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1500 questions
39
votes
5 answers
Will the mean of a set of means always be the same as the mean obtained from the entire set of raw data?
If I have calculated the mean for 4 data sets (which do have different sample sizes), can I then obtain an "overall mean" by calculating the "mean of the means"? If yes, will this "mean of the means" be the same as if I had combined the data from…
user66429
- 391
39
votes
2 answers
How to use both binary and continuous variables together in clustering?
I need to use binary variables (values 0 & 1) in k-means. But k-means only works with continuous variables. I know some people still use these binary variables in k-means ignoring the fact that k-means is only designed for continuous variables. This…
GeorgeOfTheRF
- 5,593
39
votes
7 answers
Are all simulation methods some form of Monte Carlo?
Is there a simulation method that is not Monte Carlo? All simulation methods involve substituting random numbers into the function to find a range of values for the function. So are all simulation methods in essence Monte Carlo methods?
Victor
- 6,565
39
votes
5 answers
Are decision trees almost always binary trees?
Nearly every decision tree example I've come across happens to be a binary tree. Is this pretty much universal? Do most of the standard algorithms (C4.5, CART, etc.) only support binary trees? From what I gather, CHAID is not limited to binary…
Michael McGowan
- 4,761
39
votes
2 answers
Why use stratified cross validation? Why does this not damage variance related benefit?
I've been told that is beneficial to use stratified cross validation especially when response classes are unbalanced. If one purpose of cross-validation is to help account for the randomness of our original training data sample, surely making each…
James Owers
- 647
39
votes
9 answers
What is the relationship between $Y$ and $X$ in this plot?
What is the relationship between $Y$ and $X$ in the following plot?
In my view there is negative linear relationship, But because we have a lot of outliers, the relationship is very weak. Am I right?
I want to learn how can we explain…
PSS
- 843
39
votes
1 answer
Covariance of a random vector after a linear transformation
If $\mathbf {Z}$ is random vector and $A$ is a fixed matrix, could someone explain why $$\mathrm{cov}[A \mathbf {Z}]= A \mathrm{cov}[\mathbf {Z}]A^\top.$$
user92612
- 745
39
votes
5 answers
What is the reason the log transformation is used with right-skewed distributions?
I once heard that
log transformation is the most popular one for right-skewed distributions in linear regression or quantile regression
I would like to know is there any reason underlying this statement? Why is the log transformation suitable for…
user3269
- 5,152
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39
votes
4 answers
What is the meaning of the "." (dot) in R?
I'm just reading the book "R in a Nutshell".
And it seems as if I skipped the part where the "." as in "sample.formula" was explained.
> sample.formula <- as.formula(y~x1+x2)
Is sample an object with a field formula as in other languages? And if…
Fabian
- 1,501
39
votes
4 answers
What is theta in a negative binomial regression fitted with R?
I've got a question concerning a negative binomial regression: Suppose that you have the following commands:
require(MASS)
attach(cars)
mod.NB<-glm.nb(dist~speed)
summary(mod.NB)
detach(cars)
(Note that cars is a dataset which is available in R,…
MarkDollar
- 5,955
39
votes
2 answers
How do I know which method of cross validation is best?
I am trying to figure out which cross validation method is best for my situation.
The following data are just an example for working through the issue (in R), but my real X data (xmat) are correlated with each other and correlated to different…
rdorlearn
- 3,653
39
votes
5 answers
Difference between Bayesian networks and Markov process?
What is the difference between a Bayesian Network and a Markov process?
I believed I understood the principles of both, but now when I need to compare the two I feel lost. They mean almost the same to me. Surely they are not.
Links to other…
rockstar
- 491
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38
votes
4 answers
Can anyone clarify the concept of a "sum of random variables"
In my probability class the terms "sums of random variables" is constantly used. However, I'm stuck on what exactly that means?
Are we talking about the sum of a bunch of realizations from a random variable? If so, doesn't that add up to a single…
Gosset
- 545
38
votes
2 answers
Why is the Expectation Maximization algorithm guaranteed to converge to a local optimum?
I have read a couple of explanations of EM algorithm (e.g. from Bishop's Pattern Recognition and Machine Learning and from Roger and Gerolami First Course on Machine Learning). The derivation of EM is ok, I understand it. I also understand why the…
michal
- 1,288
38
votes
1 answer
Does Cox Regression have an underlying Poisson distribution?
Our small team was having a discussion and got stuck. Does anyone know whether Cox regression has an underlying Poisson distribution. We had a debate that maybe Cox regression with constant time at risk will have similarities with Poisson regression…
Julie
- 563