# All Questions

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### Negative value of Specific Combining Abilities (SCA) Variance and Dominance Variance

Being a student of Statistics, I know variance can not take negative value by definition. However, I can across the following two articles (snaps attached) where they reported negative values for ...
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### Calculating the Chi Squared from combinations of subgroups

Let's say I can calculate the chi squared p-value of different categorical variables and summarise them into a table like so: Initial Data: ...
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### Hypothesis testing and post-hoc tests (within and between subjects) for Gaussian and non-Gaussian measures

I am doing a study to evaluate the effect of two dietary supplements on body composition. Literature suggests that Treatment 1 (Dietary Supplement 1) affects body composition with some side effects ...
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### Diffusion coefficient from double-normal probability density function

The spread of individuals of species is often described by so-called dispersal kernels. The main parameter of spread is then often the variance defined as the average squared movement distance of a ...
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### Likelihood convexification

I am doing constrained vector optimization using a non-convex non-linear likelihood function. My problem is of the following form: \begin{align*}\hat Q &= \underset{\vec Q}{\arg\min} -\log ...
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### Latin Hypercube Sampling Asymptotics

I am trying to construct a proof for a problem I am working on and one of the assumptions that I am making is that the set of points I am sampling from is dense over the entire space. Practically, I ...
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### “The total area underneath a probability density function is 1” - relative to what?

Conceptually I grasp the meaning of the phrase "the total area underneath a PDF is 1". It should mean that the chances of the outcome being in the total interval of possibilities is 100%. But I ...
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### What is the difference between a neural network and a deep neural network

I haven't seen the question stated precisely in these terms, and this is why I make a new question. What I am interested in knowing is not the definition of a neural network, but understanding the ...
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### Multiple eigenvectors in graph spectral clustering

In Newman's PNAS 2006 paper Modularity and community structure in networks, the first eigenvector splits the graph in two clusters, and then each cluster can be further divided by eigenvector of a ...
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### Standard library for Funk SVD or other gradient descent SVD/eigenvalue

I want to get the first few eigenvectors of real symmetric matrices with missing values. Some flavor of Funk's SVD should be able to solve this. I will be using this in R so any R or C++ library that ...
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### Asymptotic normality of order statistic of heavy tailed distributions

Background: I have a sample which I want to model with a heavy tailed distribution. I have some extreme values, such that the spread of the observations are relatively large. My idea was to model this ...
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### Assessments of “Approximately Normal” for t-tests

I am testing equality of means using Welch's t-test. The underlying distribution is far from normal (more skewed than the example in a related discussion here). I can obtain more data but would like ...
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### Understanding distance correlation computations

As far as I understood, distance correlation is a robust and universal way to check if there is a relation between two numeric variables. For example, if we have a set of pairs of numbers: ...
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I am having some trouble in understanding odds, and I would like just a basic explanation for how to interpret them. I have found various posts related to odds but most of them are more complex than ...