Questions tagged [covariance-matrix]

A $k\times k$ matrix of covariances between all pairs of $k$ random variables. It is also called variance-covariance matrix or simply the covariance matrix.

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System GMM yields identical results for any weighting matrix

I am estimating a system of seemingly unrelated regressions (SUR) in R. Each of the equations has one unique regressor and one common regressor. I am using ...
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Why does systemfit yield different results for OLS and WLS under cross-equation restrictions?

I am following up on the question "Why does systemfit yield identical results for OLS and WLS?". It deals with estimating a system of linear equations ...
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Covariance inversion for Gaussian process

Background Let $x=f(u_x)\in\mathbb{R}$ and let $y=[f(u_y^1)\cdots f(u_y^{N})]\in\mathbb{R}^N$ for some function $f:u \in \mathbb{R}\mapsto \mathbb{R}$. Given $y$, $u_x$, $u_{y}^1,\dots, u_{y}^{N}$, I ...
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How to test for equal spread in bivariate normal samples with equal means?

I'm working with samples taken from a bivariate normal distribution, where the differences in means is not relevant since all samples are scaled to mean (0,0) anyway, and I'm trying to remember how to ...
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Is there a way to specify multiple covariance structures in nlme for spatio-temporal longitudinal data?

I am trying to build mixed effects models for longitudinal data with spatio-temporal trends. Is there a way to specify multiple covariance structures in nlme or other packages (e.g. both a corARMA()...
1 vote
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Heteroscedasticity-consistent (robust) standard errors complemented by i) confidence intervals for beta, ii) Tolerance and iii) VIF values in R?

In order to solve heteroscedasticity in my data, I ran a regression with heteroscedasticity-consistent ("robust") standard errors. I would also like to report i) standardized betas together ...
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Mixed models: equivalence between residual covariance structure and random effects?

Is there a way to specify the covariance structure of a within-subject repeated measures model (MMRM with no random effects) such that the model is mathematically equivalent to a mixed model with ...
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exclude random effects component for a repeated measure

I'm analyzing a dataset on the Nurse Licensure Exam, comprising 3000 participants. (n) These 3000 participants were randomly recruited from 13 Sites across the US. (group level variable) About 40% of ...
1 vote
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Mapping covariance matrices with the datasets

Can someone please help me with this exercise and explain clearly to me? For me personally, when I look at this exercise, the matrix (2) stand out obviously, as it is a isotropic matrix so, it must be ...
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Joint Distribution Formulation of a Spatial X, Spatial Y, and Spatial Error Model

Introductory Problem: I have $n$ points in 3-D space, where I know their X and Y coordinates (not Z), and therefore the distances between points in those 2 dimensions. Each of the three dimensions has ...
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Distance matrix of actual dataset doesn't obey triangle inequality, leading to non-positive definite covariance matrix

Yesterday I asked a question about why my randomly generated distance matrices were leading to Matérn covariance matrices that were not positive definite. The answer there called my attention to the ...
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Calculating Matérn covariance in R returns matrices that are not positive definite

In R, I am trying to calculate Matérn covariance matrices whose inputs are randomly created distance matrices. However, I often end up getting covariance matrices that are not positive definite, ...
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Bootstrap method with 2 Fisher matrices in order to do the cross-correlations between both

I have 2 Fisher matrices where each colum/row represents the information (in Fisher's sense) of astrophysical parameters. These parameters are in the same order for both matrices. Now, I would like to ...
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GMM derivation for diagonal covariance matrices

I was trying to understand the derivation of M step in the EM algorithm for GMM. All the resources available consider only "full covariance" matrices. I wanted to implement GMM for "...
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Sampling from a very high dimensional Gaussian

I would like to a sample from a Gaussian $N(0,K)$ where $K$ is a kernel gram matrix, so that $K=[K_{ij}]$ with $K_{ij} = k(x_i,x_j)$ for some positive definite function $k$. The first issue is that ...
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How do we interpret the covariance matrices $\textbf{U}$ and $\textbf{V}$ in the Matrix Variate Normal Distribution?

Consider the Matrix Normal Distribution. My first question is: how do we interpret the entries $\textbf{X}_{ij}$ of the random matrix $\textbf{X}(n\times p)$? My second question is: how do we ...
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Understanding the multicollinearity issue in relation to linear regression

There are 2 issues that multicollinearity in linear regression leads to Interpretability goes for a toss Parameter confidence intervals are wide and it is difficult to find a parameter significant I ...
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Is the assumption of a diagonal covariance matrix on the latent space in a variational autoencoder in any way restrictive?

The covariance matrix in an autoencoder is assumed to be diagonal. And, I see it mentioned in good places that this is a fairly restrictive assumption. To quote However, in order to simplify the ...
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Clarification on covariance matrix for multidimensional Gaussian distributions

It is a well known property of Gaussian distributions that if $Y = (Y_1, \ldots, Y_n)$, where each $Y_i$ is a real Gaussian random variable, then the components of $Y$ are independent if and only if ...
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Intuition behind between-group covariance matrix from MANOVA?

Suppose that we have samples from $m$ different $p$-dimensional normal multivariate distributions, where they share a common covariance matrix $\Sigma$ but the mean vectors may be different for each ...
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Passing a cholesky decomposition for a matrix with constrained variances to an objective function

I am trying to optimize an objective function $L(\theta)$ in which some parameters that I aim to recover belong to a covariance matrix, $\Sigma$. $\Sigma$ has a unique structure, which includes ones ...
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Decompose covariance matrix into uncorrelated and correlated part

I have an almost diagonal covariance matrix, I would like to decompose it in an uncorrelated and correlated part: $$\Sigma = \Sigma_U + \Sigma_C$$ where all the matrices above are covariance ...
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Prove that modified RBF function satisfies Mercer conditions

Suppose that I have a modified RBF kernel function. $k(\mathbf{x},\mathbf{y}) = \exp{(-||\mathbf{x}-P\mathbf{y}||^2 })$ where $\mathbf{x},\mathbf{y}$ represent $d$ dimensional inputs and $P$ is the ...
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Is this formula the calculation of covariance matrix

I came across this formula in a text that says $S$ is the sample covariance matrix where $$S = \sum_{j=1}^n(\mathbf{X}_j - \bar{\mathbf{X}})(\mathbf{X}_j-\bar{\mathbf{X}})'$$. What I am trying to ...
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Exponentially Weighted Covariance Matrix with Ledoit Wolf Shrinkage

The Ledoit Wolf paper "Honey, I Shrunk the Sample Covariance Matrix" presents the formulation for the shrinkage intensity parameter estimate in Appendix B. The formula for a weighted ...
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Sample Random Effects from Mixed-effects Model

I have a mixed effects model that I'd like to simulate random effects from using a MVN approximation (sampling from the predictive distribution). My question is what is the advantage of simulating ...
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Origin of the term "spherical" in relation to covariance matrices?

I understand that a covariance matrix with all diagonal elements equal, and all off-diagonal elements also equal (but different to the diagonal elements) is called "spherical". I am curious ...
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Estimating different random effects based on a between-subject factor in a mixed model?

Background I have a between-subject factor of GROUP with 2 levels (Control and Active) and a within-subject factor of TIME with 3 levels (Time1, Time2, and Time3). Code below to generate some dummy ...
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Can I estimate the confidence intervals when fixed-effect model matrix is rank deficient?

I have run model selection for linear mixed effect models with random intercepts, built with lme4 package in R. The top model contains one continuous variable (A), 2 categorical variables (B and C) ...