I am curious if anyone has any insight into the intuition behind Sverdrup’s Lemma (e.g. GENERALIZATION OF SVERDRUP’S LEMMA AND ITS APPLICATIONS TO MULTIVARIATE DISTRIBUTION THEORY by Kabe), unfortunately the original paper by Sverdrup is behind a paywall I can’t get past. Sverdrup’s Lemma is saying presumably saying something about how to construct multivariate distributions, and my hunch is that it is a result about change of variables, but the formula is quite hard to parse, and there is little explanation in the paper (all caps). Thanks.
I wrote a blog on how to implement Multinomial Naive Bayes classifier (from scratch) using Python for categorizing news papers:
Hi, I have a big doubt when applying multi-testing to bootstrap p-values. I now have a problem where I want to know if my observed value (X) is significantly higher or lower than spected by chance.
We’re using the bootstrap distribution to determine the probability of observing a value higher or lower than X. So if X leaves a probability of p-value=0.15 at the right of that distribution, I would say that at the significance level alpha = 0.05, it is not higher than expected by chance, nor lower.
Let’s say that I know want to apply some multi-testing model, for example FDR or Bonferroni, so I input my pvalue = 0.15, and I get a corrected p-value = 1.0.
However, I have a difficult understanding this corrected value. The previous pvalue = 0.15 had a correspondence with the observed value of X in the bootstrap distribution, but now the pvalue = 1.0 would have a correspondence with…. what? I don’t understand it. I a one-tailed test it could be the lowest possible value, and I’m not sure that this is a valid reasoning, but any way this a a two-tailed test, so, this pvalue=1.0 what does mean exaclty in the bootstrap distribution??
I’m performing a GLM testing the effects of 3 angles and speed on duty factor (% time spent with foot off the ground compared to on the ground).
I am trying to find the equations of the 3 lines and I am having trouble. Any help?
Here’s the output: https://imgur.com/222363C
Here’s the graph: https://imgur.com/vYsiUPG
EDIT: using SPSS
I have a simulation which spits out data in the form of a spreadsheet containing the frequency density of each value. Whilst this is useful for creating a histogram I need to do further statistical tests on it and I think using SPSS would be the easiest way to do this. However I can’t manually import the data to SPSS as I have several samples each containing 100,000 simulations. Is there a way to perform statistical tests on this kind of data without having to do it manually?
I am comparing disease risk scores (non normal data) within the range of antelope which changes during
1.Summer 2.Spring and 3.Winter (my 3 samples)
They antelope are in different counties during each season. 32 counties in summer , 33 in spring and 35 in winter. Each county has a score based on disease risk. I am comparing these scores.
Now looking into a test called Skillings.Mack. Know anything about this? Or any other tests I could use?/ ways of solving this problem.
Thanks in advance
I’ve been trying to wrap my head around the concept of sufficiency and I thought of this. Is this true? That your data itself can be a sufficient statistic. I’m personally leaning towards no. As depending on what parameters you have, the distribution of the data of the data will depend on the parameter. Am I correct?
Feller gives a simple formula for the Ruin problem for winning or losing 1 at each trial. Is there a similar formula for the case where the winning and losing amounts are unequal?
IE, how can one choose between a small probability of loss or a small amount of loss?