On Predicting Principal Components through Linear Mixed Models
| Autor: | Renato Salvatore, Maja Bozic, Laura Marcis, Simona Balzano |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Mixed model
Multivariate statistics Multivariate analysis of variance Multivariate random variable Dimensionality reduction Statistics Principal component analysis Covariance Best prediction - Linear mixed model - Variance least squares estimation - Random-effects MANOVA model Generalized linear mixed model Mathematics |
| Zdroj: | Statistical Learning and Modeling in Data Analysis ISBN: 9783030699437 |
| Popis: | This work introduces a Principal Component Analysis of data given by the Best Predictor of a multivariate random vector. The mixed linear model framework offers a comprehensive baseline to get a dimensionality reduction of a variety of random-effects modeled data. Alongside the suitability of using model covariates and specific covariance structures, the method allows the researcher to assess the crucial changes of a set of multivariate vectors from the observed data to the Best Predicted data. The estimation of the parameters is achieved using the extension to the multivariate case of the distribution-free Variance Least Squares method. An application to some Well-being Italian indicators shows the changeover from longitudinal data to the subject-specific best prediction by a random-effects multivariate Analysis of Variance model. |
| Databáze: | OpenAIRE |
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