11T21:40:33.070 | 4325 | 4325 | null |
11839 | 2 | null | 11835 | 5 | null | Here are some good notes on standard deviation and the standard error of the mean [here](http://www.cms.murdoch.edu.au/areas/maths/statsnotes/samplestats/stdevmore.html).
The Wackerly et al text computes small sample confidence intervals in section 8.8 (page 430) you can see their formula [here](http://books.google.com... | null | CC BY-SA 4.0 | null | 2011-06-11T22:53:04.550 | 2018-08-23T13:47:08.133 | 2018-08-23T13:47:08.133 | 7290 | 4325 | null |
11840 | 1 | 11882 | null | 5 | 578 | I have been investigating the possibility of using the interval between uncommon events to test for changes in the frequency of such events over time.
As an example, say that the event is breaking a record in some sporting competition. This might occur at most a few times a year, and the data segmentation problem (whet... | Analysis of intervals between events | CC BY-SA 3.0 | null | 2011-06-11T23:49:41.347 | 2011-06-13T19:39:35.467 | 2011-06-12T00:02:30.703 | null | 4983 | [
"time-series"
] |
11841 | 2 | null | 11797 | 5 | null | One usually estimates probabilities with frequencies: [according to Laplace](http://en.wikipedia.org/wiki/Probability_interpretations) (1814),
>
The ratio of this number [of "favorable cases"] to that of all the cases possible is the measure of this probability...
This is justified by an urn model (or "tickets in a ... | null | CC BY-SA 3.0 | null | 2011-06-12T00:19:51.727 | 2011-06-12T00:19:51.727 | null | null | 919 | null |
11842 | 2 | null | 11836 | 6 | null | There is a large and rich branch of mathematics, [combinatorics](http://en.wikipedia.org/wiki/Combinatorics), devoted to solving such problems. The most important step used here is to recognize that "at least one female" is more simply characterized as "not all males." Details follow.
---
The name for the number o... | null | CC BY-SA 3.0 | null | 2011-06-12T01:37:45.843 | 2011-06-12T17:27:43.327 | 2011-06-12T17:27:43.327 | 919 | 919 | null |
11844 | 2 | null | 11832 | 4 | null | Browsing around in the online Google version of the book it seems to me that Assumption 1.5 is the normality assumption. In that case the proof of Proposition 1.3 says that $q|X \sim \chi^2(n-K)$ where $q = (n-K)s^2/\sigma^2$. Thus
$$\begin{array}{rcl}
\text{Var}(s^2|X) & = & \text{Var}(\sigma^2 q/(n-K)|X) \\
& = & ... | null | CC BY-SA 3.0 | null | 2011-06-12T07:30:38.100 | 2011-06-12T07:30:38.100 | null | null | 4376 | null |
11845 | 2 | null | 11807 | 1 | null | Probably there is a little chance for any code which would take advantage of that -- you would rather need to write something on your own.
However, the other option is to transform your data to reduce the size of your data removing redundant information. It is hard to tell how without the information about your data, b... | null | CC BY-SA 3.0 | null | 2011-06-12T08:25:58.907 | 2011-06-12T08:25:58.907 | null | null | null | null |
11846 | 2 | null | 11628 | 20 | null | I think @Jeromy already said the essential so I shall concentrate on measures of reliability.
The Cronbach's alpha is a sample-dependent index used to ascertain a lower-bound of the reliability of an instrument. It is no more than an indicator of variance shared by all items considered in the computation of a scale sco... | null | CC BY-SA 3.0 | null | 2011-06-12T10:23:38.533 | 2011-06-14T15:02:56.760 | 2011-06-14T15:02:56.760 | 930 | 930 | null |
11847 | 1 | null | null | 5 | 2017 | I have the following dataset (triplicate values of 5 independent measurements and duplicate values of a control):
```
Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 C
181.8 58.2 288.9 273.2 290.9 53.9
120.3 116.8 108.9 281.3 446 39.6
86.1 148.... | Help with data analysis of small datasets | CC BY-SA 3.0 | null | 2011-06-12T10:36:50.057 | 2011-06-13T07:39:06.533 | 2011-06-12T14:22:47.513 | 2970 | 4986 | [
"hypothesis-testing"
] |
11848 | 1 | 20475 | null | 9 | 564 | Suppose you have a casino with n poker players. Each player has a win rate - the amount of money he wins or loses per hand. We assume that these win rates are normally distributed with a mean of 0. (We also assume that the players don't pay the casino any money.) Our goal is to estimate the variance V of the distributi... | Estimating the variance of poker win rates | CC BY-SA 3.0 | null | 2011-06-12T14:35:30.007 | 2012-01-02T14:25:17.040 | 2011-06-13T11:41:14.013 | 4988 | 4988 | [
"estimation",
"normal-distribution",
"sampling",
"variance"
] |
11849 | 2 | null | 11847 | 4 | null | There is a time to make formal statistical inferences from sample to population, and a time to simply report on your descriptive results and let your audience make informal inferences--or not, as they see fit. This looks like the latter. With two control values, you are one step away from having no variation on which... | null | CC BY-SA 3.0 | null | 2011-06-12T14:58:31.647 | 2011-06-12T14:58:31.647 | null | null | 2669 | null |
11850 | 1 | null | null | 10 | 15081 | I'm really having trouble finding out how to compare ARIMA and regression models. I understand how to evaluate ARIMA models against each other, and different types of regression models (ie: regression vs dynamic regression with AR errors) against each other, however I cannot see many commonalities between ARIMA model ... | Model comparison between an ARIMA model and a regression model | CC BY-SA 3.0 | null | 2011-06-12T17:03:17.913 | 2011-06-14T05:51:01.037 | null | null | 4989 | [
"arima",
"model-comparison",
"dynamic-regression"
] |
11851 | 2 | null | 11829 | 7 | null | Thanks for the clarification. I agree with @Greg Snow that any transformation should make sense in the context of the problem. Why are you considering a log transform? Have you tried standardizing your predictors?
You want to keep in mind how the transformation changes the assumptions in your model. I'll use $\beta = (... | null | CC BY-SA 3.0 | null | 2011-06-12T17:03:27.230 | 2011-06-12T19:58:56.200 | 2011-06-12T19:58:56.200 | 26 | 26 | null |
11852 | 2 | null | 11847 | 7 | null | One could use a non-parametric version of ANOVA: this is called the [Kruskal-Wallis](http://en.wikipedia.org/wiki/Kruskal%E2%80%93Wallis_one-way_analysis_of_variance) test. It is based on ranking all 17 results and computing the mean ranks within each group. The mean rank of 2.0 among the controls is obviously smalle... | null | CC BY-SA 3.0 | null | 2011-06-12T19:03:21.303 | 2011-06-12T19:03:21.303 | null | null | 919 | null |
11853 | 2 | null | 11850 | 6 | null | If we exclude the ARIMAX models, which are ARIMA with regressors, ARIMA and regression models are models with different approaches. ARIMA tries to model the variable only with information about the past values of the same variable. Regression models on the other hand model the variable with the values of other variable... | null | CC BY-SA 3.0 | null | 2011-06-12T19:52:14.573 | 2011-06-14T05:51:01.037 | 2011-06-14T05:51:01.037 | 2116 | 2116 | null |
11854 | 2 | null | 11850 | 1 | null | You could use the MSE/AIC/BIC of the arima model and compare it to the MSE/AIC/BIC of the regression model. Just make sure that the number of fitted values are the same otherwise you might be making a mistake. For example if the ARIMA model has a lag structure of say sp+p ( a seasonal difference of order sp and an aut... | null | CC BY-SA 3.0 | null | 2011-06-12T20:50:07.973 | 2011-06-12T22:09:59.310 | 2011-06-12T22:09:59.310 | 3382 | 3382 | null |
11855 | 1 | null | null | 0 | 805 | I have 5 checklists with different perfect scores. Say, I have checklist A-E with their corresponding perfect scores:
A = 24
B = 17
C = 38
D = 41
E = 25
Each item in all the checklists are equivalent to 1 point.
I want to compare one item from one checklist to another item in other checklists.
How could I make the weig... | How to equalize the weight of each item in multiple checklists? | CC BY-SA 3.0 | null | 2011-06-13T01:19:21.967 | 2011-10-25T17:04:54.573 | 2011-06-27T11:44:04.527 | -1 | 4992 | [
"normalization"
] |
11856 | 1 | 11873 | null | 24 | 43495 | SPSS provides the output "confidence interval of the difference means." I have read in some places that it means "95 times out of 100, our sample mean difference will be between between these bounds" I find this unclear. Can anyone suggest clearer wording to explain "confidence interval of the difference in means"? Thi... | How to interpret confidence interval of the difference in means in one sample T-test? | CC BY-SA 3.0 | null | 2011-06-13T02:47:36.607 | 2013-02-18T20:38:47.020 | 2011-06-15T04:25:29.470 | 4498 | 4498 | [
"confidence-interval"
] |
11857 | 2 | null | 11856 | 5 | null | From a pedantic technical viewpoint, I personally don't think there is a "clear wording" of the interpretation of confidence intervals.
I would interpret a confidence interval as: there is a 95% probability that the 95% confidence interval covers the true mean difference
An interpretation of this is that if we were to ... | null | CC BY-SA 3.0 | null | 2011-06-13T04:50:18.203 | 2011-06-18T12:24:46.743 | 2011-06-18T12:24:46.743 | 2392 | 2392 | null |
11858 | 2 | null | 11856 | -2 | null | My Interpretation: If you conduct the experiment N times ( where N tends to infinity) then out of these large number of experiments 95% of the experiments will have confidence intervals which lie within these 95% limits. More clearly, lets say those limits are "a" and "b" then 95 out of 100 times your sample mean diffe... | null | CC BY-SA 3.0 | null | 2011-06-13T05:16:57.427 | 2011-06-13T05:16:57.427 | null | null | 1763 | null |
11859 | 1 | 11866 | null | 108 | 110019 | What is the difference between a multiclass problem and a multilabel problem?
| What is the difference between Multiclass and Multilabel Problem | CC BY-SA 3.0 | null | 2011-06-13T05:35:36.353 | 2022-01-19T05:50:29.577 | 2016-08-28T13:17:12.573 | 12359 | 4290 | [
"classification",
"clustering",
"terminology",
"multi-class",
"multilabel"
] |
11861 | 2 | null | 11850 | 1 | null | Cross validation would probably be good here. To do this you split your data set into 2 parts. You use the first part to fit both models, and then use the fitted model to predict the second part. This can be justified as an approximation to a fully Bayesian approach to model selection. We have the likelihood of a m... | null | CC BY-SA 3.0 | null | 2011-06-13T05:58:30.507 | 2011-06-13T05:58:30.507 | null | null | 2392 | null |
11865 | 2 | null | 11847 | 2 | null | This is an interesting data set. It seems like a good idea to follow @whuber's advice and do the analysis on the log scale. However, there is more than one hypothesis here. For you could have the hypothesis
$$H_{0}:\text{samples 1-5 have the same mean and variance on the log scale,}$$
$$\text{and this is different f... | null | CC BY-SA 3.0 | null | 2011-06-13T07:39:06.533 | 2011-06-13T07:39:06.533 | null | null | 2392 | null |
11866 | 2 | null | 11859 | 89 | null | I suspect the difference is that in multi-class problems the classes are mutually exclusive, whereas for multi-label problems each label represents a different classification task, but the tasks are somehow related (so there is a benefit in tackling them together rather than separately). For example, in the famous lep... | null | CC BY-SA 3.0 | null | 2011-06-13T09:50:21.037 | 2011-06-13T10:00:16.603 | 2011-06-13T10:00:16.603 | 887 | 887 | null |
11867 | 1 | null | null | 5 | 2342 | How can I compare the following mutual information values ? I'm just wondering what's the most appropriate way to display them in my report table.
I'm computing them with this formula 
where e and c are clusters and the intersection is the number of elements in co... | How can I compare the following mutual information values? | CC BY-SA 3.0 | null | 2011-06-13T12:55:06.773 | 2012-09-04T10:25:02.923 | 2011-06-13T18:13:35.187 | null | 3941 | [
"clustering",
"mutual-information"
] |
11868 | 1 | 11874 | null | 5 | 963 | Assume that you have a Poisson model with overdispersion. Besides negative binomial models, what are other appropriate count-data modeling regression techniques?
| What count-data models to choose besides negative binomial model when overdispersion occurs? | CC BY-SA 3.0 | null | 2011-06-13T13:04:46.723 | 2011-08-28T08:51:46.007 | 2011-06-13T13:15:19.347 | 2116 | 4496 | [
"regression",
"poisson-distribution",
"count-data",
"negative-binomial-distribution"
] |
11869 | 1 | 11884 | null | 5 | 9350 | I have a broad question about sliding window validation. Specifically, I am looking at using Rapid Miner to predict future values of a financial series using "lagged" values of that series and other covariates. I have been experimenting with the windowing operator in this software and lagging the values to prepare for ... | Sliding window validation for time series | CC BY-SA 3.0 | null | 2011-06-13T13:09:15.977 | 2011-06-14T17:40:26.777 | 2011-06-13T18:13:55.420 | null | 2040 | [
"time-series",
"data-mining",
"rapidminer"
] |
11871 | 1 | 11880 | null | 5 | 844 | The "[Introductory Statistics with R](http://www.springer.com/statistics/computanional+statistics/book/978-0-387-79053-4)" book contains a section that deals with correlations (section 6.4 in the second edition). The book shows Pearson, Spearman and Kendall correlation coefficients computed on the `blood.glucose` and `... | Interpreting p-values associated with correlation measurements | CC BY-SA 3.0 | null | 2011-06-13T13:31:51.697 | 2011-06-13T18:36:35.977 | null | null | 1496 | [
"correlation",
"statistical-significance",
"references",
"mathematical-statistics"
] |
11872 | 1 | null | null | 16 | 7712 | I would be interested in finding ways in R for efficiently updating a linear model when an observation or a predictor is added. biglm has an updating capability when adding observations, but my data are small enough to reside in memory (although I do have a large number of instances to update). There are ways to do thi... | Updating linear regression efficiently when adding observations and/or predictors in R | CC BY-SA 4.0 | null | 2011-06-13T14:01:30.360 | 2021-01-12T12:05:48.820 | 2021-01-12T12:05:48.820 | 28436 | 30 | [
"r",
"regression",
"computational-statistics",
"linear-model"
] |
11873 | 2 | null | 11856 | 14 | null | This is not an easy thing, even for respected statisticians. Look at one recent attempt by [Nate Silver](http://fivethirtyeight.blogs.nytimes.com/2010/09/29/the-uncanny-accuracy-of-polling-averages-part-i-why-you-cant-trust-your-gut/):
>
... if I asked you to tell me how often your commute takes 10 minutes longer tha... | null | CC BY-SA 3.0 | null | 2011-06-13T14:14:13.190 | 2011-06-13T21:19:33.360 | 2011-06-13T21:19:33.360 | 919 | 919 | null |
11874 | 2 | null | 11868 | 6 | null | If you're willing to impose an upper bound on your counts, the beta-binomial works well. Its story is that the binomial probability for each of your count responses is drawn from a beta distribution, which is bounded between zero and one and is used to model binomial probabilities in a Bayesian context. There is also a... | null | CC BY-SA 3.0 | null | 2011-06-13T14:15:34.577 | 2011-06-13T14:15:34.577 | null | null | 4862 | null |
11875 | 2 | null | 11872 | 3 | null | Why don't you try the update capability of the linear model object
```
update.lm( lm.obj, formula, data, weights, subset, na.action)
```
Take a look at this links
- For a general explanation of the update function:
[http://stat.ethz.ch/R-manual/R-devel/library/stats/html/update.html](http://stat.ethz.ch/R-manual/R-... | null | CC BY-SA 3.0 | null | 2011-06-13T14:44:34.367 | 2011-06-13T14:44:34.367 | null | null | 2902 | null |
11876 | 1 | 11877 | null | 7 | 13199 | I'm estimating some count data. I have counts for say $m=100$ individuals. Unfortunately when using the Poisson regression overdispersion occurs. So I was thinking to fit a negbin model. But this is not appropriate in my case. So I assume that I can not fit a Poisson regression, because the way the Poisson distribution... | Fitting a beta-binomial model in the case of overdispersion in R | CC BY-SA 3.0 | null | 2011-06-13T14:44:40.063 | 2011-06-14T06:06:46.793 | 2011-06-14T06:06:46.793 | 2116 | 4496 | [
"r",
"regression",
"count-data",
"beta-binomial-distribution",
"overdispersion"
] |
11877 | 2 | null | 11876 | 8 | null | Beta binomial does sound like a good choice. Ben Bolker has a nice example of how to do it with his bbmle package [here](http://cran.r-project.org/web/packages/bbmle/vignettes/mle2.pdf). I believe his book has more, some kind of tadpole-related example. You can get preprints of the book [here](http://www.math.mcmaster.... | null | CC BY-SA 3.0 | null | 2011-06-13T15:57:52.590 | 2011-06-13T15:57:52.590 | null | null | 4862 | null |
11878 | 1 | null | null | 13 | 780 | I'm trying to fit a hierarchical model using jags, and the rjags package. My outcome variable is y, which is a sequence of bernoulli trials. I have 38 human subjects which are performing under two categories: P and M. Based on my analysis, every speaker has a probability of success in category P of $\theta_p$ and a pro... | MCMC converging to a single value? | CC BY-SA 3.0 | null | 2011-06-13T16:41:55.423 | 2015-10-29T07:55:17.897 | 2011-06-13T17:17:36.207 | 287 | 287 | [
"markov-chain-montecarlo",
"multilevel-analysis",
"jags"
] |
11879 | 2 | null | 11018 | 4 | null | Lucky for me, Andrew Gelman decided to discuss [this topic](http://www.stat.columbia.edu/~cook/movabletype/archives/2011/06/sampling_design.html) on his blog last week! There I found the following books recommended in the comments:
[Applied Survey Data Analysis](http://rads.stackoverflow.com/amzn/click/1420080660) by ... | null | CC BY-SA 3.0 | null | 2011-06-13T16:44:57.480 | 2011-07-15T19:58:50.013 | 2011-07-15T19:58:50.013 | 3748 | 3748 | null |
11880 | 2 | null | 11871 | 5 | null | One explanation is that outliers, even mild ones can affect the results in a pearson correlation. If the outlier is a legitimate point (not a typo or other error) then it should increase the significance of the correlation (as you see), but will not change much in the other 2, so it is easy for the pearson correlation... | null | CC BY-SA 3.0 | null | 2011-06-13T16:51:20.857 | 2011-06-13T16:51:20.857 | null | null | 4505 | null |
11881 | 2 | null | 11871 | 5 | null | @Greg Snow is on the money about your first question.
In regard to your second, comparing the two tests is misleading since two hypotheses are different even though the scientific question is (ostensibly) the same. This is a case where it's really important to be explicit about what hypothesis test you're using.
To be ... | null | CC BY-SA 3.0 | null | 2011-06-13T18:36:35.977 | 2011-06-13T18:36:35.977 | null | null | 26 | null |
11882 | 2 | null | 11840 | 3 | null | This is a fairly common problem but very tricky.It can be found by googling Intermittent Demand , Sparse Data Analysis and some other "names". We deal with time series data where there are two random variables ; The interval and the actual demand at each point.We have not experimented with cases like yours where the de... | null | CC BY-SA 3.0 | null | 2011-06-13T19:10:52.507 | 2011-06-13T19:39:35.467 | 2011-06-13T19:39:35.467 | 3382 | 3382 | null |
11883 | 1 | 22670 | null | 9 | 1345 | I am analyzing data from two surveys that I merged together:
- School staff survey, for years 2005-06 and 2007-08
- School students survey, for years 2005-06 through 2008-09
For both of these data sets, I have observations (at the student or staff level) from 3 different school districts, each having representative... | On the use of weighted correlations in aggregated survey data | CC BY-SA 3.0 | null | 2011-06-13T19:48:52.427 | 2012-02-12T08:53:40.407 | 2012-02-12T08:53:40.407 | 930 | 3309 | [
"correlation",
"survey",
"multilevel-analysis"
] |
11884 | 2 | null | 11869 | 7 | null | Your understanding about sliding window analysis is generally correct. You may find it helpful to separate the model validation process from the actual forecasting. In model validation, you use $k$ instances to train a model that predicts "one step" forward. Make sure each of your $k$ instances uses only information av... | null | CC BY-SA 3.0 | null | 2011-06-13T20:56:19.137 | 2011-06-14T17:40:26.777 | 2011-06-14T17:40:26.777 | 4942 | 4942 | null |
11885 | 1 | null | null | 1 | 2060 | In the [card game Pitch](http://en.wikipedia.org/wiki/Pitch_%28card_game%29) how do I calculate when my opponents have 12 cards out of 52 if they have the Ace, King or Queen of a suit? I assume there is only about a 22% chance of the Ace being in their hand, but I don't know how to add the other two cards.
I want my J... | Odds for high in the card game pitch | CC BY-SA 3.0 | null | 2011-06-13T23:18:32.027 | 2019-02-07T15:33:39.777 | 2018-09-03T19:44:12.330 | 22311 | 5001 | [
"probability",
"games"
] |
11886 | 2 | null | 11885 | 2 | null | You have 6 cards (out of 52) and you want to know if another set of 12 (from the same 52) have at least one of three particular cards which you do not have. It is easier to work out the probability they do not have any of the three, which is
$$\frac{43}{46}\times\frac{42}{45}\times\frac{41}{44}\times\frac{40}{43}\tim... | null | CC BY-SA 3.0 | null | 2011-06-14T00:08:32.417 | 2011-06-14T00:18:10.447 | 2011-06-14T00:18:10.447 | 2958 | 2958 | null |
11887 | 1 | 15400 | null | 5 | 1941 | I collect blood from one human (donor), separate leukocytes and put $2\times10^6$ of them per each well of five:
- well 1: $2\times10^6$ Lk (leukocytes)
- well 2: $2\times10^6$ Lk
- well 3: $2\times10^6$ Lk
- well 4: $2\times10^6$ Lk
- well 5: $2\times10^6$ Lk
(Lk consists of a mix of different sets of... | What experimental design is this? | CC BY-SA 3.0 | null | 2011-06-14T01:07:19.033 | 2016-04-06T17:46:20.330 | 2016-04-06T17:46:20.330 | 2798 | 5003 | [
"anova",
"repeated-measures",
"experiment-design"
] |
11888 | 1 | 11891 | null | 3 | 583 | Why is it so successful for the lasso, though for most other problems standard Quasi-Newton approaches seem to be preferred? I sort of have this vague geometric idea that it might have to do with the shape of the $L_1$ ball, but haven't really been able to formalize it.
| The effectiveness of coordinate ascent | CC BY-SA 3.0 | null | 2011-06-14T03:47:37.230 | 2011-06-14T06:47:25.070 | 2011-06-14T05:47:14.330 | 2116 | 5007 | [
"optimization",
"lasso"
] |
11890 | 2 | null | 11888 | 2 | null | Trevor Hastie has some ideas starting on page 19 [here.](http://www.stanford.edu/~hastie/TALKS/glmnet.pdf) A big part of the answer has to do with being able to simply ignore large portions of the data during updates, either because things are sparse or because we're only looking at one variable at a time or because s... | null | CC BY-SA 3.0 | null | 2011-06-14T05:34:39.483 | 2011-06-14T05:34:39.483 | null | null | 4862 | null |
11891 | 2 | null | 11888 | 3 | null | There are several things to keep in mind here. Lasso for linear regression is optimization of a quadratic function with an $\ell_1$-norm penalty term. The latter is non-smooth, and one can put the problem into the context of quadratic optimization with linear constraints. General purpose solvers turn out to be less tha... | null | CC BY-SA 3.0 | null | 2011-06-14T06:47:25.070 | 2011-06-14T06:47:25.070 | null | null | 4376 | null |
11892 | 1 | null | null | 4 | 1000 | I have a dataset with 4025 participants across two time points. I have scored them on a three-point categorical variable (`Unlikely, Possible, Probable`) at each time point. I would like to visualize the various patterns of change (e.g. going from `Unlikely` at T1 to `Possible` at T2 or going from `Possible` at T1 to `... | Plotting changes in a three-valued ordinal variable across two time points using R | CC BY-SA 3.0 | null | 2011-06-14T07:26:45.253 | 2011-06-20T08:34:35.890 | 2017-04-13T12:44:29.013 | -1 | 913 | [
"r",
"data-visualization",
"categorical-data",
"ggplot2"
] |
11893 | 2 | null | 10185 | 5 | null | I ran your model with rjags package. I have not provided any initial value since JAGS can produce them for you. You can see the error below
```
> m <- jags.model(file = "model.txt", n.chain = 1)
Compiling model graph
Resolving undeclared variables
Deleting model
Error in jags.model(file = "model.txt", n.chain = 1) ... | null | CC BY-SA 3.0 | null | 2011-06-14T07:47:41.560 | 2011-06-14T07:47:41.560 | null | null | 4618 | null |
11894 | 2 | null | 11892 | 2 | null | One of your options is to use a sunflowerplot for each combination. This is available from a default installation of R. For some datasets, a sunflowerplot is not particularly clear, so I have used colour coding instead.
If R is your thing, the code below should get you going with the colour coding (just copy-and-pasted... | null | CC BY-SA 3.0 | null | 2011-06-14T07:55:35.317 | 2011-06-14T07:55:35.317 | null | null | 4257 | null |
11895 | 1 | null | null | 2 | 271 | In the extreme case where all of the components of an $M$-variate observation are pairwise independent from each other, a multivariate normal distribution can be decomposed into the product of $M$ univariate normal distributions. For example, $$p \left( X_{1},X_{2},X_{3};[\mu_{1},\mu_{2},\mu_{3}]^{T},\left[\begin{array... | Is there a covariance MLE which takes into account independence relationships? | CC BY-SA 3.0 | null | 2011-06-14T08:29:35.377 | 2011-06-15T15:04:15.530 | 2011-06-15T15:04:15.530 | 3691 | 3691 | [
"normal-distribution",
"maximum-likelihood",
"independence"
] |
11896 | 1 | null | null | 3 | 1114 | Short question: What happens to the beta-binomial distribution, when n increases to infinity? Is there a count distribution arising like it's for the classical binomial distribution?
| What happens with the beta-binomial distribution, when n approaches infinity? | CC BY-SA 3.0 | null | 2011-06-14T10:01:51.670 | 2011-06-14T13:46:37.730 | 2011-06-14T12:40:27.723 | 2116 | 4496 | [
"distributions",
"mathematical-statistics",
"beta-binomial-distribution",
"proof"
] |
11897 | 1 | null | null | 11 | 255 | You may have heard about the recent enterohaemorrhagic E. coli
([EHEC](http://en.wikipedia.org/wiki/EHEC))
[outbreak in Germany](http://en.wikipedia.org/wiki/2011_E._coli_O104%3aH4_outbreak).
What questions would a statistician ask about EHEC analysis ?
I'm thinking of Q+As
between reporters / public officials ↔ non-e... | What questions would a statistician ask about analysis of E. coli outbreak? | CC BY-SA 3.0 | null | 2011-06-14T10:53:01.613 | 2011-06-21T18:10:47.383 | 2011-06-20T10:25:58.717 | 557 | 557 | [
"data-visualization",
"teaching"
] |
11898 | 1 | 11902 | null | 4 | 1083 | I have calculated the repeatability of individuals' responses to a stimulus using the methodology of [Lessells & Boag (1987) Auk 104:116](http://www.univet.hu/users/jkis/education/Kutatastervezes/Lessells_Boag_Auk_87_Unrepeatable_repeatabilities_-_a_common_mistake.pdf), where repeatability r = among-groups variance com... | Confidence intervals for repeatability | CC BY-SA 3.0 | null | 2011-06-14T10:59:48.373 | 2011-06-14T14:43:35.997 | null | null | 266 | [
"confidence-interval",
"repeated-measures",
"repeatability"
] |
11899 | 1 | 11911 | null | 9 | 17912 | What approaches are there to perform FA on data that is clearly ordinal (or nominal for that matter) by nature? Should the data be transformed our are there readily available `R` packages that can handle this format? What if the data is of a mixed nature, containing both numerical, ordinal and nominal data?
The data is... | Factor analysis on mixed (continuous/ordinal/nominal) data? | CC BY-SA 3.0 | null | 2011-06-14T13:23:48.300 | 2019-05-15T16:16:56.790 | 2011-06-15T12:12:28.120 | 3401 | 3401 | [
"r",
"factor-analysis",
"categorical-data",
"ordinal-data"
] |
11900 | 2 | null | 11896 | 2 | null | Consider the [urn model](http://en.wikipedia.org/wiki/Beta-binomial_distribution#Beta-binomial_as_an_urn_model) for the beta-binomial:
>
... imagine an urn containing α red balls and β black balls, where random draws are made. If a red ball is observed, then two red balls are returned to the urn. Likewise, if a black... | null | CC BY-SA 3.0 | null | 2011-06-14T13:46:37.730 | 2011-06-14T13:46:37.730 | null | null | 919 | null |
11901 | 2 | null | 11872 | 7 | null | If the algorithm you are looking for is indeed something like [Applied Statistics 274, 1992, Vol 41(2)](https://www.jstor.org/stable/2347583) then you could just use [biglm](http://cran.r-project.org/package=biglm) as it does not require you to keep your data in a file.
| null | CC BY-SA 4.0 | null | 2011-06-14T13:55:57.360 | 2021-01-12T12:05:40.433 | 2021-01-12T12:05:40.433 | 28436 | 334 | null |
11902 | 2 | null | 11898 | 3 | null | I would go for bootstrap to compute 95% CIs. This is what is generally done with coefficient of heritability or intraclass correlation. (I found no other indication in Falconer's book.) There is an example in the [gap](http://cran.r-project.org/web/packages/gap/index.html) package of an handmade bootstrap (see `help(h2... | null | CC BY-SA 3.0 | null | 2011-06-14T14:43:35.997 | 2011-06-14T14:43:35.997 | null | null | 930 | null |
11903 | 1 | null | null | 5 | 135 |
## Background
Conventional approaches to fitting a priori models to observed data seek to find those model parameters that maximize the likelihood of the data. For more complicated models, this typically necessitates an iterative search across a reasonable parameter space, computing the likelihood of the data given ... | Estimation by future likelihood maximization | CC BY-SA 3.0 | null | 2011-06-14T16:05:00.390 | 2011-06-17T05:55:52.093 | 2011-06-14T21:59:16.870 | null | 364 | [
"model-selection",
"maximum-likelihood",
"cross-validation"
] |
11904 | 2 | null | 2272 | 12 | null | Always fun to engage in a bit of philosophy. I quite like Keith's response, however I would say that he is taking the position of "Mr forgetful Bayesia". The bad coverage when type B and type C can only come about if (s)he applies the same probability distribution at every trial, and refuses to update his(her) prior.... | null | CC BY-SA 3.0 | null | 2011-06-14T16:37:10.570 | 2011-06-14T16:37:10.570 | null | null | 2392 | null |
11906 | 1 | null | null | 3 | 216 | I am tasked with analyzing data to find "triggers" to an event. Specifically, this is transaction data from a bank (e.g., checking account daily balances, daily over draft fees, daily number of checks cleared etc) and the event of interest is the checking account being closed by the customer. It sounds like I am really... | Use of survival analysis for trigger mining | CC BY-SA 3.0 | null | 2011-06-14T19:12:15.087 | 2011-09-24T01:27:58.047 | 2020-06-11T14:32:37.003 | -1 | 2040 | [
"data-mining",
"survival",
"cart"
] |
11907 | 1 | null | null | 3 | 513 | The "exclusive or" function has a long and arduous history in the AI/machine learning communities. From my understanding of "association rule learning", xor would appear to be a problem for this type of learning. That is, suppose we have the following data:
```
A B C
0 0 0
0 1 1
1 0 1
1 1 ... | What is the association rule learning approach to the logical XOR problem? | CC BY-SA 3.0 | null | 2011-06-14T19:33:06.610 | 2013-05-27T09:06:46.213 | 2011-06-14T20:35:46.947 | 930 | 2485 | [
"machine-learning",
"data-mining"
] |
11908 | 2 | null | 11903 | 2 | null | The idea looks related to using the bootstrap, see Section 7.11 in [ESL](http://www-stat.stanford.edu/~tibs/ElemStatLearn/), as an alternative to cross-validation. The bootstrap also resamples subsets for training and uses the original data for estimation of the generalization error (evaluation of the model). The diffe... | null | CC BY-SA 3.0 | null | 2011-06-14T20:49:08.887 | 2011-06-17T05:55:52.093 | 2011-06-17T05:55:52.093 | 4376 | 4376 | null |
11909 | 1 | null | null | 5 | 620 | It's hard to think of a more eloquent way of phrasing this question - I'm basically wondering if a classifier trained on data where examples of some of the classes are infrequent/rare would be a bad model? I'm mainly interested in decision trees (C4.5).
I think the answer is no, but that you will get a high error, bec... | Do infrequent examples screw up classifiers? If so, when is it okay to remove the infrequent examples from the data? | CC BY-SA 3.0 | 0 | 2011-06-14T22:15:46.340 | 2011-09-13T00:51:09.427 | null | null | 3984 | [
"machine-learning",
"classification",
"cart"
] |
11910 | 2 | null | 11679 | 1 | null | After a few days, I decided it may be best to use an alternative method. What I did was sample the data such that it reflected the reported distributions in the population. I repeated this a number of times, each time randomly sampling in appropriate proportions, and took the average performance on the classifier.
I co... | null | CC BY-SA 3.0 | null | 2011-06-14T22:27:19.433 | 2011-06-14T22:27:19.433 | null | null | 4673 | null |
11911 | 2 | null | 11899 | 3 | null | Particularly if you have nominal indicators along with the ordinal & continuous ones, this is probably a good candidate for latent class factor analysis.
Take a look at this -- [http://web.archive.org/web/20130502181643/http://www.statisticalinnovations.com/articles/bozdogan.pdf](http://web.archive.org/web/201305021816... | null | CC BY-SA 3.0 | null | 2011-06-14T22:34:41.453 | 2015-12-13T19:26:16.700 | 2015-12-13T19:26:16.700 | 22228 | 11954 | null |
11912 | 2 | null | 11807 | 1 | null | Have you looked at the [caret package](http://caret.r-forge.r-project.org/Classification_and_Regression_Training.html) in R? It provides an interface that makes it easier to use a variety of models, including some for recursive partitioning such as [rpart](http://cran.r-project.org/web/packages/rpart/index.html), [ctre... | null | CC BY-SA 3.0 | null | 2011-06-14T22:39:44.687 | 2011-06-14T22:39:44.687 | null | null | 3984 | null |
11913 | 2 | null | 11909 | 3 | null | By 'infrequent example', i assume you mean that the class label occurs infrequently, (i.e., points to which you've assigned a class label occurs with very low frequency in your data). So hiding them from your classifier in essence removes any opportunity your classifier would have had to learn to assign that class lab... | null | CC BY-SA 3.0 | null | 2011-06-14T23:29:16.463 | 2011-06-14T23:29:16.463 | null | null | 438 | null |
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