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• W2155684712 abstract "Classical regression analysis relates the expectation of a response variable to a linear combination of explanatory variables. In this article, we propose a covariance regression model that parameterizes the covariance matrix of a multivariate response vector as a parsimonious quadratic function of explanatory variables. The approach is analogous to the mean regression model, and is similar to a factor analysis model in which the factor loadings depend on the explanatory variables. Using a random-effects representation, parameter estimation for the model is straightforward using either an EM-algorithm or an MCMC approximation via Gibbs sampling. The proposed methodology provides a simple but flexible representation of heteroscedasticity across the levels of an explanatory variable, improves estimation of the mean function and gives better calibrated prediction regions when compared to a homoscedastic model." @default.
• W2155684712 created "2016-06-24" @default.
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• W2155684712 date "2012-04-01" @default.
• W2155684712 modified "2023-10-01" @default.
• W2155684712 title "A Covariance Regression Model" @default.
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• W2155684712 doi "https://doi.org/10.5705/ss.2010.051" @default.
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