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11688 | 2 | null | 2717 | 0 | null | Co-clustering is one of the answers I think. But Im not expert here. Co-clustring isn't newborn method, so you can find some algos in R, wiki shows that concepts in good way. Another method that isnt menthioned is graph partitioning (but I see that graph wouldnt be sparse,graph partitioning would be useful if your matr... | null | CC BY-SA 3.0 | null | 2011-06-07T22:55:36.453 | 2011-06-07T22:55:36.453 | null | null | 4908 | null |
11689 | 1 | 15248 | null | 4 | 3979 | I'm reading about the Linear Discriminant Analysis by Fisher and I have a couple of questions about its usage.
- If you have k>2 classes in a two-dimensional space you find k−1 vectors that you need to use to project the sample data. Is it possible that one sample is closer to different means along different vectors?
... | Usage of LDA with more than two classes | CC BY-SA 3.0 | null | 2011-06-07T23:05:30.967 | 2011-09-06T20:22:54.613 | 2011-09-06T17:01:18.673 | 223 | 4889 | [
"machine-learning",
"clustering",
"classification",
"discriminant-analysis"
] |
11690 | 2 | null | 11531 | 0 | null | Mayby try some "moving deletator" - in window of p observations compute standard deviation and then delete obs for which absolute difference to previous observation is x times bigger then standard deviation in that window. But this method could don't work with densely packed outliers (one after another) which is showed... | null | CC BY-SA 3.0 | null | 2011-06-07T23:25:43.797 | 2011-06-07T23:25:43.797 | null | null | 4908 | null |
11691 | 1 | 11702 | null | 88 | 78312 | How would you know if your (high dimensional) data exhibits enough clustering so that results from kmeans or other clustering algorithm is actually meaningful?
For k-means algorithm in particular, how much of a reduction in within-cluster variance should there be for the actual clustering results to be meaningful (and ... | How to tell if data is "clustered" enough for clustering algorithms to produce meaningful results? | CC BY-SA 3.0 | null | 2011-06-08T00:04:43.590 | 2015-02-09T02:07:12.587 | null | null | 2973 | [
"clustering",
"k-means"
] |
11692 | 2 | null | 11687 | 2 | null | If you want to model the data and the dependent categorical variable has no ordering (nominal) then you must use a multinomial logit model. If the dependent variable does have an ordering (ordinal) then you can use a cumulative logit model (proportional odds model).
For me personally, I find the results much easier to... | null | CC BY-SA 3.0 | null | 2011-06-08T00:49:52.040 | 2011-06-08T02:59:47.810 | 2011-06-08T02:59:47.810 | 2310 | 2310 | null |
11693 | 2 | null | 11687 | 4 | null | If you ignore the ordered nature of the variables the appropriate methods will still provide correct analysis, but the advantage of using methods for ordered data is they provide greater information about the order and magnitude of significant variables.
| null | CC BY-SA 3.0 | null | 2011-06-08T00:55:19.533 | 2011-06-08T00:55:19.533 | null | null | 4927 | null |
11694 | 2 | null | 11691 | 6 | null | I have just started using clustering algorithms recently, so hopefully someone more knowledgeable can provide a more complete answer, but here are some thoughts:
'Meaningful', as I'm sure you're aware, is very subjective. So whether the clustering is good enough is completely dependent upon why you need to cluster in ... | null | CC BY-SA 3.0 | null | 2011-06-08T02:08:11.113 | 2011-06-08T02:08:11.113 | null | null | 1977 | null |
11695 | 1 | null | null | 2 | 365 | What is power in logistic regression? Is it the ability of the test to reject the null hypothesis when it is actually false?
Second, if you're trying to maximize your statistical power when doing a logistic regression, is it better to use predictor values that are only high or low or a range of predictor values?
| Logistic regression - power and predictor values | CC BY-SA 3.0 | null | 2011-06-08T03:37:16.950 | 2011-06-08T05:40:59.183 | 2011-06-08T03:51:10.397 | 183 | 4928 | [
"logistic",
"statistical-power"
] |
11696 | 1 | null | null | 0 | 1386 | Can you please suggest me a good model-based learning algorithm to recommend items to the user? Is there any open source implementation available on model based learning algorithm? I am sure Apache Mahout doesn't implemented any model based learning algorithms.
| Model-based learning algorithm for recommendation engine | CC BY-SA 3.0 | null | 2011-06-08T05:07:14.653 | 2017-10-24T14:12:32.040 | 2011-06-08T08:41:15.707 | null | 4665 | [
"machine-learning",
"recommender-system"
] |
11697 | 2 | null | 11695 | 2 | null | Power by definition is what you wrote. The ability to reject a false null hypothesis. That is how assertive a model is to say that a predictor x has something to do with the dependent variable y. Power is a probability so closer is it to 1, better it is.
For the second question, there is no fixed answer to this quest... | null | CC BY-SA 3.0 | null | 2011-06-08T05:40:59.183 | 2011-06-08T05:40:59.183 | null | null | 1763 | null |
11698 | 2 | null | 11691 | 10 | null | Surely, the ability to visually discern the clusters in a plotable number of dimensions is a doubtful criterion for the usefulness of a clustering algorithm, especially if this dimension reduction is done independently of the clustering itself (i.e.: in a vain attempt to find out if clustering will work).
In fact, clus... | null | CC BY-SA 3.0 | null | 2011-06-08T07:01:16.137 | 2011-06-08T07:01:16.137 | null | null | 4257 | null |
11699 | 1 | 139428 | null | 1 | 4759 | I am trying to use SPSS to build a linear regression on historical data (dependent and independent variables) and then apply this to new data (independent variables only) to generate predicted values and associated prediction intervals.
I've looked in detail at the documentation on the `REGRESSION` procedure within SPS... | How do you apply a linear regression built in SPSS to new data and generate prediction intervals | CC BY-SA 3.0 | null | 2011-06-08T07:34:51.063 | 2015-02-26T13:39:26.257 | 2011-06-08T08:50:03.333 | 183 | 4933 | [
"regression",
"spss",
"predictive-models"
] |
11700 | 2 | null | 11689 | 2 | null | I'm not sure I understand what you mean by projecting your sample data, but:
The result per set of 2 classes of LDA is always a linear form in the coordinates of your space (e.g. `3x_1-x_2+2`). Hence it also defines a hyperplane (a line in 2D, a plane in 3D,...), where this linear form is zero, and the 'discriminating'... | null | CC BY-SA 3.0 | null | 2011-06-08T07:35:07.850 | 2011-06-08T07:35:07.850 | null | null | 4257 | null |
11701 | 2 | null | 11418 | 2 | null | I'm still stuck with this problem. I have received some suggestions from the R mailing list (thanks to Christian Hennig) that I attach here:
>
Have you considered the dbscan function in library fpc, or was it
another one? The fpc::dbscan() function doesn't have a "distance"
parameter but several options, one of wh... | null | CC BY-SA 3.0 | null | 2011-06-08T07:57:01.557 | 2011-08-07T20:36:34.503 | 2011-08-07T20:36:34.503 | 930 | 4147 | null |
11702 | 2 | null | 11691 | 86 | null | About k-means specifically, you can use the Gap statistics. Basically, the idea is to compute a goodness of clustering measure based on average dispersion compared to a reference distribution for an increasing number of clusters.
More information can be found in the original paper:
>
Tibshirani, R., Walther, G., and
... | null | CC BY-SA 3.0 | null | 2011-06-08T08:43:28.373 | 2011-06-08T14:50:08.427 | 2017-04-13T12:44:25.283 | -1 | 930 | null |
11703 | 1 | null | null | 3 | 7551 | Assume the following easy example of a glm regression with an offset:
```
numberofdrugs <- rpois(84, 10)
healthvalue <- rpois(84,75)
age <- rnorm(84,50,5)
test <- glm(healthvalue~age, family=poisson, offset=log(numberofdrugs))
summary(test)
fitted(test) # How to get one of these values manually?
``... | How to estimate and interpret an offset correctly in a Poisson regression? | CC BY-SA 3.0 | null | 2011-06-08T10:00:11.857 | 2017-09-18T19:30:48.897 | 2017-09-18T17:17:23.237 | 7290 | 4496 | [
"r",
"regression",
"poisson-distribution",
"count-data",
"offset"
] |
11705 | 2 | null | 11703 | 1 | null | About the practical part -- outputs of `glm` or `summary` are just lists which are pretty-printed for user convenience. You can see their full structure calling `unclass` on them and extract single values as usual, with a help of `$`, `[[]]` and `[]` operators.
| null | CC BY-SA 3.0 | null | 2011-06-08T10:52:42.993 | 2011-06-08T10:52:42.993 | null | null | null | null |
11706 | 2 | null | 11676 | 9 | null | R gives null and residual deviance in the output to `glm` so that you can make exactly this sort of comparison (see the last two lines below).
```
> x = log(1:10)
> y = 1:10
> glm(y ~ x, family = poisson)
>Call: glm(formula = y ~ x, family = poisson)
Coefficients:
(Intercept) x
5.564e-13 1.000e+0... | null | CC BY-SA 3.0 | null | 2011-06-08T11:26:42.833 | 2013-12-10T21:06:39.087 | 2013-12-10T21:06:39.087 | 4862 | 4862 | null |
11707 | 1 | null | null | 75 | 79327 | According to the Wikipedia article on [unbiased estimation of standard deviation](http://en.wikipedia.org/wiki/Unbiased_estimation_of_standard_deviation) the sample SD
$$s = \sqrt{\frac{1}{n-1} \sum_{i=1}^n (x_i - \overline{x})^2}$$
is a biased estimator of the SD of the population. It states that $E(\sqrt{s^2}) \neq ... | Why is sample standard deviation a biased estimator of $\sigma$? | CC BY-SA 3.0 | null | 2011-06-08T12:28:05.087 | 2021-07-25T02:29:55.573 | 2012-07-07T10:01:29.047 | 930 | 4937 | [
"estimation",
"standard-deviation"
] |
11708 | 1 | null | null | 1 | 1425 | I have a large dataset that has many variables. I'm trying to determine which variables correlate strongly with one specific variable. When you look at the entire dataset as a whole, the correlation of different variables is pretty weak.
I know, however, that within certain subsets of the data the correlation is stro... | Determining correlation in certain subsets of a dataset in R | CC BY-SA 3.0 | null | 2011-06-08T12:30:08.830 | 2011-06-08T14:35:23.717 | 2011-06-08T14:35:23.717 | 183 | 4936 | [
"r",
"regression",
"correlation",
"large-data"
] |
11709 | 2 | null | 11703 | 3 | null | There should not be an estimate of the offset: this offset is (could be) different for every observation (the whole idea is that you monitor the number of events within a (linear) 'timemeasure' (here apparently `numberofdrugs`).
There is no one 'population' offset you could estimate: person 1 is going to have 5 drugs a... | null | CC BY-SA 3.0 | null | 2011-06-08T12:31:30.467 | 2011-06-08T12:31:30.467 | null | null | 4257 | null |
11710 | 2 | null | 11687 | 10 | null | There are major power and precision gains from treating Y as ordinal when appropriate. This arises from the much lower number of parameters in the model (by a factor of k where k is one less than the number of categories of Y). There are several ordinal models. The most commonly used are the proportional odds and co... | null | CC BY-SA 3.0 | null | 2011-06-08T12:41:40.190 | 2011-06-08T12:41:40.190 | null | null | 4253 | null |
11711 | 2 | null | 11708 | 4 | null | It is a bit unclear what is your aim behind this, but maybe you just need a feature selection?
Try for instance training a Random Forest predicting the value you optimize from the other ones and extract its importance scores. What it does is almost explicitly a search for hyper-rectangles in your feature space with sma... | null | CC BY-SA 3.0 | null | 2011-06-08T12:46:18.803 | 2011-06-08T12:46:18.803 | null | null | null | null |
11712 | 2 | null | 11708 | 1 | null | Very Interesting problem. With my limited experience my first comment is that this problem would not be having many shortcuts. However I have done this kind of exercise. I would suggest the following points:
1) make a list of the variables that could be related-- this means that dont try to relate in your mind every x ... | null | CC BY-SA 3.0 | null | 2011-06-08T12:51:53.063 | 2011-06-08T13:01:39.073 | 2011-06-08T13:01:39.073 | 1763 | 1763 | null |
11713 | 1 | null | null | 7 | 3354 | This follows on from [my previous question on assessing reliability](https://stats.stackexchange.com/questions/11628/assessing-reliability-of-a-questionnaire-dimensionality-problematic-items-and).
I designed a questionnaire (six 5-points Likert items) to evaluate the attitude of a group of users toward a product. I wo... | Whether to use EFA or PCA to assess dimensionality of a set of Likert items | CC BY-SA 3.0 | null | 2011-06-08T12:57:46.427 | 2011-09-30T20:53:05.520 | 2017-04-13T12:44:29.013 | -1 | 4903 | [
"pca",
"factor-analysis",
"scales",
"reliability",
"likert"
] |
11714 | 1 | 11719 | null | 3 | 3190 | I'm having a hard time understanding what the authors of [this paper (pdf)](http://www.sciamachy.org/validation/documentation/proceedings_ES2007/463103me.pdf) want to tell me with this graph (Fig. 2,3 (shown below) and 4, right):
[Caption:... | Comparing two datasets (of the same physical quantity) - what do I learn from this graph? | CC BY-SA 3.0 | null | 2011-06-08T13:28:20.487 | 2011-06-08T19:02:18.293 | 2011-06-08T13:36:20.213 | 4373 | 4373 | [
"dataset",
"standard-deviation",
"standard-error",
"error-propagation",
"measurement-error"
] |
11716 | 2 | null | 11699 | 4 | null | If you have SPSS Version 19, I believe they introduced "Scoring Wizard" under Utilities that apparently can accomplish this sort of task. That said, I have tried to get it to work and do not have the desire to debug the errors I am getting since it is very easy to do in R.
I echo @Jeromy's response; if you need to s... | null | CC BY-SA 3.0 | null | 2011-06-08T14:31:53.123 | 2011-06-08T14:31:53.123 | null | null | 569 | null |
11717 | 1 | null | null | 6 | 347 | Consider the following survey question:
>
Q: How would you classify the importance for you of the following 5 items:
A
B
C
D
E
Assign to each item a number in the set {1,2,3,4,5}, with 1 meaning the highest importance and 5 meaning the lowest importance; the number used to an item cannot be used in any other item.
... | Testing the importance of an item among a finite set of items | CC BY-SA 3.0 | null | 2011-06-08T14:58:04.123 | 2018-06-09T20:09:43.350 | 2020-06-11T14:32:37.003 | -1 | 6245 | [
"hypothesis-testing",
"ordinal-data",
"ranking",
"paired-data",
"psychometrics"
] |
11718 | 2 | null | 11699 | 0 | null | Why would you use linear regression on time series in the first place ? If you have time series data there may be lags required for all series and adjustments for Pulses , Level Shifts , Seasonal Pulses and /or Local Time Trends. Additionally you might have parameters that change over time (N.B. this is not rectified b... | null | CC BY-SA 3.0 | null | 2011-06-08T15:02:20.717 | 2011-06-08T15:12:11.830 | 2011-06-08T15:12:11.830 | 3382 | 3382 | null |
11719 | 2 | null | 11714 | 5 | null | This appears to be an unconventional way to report correlation (or lack thereof). It focuses more on the variability of the measurements (across the earth at each fixed altitude) than on the correlation among them. As such the graphic may be of physical interest but it's an obscure way (at best) of comparing two meas... | null | CC BY-SA 3.0 | null | 2011-06-08T15:09:59.537 | 2011-06-08T15:09:59.537 | null | null | 919 | null |
11720 | 2 | null | 11717 | 6 | null | The naive approach would be to compute the marginal distribution of rankings (e.g., mean score for each item), but it would throw away a lot of information as it does not account for the within-person relationship between ranks.
As an extension to [paired preference model](http://en.wikipedia.org/wiki/Pairwise_comparis... | null | CC BY-SA 3.0 | null | 2011-06-08T15:28:01.107 | 2011-06-08T15:28:01.107 | null | null | 930 | null |
11721 | 2 | null | 10900 | 4 | null | The Laplace (aka double exponential) distribution has relatively light tails - exponential in fact :). The Laplace and t/Cauchy distributions are part of a larger family of scale mixtures of normals, which are distributions that can be written as an infinite mixture like so:
$$p(x) = \int Nor(x; 0, r^2s^2)p(s^2)ds^2$$
... | null | CC BY-SA 3.0 | null | 2011-06-08T16:06:44.050 | 2011-06-08T16:06:44.050 | null | null | 26 | null |
11722 | 1 | 14782 | null | 6 | 792 | Repeating an experiment ([about which I asked before](https://stats.stackexchange.com/questions/10407/probability-for-finding-a-double-as-likely-event)) with $n$ possible outcomes $t$ times independently, where all but one outcomes have probability $\frac{1}{n+1}$ and the other outcome has the double probability $\frac... | How to combine two independent repeated experiments with different success probabilities? | CC BY-SA 3.0 | null | 2011-06-08T16:13:48.513 | 2011-08-24T22:38:48.597 | 2017-04-13T12:44:29.013 | -1 | 565 | [
"probability",
"sampling"
] |
11723 | 2 | null | 11672 | 2 | null | Sophie and I discussed this earlier (she is a student at my university) and I am still not satisfied with any of my suggestions so far. Here are two possibilities for the winner/loser data (assuming you always have a winner).
1) Compete each yellow against each red (64 competitions) and record which colour won. Test ... | null | CC BY-SA 3.0 | null | 2011-06-08T16:28:56.170 | 2011-06-08T16:28:56.170 | null | null | 266 | null |
11724 | 1 | 11742 | null | 9 | 34926 | I'm running a binary logistic regressions with 3 numerical variables. I'm suppressing the intercept in my models as the probability should be zero if all input variables are zero.
What's minimal number of observations I should use?
| Minimum number of observations for logistic regression? | CC BY-SA 3.0 | null | 2011-06-08T18:33:53.903 | 2019-12-19T16:24:51.600 | 2019-12-19T16:24:51.600 | 11887 | 333 | [
"regression",
"logistic",
"sample-size"
] |
11725 | 2 | null | 11500 | -1 | null | How about generating a synthetic binary target variable first and then running a logistic regression model?
The synthetic variable should be something like... "If the observation is in the top decile on all of the input variable distributions flag it as 1 else 0"
Having generated the binary target variable... Run logis... | null | CC BY-SA 3.0 | null | 2011-06-08T18:43:52.830 | 2011-06-08T18:43:52.830 | null | null | 333 | null |
11726 | 2 | null | 11609 | 2 | null | In frequentist statistics, the event $E$ is fixed -- the parameter either lies in $[a, b]$ or it doesn't. Thus, $E$ is independent of $C$ and $C'$ and so both $P(E|C) = P(E)$ and $P(E|C') = P(E)$.
(In your argument, you seem to think that $P(E|C) = 1$ and $P(E|C') = 0$, which is incorrect.)
| null | CC BY-SA 3.0 | null | 2011-06-08T18:56:31.923 | 2011-06-08T19:12:04.370 | 2011-06-08T19:12:04.370 | 1106 | 1106 | null |
11727 | 2 | null | 11609 | 31 | null | I think the fundamental problem is that frequentist statistics can only assign a probability to something that can have a long run frequency. Whether the true value of a parameter lies in a particular interval or not doesn't have a long run frequency, becuase we can only perform the experiment once, so you can't assig... | null | CC BY-SA 3.0 | null | 2011-06-08T18:57:51.263 | 2011-06-09T09:42:40.690 | 2017-04-13T12:44:55.360 | -1 | 887 | null |
11728 | 2 | null | 11714 | 1 | null | Good questions. I scanned over the paper and have a couple of general thoughts...
First, with respect to
>
Note: contrary to convention, the measured quantity is plotted on the x-axis, not y-axis.
I like the unconventional orientation in this setting: with the Y-axis being altitude it lets me easily visualize that a... | null | CC BY-SA 3.0 | null | 2011-06-08T19:02:18.293 | 2011-06-08T19:02:18.293 | null | null | 1080 | null |
11730 | 2 | null | 11691 | 3 | null | To tell whether a clustering is meaningful, you can run an algorithm to count the number of clusters, and see if it outputs something greater than 1.
Like chl said, one cluster-counting algorithm is the gap statistic algorithm. Roughly, this computes the total cluster variance given your actual data, and compares it ag... | null | CC BY-SA 3.0 | null | 2011-06-08T19:09:12.110 | 2011-06-08T19:09:12.110 | null | null | 1106 | null |
11731 | 2 | null | 11724 | 9 | null | There isn't really a minimum number of observations. Essentially the more observations you have the more the parameters of your model are constrained by the data, and the more confident the model becomes. How many observations you need depends on the nature of the problem and how confident you need to be in your mode... | null | CC BY-SA 3.0 | null | 2011-06-08T19:10:58.603 | 2011-06-08T19:10:58.603 | null | null | 887 | null |
11732 | 2 | null | 10182 | 18 | null | Both methods rely on the same idea, that of decomposing the observed variance into different parts or components. However, there are subtle differences in whether we consider items and/or raters as fixed or random effects. Apart from saying what part of the total variability is explained by the between factor (or how m... | null | CC BY-SA 4.0 | null | 2011-06-08T19:53:52.143 | 2019-02-17T01:36:16.990 | 2019-02-17T01:36:16.990 | 79696 | 930 | null |
11734 | 1 | null | null | 2 | 449 | I have implemented a three-way anova with type III sum of squares in c++. Since some of my experiments (observations) are more important (more informative), I want to give them a higher weight in my analysis. For example, an experiment which is very important has a weight of 10, and a relatively important one has a wei... | How to implement a weighted 3-way ANOVA in unbalanced design? | CC BY-SA 4.0 | null | 2011-06-08T20:34:35.620 | 2021-04-12T03:16:58.320 | 2021-04-12T03:16:58.320 | 11887 | 2885 | [
"anova",
"sums-of-squares",
"weighted-sampling"
] |
11736 | 2 | null | 11724 | 0 | null | Update: I didn't see the above comment, by @David Harris, which is pretty much like mine. Sorry for that. You guys can delete my answer if it is too similar.
I'd second Dikran Marsupail post and add my two cents.
Take in consideration your prior knowledge about the effects that you expect from your independent variable... | null | CC BY-SA 3.0 | null | 2011-06-08T22:03:32.000 | 2011-06-08T22:03:32.000 | null | null | 3058 | null |
11737 | 1 | 11741 | null | 3 | 1880 | This question's context is time series forecasting using regression, with multivariate training data. With a regularization method like LARS w/ LASSO, elastic net, or ridge, we need to decide on the model complexity or regularization parameters. For example, the ridge $\lambda$ penalty or the number of steps to go alon... | Cross-validating for model parameters with time series | CC BY-SA 3.0 | null | 2011-06-08T22:13:31.840 | 2011-06-09T02:10:15.290 | 2011-06-08T23:13:14.547 | null | 4942 | [
"time-series",
"model-selection",
"cross-validation",
"regularization"
] |
11738 | 2 | null | 11609 | 3 | null | The way you pose the problem is a little muddled. Take this statement: Let $E$ be the event that the true parameter falls in the interval $[a,b]$. This statement is meaningless from a frequentist perspective; the parameter is the parameter and it doesn't fall anywhere, it just is. P(E) is meaningless, P(E|C) is meaning... | null | CC BY-SA 3.0 | null | 2011-06-08T22:37:56.597 | 2011-06-08T22:48:35.597 | 2011-06-08T22:48:35.597 | 26 | 26 | null |
11739 | 1 | null | null | 3 | 122 | A treatment was given to one hand of a subject, and a single outcome metric is measured for both hands, twice pre and several times post treatment.
What is best practice for assessing effectiveness of treatment?
Treated and Untreated "groups" really are paired.
| Pre and Post, treated and un treated but from same subject | CC BY-SA 3.0 | null | 2011-06-08T23:58:58.097 | 2011-06-09T02:02:48.033 | 2011-06-09T02:02:48.033 | 183 | 4944 | [
"repeated-measures",
"clinical-trials"
] |
11740 | 2 | null | 11739 | 3 | null | For each time the metric is measured, take the difference of the measurements between the two hands. This gives you just one variable measured over time, which you can measure as repeated measures. You hypothesize that the mean value of this difference across subjects will change (or won't change) after the treatment.
| null | CC BY-SA 3.0 | null | 2011-06-09T01:29:28.610 | 2011-06-09T01:29:28.610 | null | null | 3874 | null |
11741 | 2 | null | 11737 | 3 | null | You can include a "minimum" number of observations that you think you need to fit your model, and exclude n< this number from cross validation. Obviously, you can't fit a model using just the 1st sample, and you can't really fit a model using the 1st 2 samples. At some reasonable point (5? 10?) you'll have enough obs... | null | CC BY-SA 3.0 | null | 2011-06-09T02:10:15.290 | 2011-06-09T02:10:15.290 | null | null | 2817 | null |
11742 | 2 | null | 11724 | 22 | null | There is one way to get at a solid starting point. Suppose there were no covariates, so that the only parameter in the model were the intercept. What is the sample size required to allow the estimate of the intercept to be precise enough so that the predicted probability is within 0.1 of the true probability with 95%... | null | CC BY-SA 3.0 | null | 2011-06-09T02:45:10.820 | 2011-06-09T02:45:10.820 | null | null | 4253 | null |
11743 | 2 | null | 11609 | 11 | null | OK, now you're talking! I've voted to delete my previous answer because it doesn't make sense with this major-updated question.
In this new, updated question, with a computer that calculates 95% confidence intervals, under the orthodox frequentist interpretation, here are the answers to your questions:
- No.
- No.
... | null | CC BY-SA 3.0 | null | 2011-06-09T03:19:31.060 | 2011-06-09T11:58:12.827 | 2011-06-09T11:58:12.827 | null | null | null |
11744 | 2 | null | 11609 | 16 | null | Major update, major new answer. Let me try to clearly address this point, because it's where the problem lies:
"If you argue that "after seeing the interval, the notion of probability no longer makes sense", then fine, let's work in an interpretation of probability in which it does make sense."
The rules of probabilit... | null | CC BY-SA 3.0 | null | 2011-06-09T03:39:05.227 | 2011-06-09T03:46:45.040 | 2011-06-09T03:46:45.040 | 26 | 26 | null |
11745 | 1 | 11779 | null | 4 | 1121 | When characterizing an information measure one desires to have the following 'Grouping' property (cf., Cover&Thomas, Ch.2 exercise 46)
$$H(p_1, p_2,\dots, p_n)=H(p_1+p_2, p_3,\dots, p_n)+(p_1+p_2)H(\frac{p_1}{p_1+p_2},\frac{p_2}{p_1+p_2})$$
(a.k.a. recursive). An analogous Grouping axiom is employed for Renyi entropy... | Property of entropy | CC BY-SA 3.0 | null | 2011-06-09T05:58:45.603 | 2011-06-10T08:55:44.707 | 2011-06-10T08:55:44.707 | 3485 | 3485 | [
"inference",
"entropy",
"information-theory"
] |
11746 | 1 | 11747 | null | 25 | 16973 | The Pearson's coefficient between two variables is quite high (r=.65). But when I rank the variable values and run a Spearman's correlation, the cofficient value is much lower (r=.30).
- What is the interpretation of this?
| What could cause big differences in correlation coefficient between Pearson's and Spearman's correlation for a given dataset? | CC BY-SA 3.0 | null | 2011-06-09T07:14:24.973 | 2017-02-11T13:14:23.617 | 2011-06-09T08:06:27.420 | 183 | 3671 | [
"correlation",
"spearman-rho"
] |
11747 | 2 | null | 11746 | 44 | null |
### Why the big difference
- If your data is normally distributed or uniformly distributed, I would think that Spearman's and Pearson's correlation should be fairly similar.
- If they are giving very different results as in your case (.65 versus .30), my guess is that you have skewed data or outliers, and that out... | null | CC BY-SA 3.0 | null | 2011-06-09T07:32:00.293 | 2011-06-09T12:32:19.720 | 2017-04-13T12:44:26.710 | -1 | 183 | null |
11749 | 1 | null | null | 1 | 179 | I would like simulate appearance of publications in a forum and I need know what is the probability distribution of new question being asked in a forum. In my first simulation I used to normal distribution, but I think that the best distribution can be exponential distribution.
| Probability distribution of questions in a forum | CC BY-SA 3.0 | null | 2011-06-09T09:05:26.687 | 2011-06-10T16:21:24.647 | 2011-06-10T06:46:57.370 | 2116 | 4953 | [
"distributions",
"probability"
] |
11750 | 2 | null | 11544 | 1 | null | I was thinking more about the question and thought I would give a slight enhancement of the naive approach as an answer in hopes that people know further ideas in the direction. It also allows us to eliminate the need to know the size of the fluctuations.
---
The easiest way to implement it is with two parameters $(... | null | CC BY-SA 3.0 | null | 2011-06-09T09:07:54.933 | 2011-06-09T09:07:54.933 | null | null | 4872 | null |
11752 | 1 | null | null | 2 | 1901 | Does anybody know if there're common known disadvantages of a negbin regression? In my opinion it seems to fit every problem pretty good (measured with the estimated dispersionparameter). So why not always use it?
| Disadvantages of negbin regression | CC BY-SA 3.0 | null | 2011-06-09T11:16:12.717 | 2011-08-28T08:48:03.403 | null | null | 4496 | [
"regression",
"generalized-linear-model",
"negative-binomial-distribution"
] |
11753 | 1 | 11757 | null | 3 | 1842 | How do you estimate degrees of freedoms for derived measurements?
I want to assess the significance of the distance of an independent data point to a regression line. I can easily calculate the (vertical) distance between the data point and the regression line, and I get the uncertainty of the distance from the uncerta... | Distance to a regression line, and degrees of freedom | CC BY-SA 3.0 | null | 2011-06-09T11:55:47.333 | 2011-06-09T13:16:40.683 | 2011-06-09T12:18:20.830 | 198 | 198 | [
"regression",
"degrees-of-freedom"
] |
11754 | 1 | 11787 | null | 6 | 447 | I would like to estimate a multi level model in Stata or R (using lmer) where the first level coefficients are the same for all observations, but the coefficients within observation are correlated.
An example would look something like this:
$$Y_i = \beta_1 x_{1i} + \beta_2 x_{2i} + \beta_3 x_{3i} + ... + \varepsilon_{... | Estimating correlated parameters with multi-level model | CC BY-SA 3.0 | null | 2011-06-09T12:29:07.600 | 2017-04-29T21:13:56.563 | 2017-04-29T21:13:56.563 | 28666 | 3700 | [
"r",
"multilevel-analysis",
"lme4-nlme"
] |
11755 | 2 | null | 11753 | 1 | null | Simplest way would be to include the new data point in the regression and add an indicator (dummy) variable to the model that takes the value 1 for your new data point and 0 for all the rest. Then simply look at the t-statistic and p-value for the indicator variable.
This approach assumes the residual variance for the... | null | CC BY-SA 3.0 | null | 2011-06-09T12:55:51.920 | 2011-06-09T12:55:51.920 | null | null | 449 | null |
11757 | 2 | null | 11753 | 6 | null | There is a well established theory of prediction intervals in the context of linear regression. New values at $x=x_0$ have a normal distribution with mean $\alpha+\beta x_0$ (not surprisingly) and variance $\sigma^2\left(1+\frac{1}{n} + \frac{(x_0-\bar{x})^2}{\sum{(x_i-\bar{x})^2}}\right)$.
After plugging in the estim... | null | CC BY-SA 3.0 | null | 2011-06-09T13:16:40.683 | 2011-06-09T13:16:40.683 | null | null | 279 | null |
11758 | 2 | null | 3713 | 32 | null | A quote from Hastie, Tibshirani and Friedman,
[Elements of Statistical Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/),
p. 506:
>
"An appropriate dissimilarity measure
is far more important in obtaining
success with clustering than choice
of clustering algorithm. This aspect
of the problem ...
... | null | CC BY-SA 3.0 | null | 2011-06-09T13:33:16.750 | 2011-06-20T10:17:35.320 | 2011-06-20T10:17:35.320 | 557 | 557 | null |
11759 | 1 | 11760 | null | 5 | 1375 | I want to generate series of 0s and 1s that exhibit some clustering. By this I mean that 1s and 0s should occur together. So I envisage series of 0s and 1s that will exhibit similar clustering of these elements, and not just random series of 0s and 1s.
In essence, for a single time series, I would go about that by thre... | How can I generate correlated timeseries made up of 0s and 1s? | CC BY-SA 3.0 | null | 2011-06-09T14:05:18.013 | 2011-06-14T14:07:31.290 | 2011-06-14T14:07:31.290 | 4955 | 4955 | [
"time-series",
"simulation",
"markov-process"
] |
11760 | 2 | null | 11759 | 7 | null | A standard method is to begin by generating an autocorrelated Gaussian process $z_i$. (It doesn't have to be Gaussian, but such processes are easy to generate.) Take the logistic (inverse logit) of the values, producing a series of numbers $p_i = 1/\left(1 + \exp(-z_i)\right)$ in the interval $(0,1)$. Independently ... | null | CC BY-SA 3.0 | null | 2011-06-09T14:15:29.530 | 2011-06-09T14:15:29.530 | null | null | 919 | null |
11761 | 2 | null | 6978 | 5 | null | You might look into the [Vowpal Wabbit project](https://github.com/JohnLangford/vowpal_wabbit/wiki), from John Langford at Yahoo! Research . It is an online learner that does specialized gradient descent on a few loss functions. VW has some killer features:
- Installs on Ubuntu trivially, with "sudo apt-get install vo... | null | CC BY-SA 3.0 | null | 2011-06-09T14:45:29.813 | 2011-06-09T16:09:25.037 | 2011-06-09T16:09:25.037 | 4942 | 4942 | null |
11762 | 1 | null | null | 2 | 298 | My question is very general. I am learning extreme value theory to examine tail behavior. The concept of regular variation is still too vague to me. Could anyone help to provide more info to clarify? Any thoughts on its importance on probability theory?
| More info needed on second order regular variation in extreme value theory | CC BY-SA 3.0 | null | 2011-06-09T14:49:25.973 | 2011-06-11T05:43:42.933 | 2011-06-10T05:00:01.503 | 919 | 4497 | [
"probability"
] |
11763 | 2 | null | 643 | 7 | null | Often when mathematicians talk about probability they start with a known probability distribution then talk about the probability of events. The true value of the central limit theorem is that it allows us to use the normal distribution as an approximation in cases where we do not know the true distribution. You coul... | null | CC BY-SA 3.0 | null | 2011-06-09T15:53:24.603 | 2011-06-09T15:53:24.603 | null | null | 4505 | null |
11764 | 1 | 11790 | null | 38 | 8670 | Neural networks are often treated as "black boxes" due to their complex structure. This is not ideal, as it is often beneficial to have an intuitive grasp of how a model is working internally. What are methods of visualizing how a trained neural network is working? Alternatively, how can we extract easily digestible de... | How to visualize/understand what a neural network is doing? | CC BY-SA 3.0 | null | 2011-06-09T17:19:19.360 | 2016-02-17T04:10:17.037 | null | null | 2965 | [
"data-visualization",
"neural-networks"
] |
11765 | 1 | null | null | 2 | 704 | The intercoder reliability statistic Krippendorff's alpha is nice because it can be used across many different types of data: nominal, ordinal, interval, ratio, circular, etc. To do so, you just substitute a different distance metric into the reliability calculation. See [wikipedia](http://en.wikipedia.org/wiki/Kripp... | Where do the distance metrics for the Krippendorff's alpha statistic come from? | CC BY-SA 3.0 | null | 2011-06-09T17:47:34.477 | 2018-08-15T08:04:28.927 | 2018-08-15T08:04:28.927 | 11887 | 4110 | [
"distance",
"reliability",
"metric",
"agreement-statistics"
] |
11766 | 2 | null | 9396 | 1 | null | The significance of modeling the cumulative sum of residuals is to better approximate the [Ornstein-Uhlembeck process](http://en.wikipedia.org/wiki/Ornstein%E2%80%93Uhlenbeck_process) of equation $(12)$ with discrete real-life data.
This process $X_i(t)$ represents the idiosyncratic above- or below- market fluctuations... | null | CC BY-SA 3.0 | null | 2011-06-09T18:03:20.480 | 2011-06-09T18:03:20.480 | null | null | 4942 | null |
11767 | 2 | null | 11764 | 13 | null | Estimate feature importance by randomly bumping every value of a single feature, and recording how your overall fitness function degrades.
So if your first feature $x_{1,i}$ is continuously-valued and scaled to $[0,1]$, then you might add $rand(0,1)-0.5$ to each training example's value for the first feature. Then look... | null | CC BY-SA 3.0 | null | 2011-06-09T18:23:18.693 | 2011-06-09T18:23:18.693 | null | null | 4942 | null |
11768 | 1 | null | null | 6 | 444 | In class, we've been learning a myriad of really interesting techniques to sample from a given distribution, filter online data, particle filters, etc.
My issue is that when I take some real-world data and plot it, the distribution is clearly not Gaussian. So, I need to estimate some distribution. Or, in the case o... | Which distribution to use with MCMC and empirical data? | CC BY-SA 3.0 | null | 2011-06-09T18:25:37.017 | 2017-09-28T18:27:35.047 | 2017-09-28T18:27:35.047 | 60613 | 2566 | [
"markov-chain-montecarlo"
] |
11769 | 1 | null | null | 16 | 1105 | As a student in physics, I have experienced the "Why I am a Bayesian" lecture perhaps half a dozen times. It is always the same -- the presenter smugly explains how the Bayesian interpretation is superior to the frequentist interpretation allegedly employed by the masses. They mention Bayes rule, marginalization, pri... | Is there more to probability than Bayesianism? | CC BY-SA 3.0 | null | 2011-06-07T16:47:38.303 | 2020-03-18T01:01:01.113 | 2020-03-18T01:01:01.113 | 11887 | 3334 | [
"probability",
"bayesian",
"frequentist",
"philosophical"
] |
11770 | 2 | null | 11768 | 6 | null | Kolmogorov Smirnoff is always a good test to see if an arbitrary distribution fits. You can use the test cited below to see if two sets of data came from the same distribution:
>
Li, Q. and E. Maasoumi and J.S. Racine
(2009), “A Nonparametric Test for
Equality of Distributions with Mixed
Categorical and Continu... | null | CC BY-SA 3.0 | null | 2011-06-09T19:06:04.097 | 2011-06-10T10:20:25.863 | 2011-06-10T10:20:25.863 | 930 | 1893 | null |
11771 | 2 | null | 11768 | 5 | null | Note that goodness of fit tests can only rule out distributions, they don't prove which distribution the data came from. And in many cases they may have low power to rule out some distributions, so you really don't know if the data comes from that distribution, or you just don't have the power.
But note that you can h... | null | CC BY-SA 3.0 | null | 2011-06-09T19:07:00.947 | 2011-06-09T19:07:00.947 | null | null | 4505 | null |
11772 | 2 | null | 11769 | 12 | null | The Bayesian interpretation of probability suffices for practical purposes. But even given a Bayesian interpretation of probability, there is more to statistics than probability, because the foundation of statistics is decision theory and decision theory requires not only a class of probability models but also the spe... | null | CC BY-SA 3.0 | null | 2011-06-09T19:24:29.173 | 2011-06-09T19:24:29.173 | null | null | 3567 | null |
11773 | 1 | 19499 | null | 3 | 188 | In the following bioinformatics paper, ["Quantifying environmental adaptation of metabolic pathways in metagenomics"](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2629784/), Gianoulis et al. employ the use of two tools to detect multivariate relationships between environmental features and microbiomic features:
- Regul... | What is discriminative partition matching? | CC BY-SA 3.0 | null | 2011-06-09T20:05:42.557 | 2011-12-07T17:51:23.760 | 2011-06-12T08:09:46.040 | null | 3567 | [
"machine-learning"
] |
11774 | 2 | null | 11754 | 0 | null | How is this advantageous over a normal varying coefficient model such as:
```
fit<-lmer(score~1+vector of class_attributes+vector of student attributes
+(1+vector of class attributes+vector of student attributes)
+(1+vector of student attributes|class)
+(1+vector of class attributes|student))
```
?
In this example, th... | null | CC BY-SA 3.0 | null | 2011-06-09T20:14:53.080 | 2011-06-09T22:39:42.577 | 2011-06-09T22:39:42.577 | 1893 | 1893 | null |
11775 | 2 | null | 11634 | 3 | null | In some sense this depends on what you mean by $x$ and $\delta x$. Usually people mean that they are modeling $X$ as a random variable with mean $x$ and variance $(\delta x)^2$. Sometimes they mean the stronger condition that $X$ is actually Gaussian, and sometimes they have a broader meaning that $x$ and $\delta x$ ... | null | CC BY-SA 3.0 | null | 2011-06-09T20:23:13.360 | 2011-06-17T23:04:41.153 | 2011-06-17T23:04:41.153 | 4925 | 4925 | null |
11776 | 2 | null | 11428 | 3 | null | Yes, Gary Becker discusses this at length famously in "Crime and Punishment: An Economic Approach". You can find it at
```
http://www.nber.org/chapters/c3625.pdf
```
and in his Nobel lecture section on crime at
```
http://faculty.smu.edu/millimet/classes/eco4361/readings/quantity%20section/becker.pdf
```
Typically... | null | CC BY-SA 3.0 | null | 2011-06-09T20:38:13.227 | 2011-06-09T20:38:13.227 | null | null | 1893 | null |
11777 | 2 | null | 11595 | 15 | null | An offset model is modeling goals per game, as one can see here:
```
log(goals/games) = a+bx
```
is equivalent to
```
log(goals) -log(games) = a+bx
```
is equivalent to
```
log(goals)= a+bx +log(games) <-this is an offset model, assumes coef on the last term =1
```
See slide 35 here:
[http://www.ed.uiuc.edu/cours... | null | CC BY-SA 3.0 | null | 2011-06-09T20:48:07.277 | 2014-08-31T21:32:50.907 | 2014-08-31T21:32:50.907 | 1970 | 1893 | null |
11778 | 2 | null | 11754 | 1 | null | How about just writing out the likelihood function and maximizing?
| null | CC BY-SA 3.0 | null | 2011-06-09T20:52:53.827 | 2011-06-09T20:52:53.827 | null | null | 3601 | null |
11779 | 2 | null | 11745 | 4 | null | There is a simple interpretation of the above grouping property. Suppose your alphabet is $A, B, C,...$ where the letters have frequency $p_1, p_2, p_3, ..$ Now let $S$ be a random sequence of large length in your alphabet. Introduce a modified alphabet in which the letters $A$ and $B$ are merged into a new letter, ... | null | CC BY-SA 3.0 | null | 2011-06-09T21:09:41.120 | 2011-06-09T21:09:41.120 | null | null | 3567 | null |
11780 | 2 | null | 11769 | 6 | null | There are non-Bayesian systems or philosophies of probability -- Baconian & Pascalian, e.g. If you are into epistemology & philosophy of science you might enjoy the debates--otherwise, you'll shake your head & conclude that in fact the Bayesian interpretation is all there is.
For good discussions,
- Cohen, L.J. An i... | null | CC BY-SA 3.0 | null | 2011-06-09T21:10:18.623 | 2011-06-09T21:10:18.623 | null | null | 11954 | null |
11781 | 2 | null | 11609 | 1 | null | If I say the probability the Knicks scored between xbar - 2sd(x) and xbar + 2sd(x) is about .95 in some given game in the past, that is a reasonable statement given some particular distributional assumption about the distribution of basketball scores. If I gather data about the scores given some sample of games and ca... | null | CC BY-SA 3.0 | null | 2011-06-09T21:29:55.963 | 2011-06-09T22:52:53.693 | 2011-06-09T22:52:53.693 | 1893 | 1893 | null |
11782 | 2 | null | 643 | 2 | null | In my experience the CLT is less useful than it appears. One never knows in the middle of a project whether n is large enough for the approximation to be adequate to the task. And for statistical testing, the CLT helps you protect the type I error but does little to keep the type II error at bay. For example, the t-... | null | CC BY-SA 3.0 | null | 2011-06-09T21:40:25.677 | 2011-06-09T21:40:25.677 | null | null | 4253 | null |
11783 | 2 | null | 11609 | 6 | null | I'll throw in my two cents (maybe redigesting some of the former answers). To a frequentist, the confidence interval itself is in essence a two-dimensional random variable: if you would redo the experiment a gazillion times, the confidence interval you would estimate (i.e.: calculate from your newly found data each tim... | null | CC BY-SA 3.0 | null | 2011-06-09T21:58:35.820 | 2011-06-09T22:20:34.857 | 2011-06-09T22:20:34.857 | 4257 | 4257 | null |
11784 | 2 | null | 11769 | 7 | null | Take a look at [this paper](http://www.stat.columbia.edu/~gelman/research/unpublished/philosophy.pdf) by Cosma Shalizi and Andrew Gelman about philosophy and Bayesianism. Gelman is a proeminent bayesian and Shalizi a frequentist!
Take a look also at [this short criticism](http://cscs.umich.edu/~crshalizi/weblog/664.ht... | null | CC BY-SA 4.0 | null | 2011-06-09T22:46:29.547 | 2019-10-31T10:44:42.600 | 2019-10-31T10:44:42.600 | 11887 | 3058 | null |
11785 | 2 | null | 11657 | 5 | null | At a very high-level view, latent topics are formed from words that often appear together in the same documents.
Your examples don't have a clear set of topics, so let's use the following documents instead:
```
Doc1: After I eat my breakfast of apples, oranges, bananas, and grapes, I'm going to go snowboarding in the A... | null | CC BY-SA 3.0 | null | 2011-06-09T23:16:07.517 | 2011-06-09T23:16:07.517 | null | null | 1106 | null |
11787 | 2 | null | 11754 | 1 | null | Have you tried to use Bugs or Jags, calling one of them from R? The model you seem to be estimating is a simple varying slope model, with predictors at the second level.
I'd rewrite your model as:
Be $i = 1, ...n$ students and $k = 1, ... K$ classes. Assuming your data is in the form student-class (i.e. repeated measur... | null | CC BY-SA 3.0 | null | 2011-06-09T23:28:16.390 | 2011-06-09T23:28:16.390 | null | null | 3058 | null |
11788 | 1 | 11838 | null | 4 | 1311 | I have compiled a very small set of summary data from the literature, and I wish to compare the variances between aspects of the literature-based data, and to some of my own data. The summary data includes the mean, standard deviation and sample size.
In earlier tests, I compared the variances of one continuous depende... | How to compare the variance from published summary statistics with own data? | CC BY-SA 3.0 | null | 2011-06-10T01:31:52.463 | 2011-06-11T21:40:33.070 | 2011-06-11T20:47:27.090 | 4238 | 4238 | [
"r",
"variance",
"descriptive-statistics"
] |
11789 | 2 | null | 11769 | 7 | null | "Bayesian" and "frequentist" aren't "probabilistic philosophies". They're schools of statistical thought and practice concerned mainly with quantifying uncertainty and making decisions, although they're often associated with particular interpretations of probability. Probably the most common perception, although it is ... | null | CC BY-SA 3.0 | null | 2011-06-10T02:12:09.267 | 2011-06-10T02:12:09.267 | null | null | 26 | null |
11790 | 2 | null | 11764 | 12 | null | Neural networks are sometimes called "differentiable function approximators". So what you can do is to differentiate any unit with respect to any other unit to see what their relationshsip is.
You can check how sensitive the error of the network is wrt to a specific input as well with this.
Then, there is something cal... | null | CC BY-SA 3.0 | null | 2011-06-10T06:29:05.517 | 2011-06-10T21:56:42.070 | 2011-06-10T21:56:42.070 | 2860 | 2860 | null |
11791 | 2 | null | 11768 | 2 | null | There is no definitive answer to your second question, since all the method in statistics are dedicated to developing distributions to fit the empirical data. So the "best practice" would be finding the appropriate statistical model, which might have generated the data.
| null | CC BY-SA 3.0 | null | 2011-06-10T06:45:00.600 | 2011-06-10T06:45:00.600 | null | null | 2116 | null |
11793 | 2 | null | 11769 | 6 | null | For me, the important thing about Bayesianism is that it regards probability as having the same meaning we apply intuitively in everyday life, namely the degree of plausibility of the truth of a proposition. Very few of us really use probability to mean strictly a long run frequency in everyday use, if only because we... | null | CC BY-SA 3.0 | null | 2011-06-10T07:08:17.753 | 2011-06-10T07:08:17.753 | null | null | 887 | null |
11794 | 1 | null | null | 2 | 1213 | I need to perform a computation of reliability of a 5-point Likert scale having 6 items. From a factor analysis I found that my scale is a multidimensional scale (3 factors), so I cannot use Cronbach's alpha to compute reliability. I have seen in several papers that it is possible to use the multidimensional extension ... | How to compute multidimensional omega with R | CC BY-SA 3.0 | null | 2011-06-10T08:16:34.113 | 2015-12-15T04:22:41.220 | 2011-06-10T09:21:19.067 | 2116 | 4903 | [
"r",
"reliability",
"likert"
] |
11795 | 1 | 11796 | null | 2 | 6930 | If
$$E[f(x)]=0$$
can we derive that
$$E[f'(x)]=0?$$
For example $f(x)$ means some noise with zero mean, gaussian distribution.
| Is it possible to differentiate in expectation? | CC BY-SA 3.0 | null | 2011-06-10T08:31:26.720 | 2011-06-10T17:56:54.840 | 2011-06-10T09:11:53.237 | 2116 | 4898 | [
"distributions",
"expected-value"
] |
11796 | 2 | null | 11795 | 9 | null | With your definitions no. Suppose we have a random variable $X$, what you are asking if it is possible to derive
$$Ef'(X)=0$$
from
$$Ef(X)=0.$$
Take $f(x)=x$. Then $Ef(X)=EX=0$ and this means that variable $X$ has zero mean. Now $f'(x)=1$, and
$$Ef'(X)=E[1]=1,$$
hence the original statement does not hold for all fun... | null | CC BY-SA 3.0 | null | 2011-06-10T09:18:19.240 | 2011-06-10T09:18:19.240 | null | null | 2116 | null |
11797 | 1 | 11841 | null | 3 | 872 | I have built an unrestricted co-occurrence network of words from a songs corpus. To convert it to a restricted network, Ramon Ferrer Cancho and Ricard V. Sole describe the following approach in their paper [The small world of human language](http://complex.upf.es/~ricard/SWPRS.pdf):
>
The technique can be improved by ... | How to convert an unrestricted co-occurrence network to a restricted one? | CC BY-SA 3.0 | null | 2011-06-10T10:00:13.303 | 2011-06-12T00:19:51.727 | 2011-06-11T08:11:25.157 | 4966 | 4966 | [
"text-mining",
"networks"
] |
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