Nonparametric empirical bayes prediction in mixed models Nonparametric empirical bayes prediction in mixed models

Mixed models are classical tools in statistics for modeling repeated data on subjects, such as data on patients or firms collected over time. They extend conventional linear models to include latent parameters, called random effects, that capture between-subject variation and accommodate dependence...

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Vydáno v:	Statistics and computing Ročník 35; číslo 5
Hlavní autoři:	Banerjee, Trambak, Sharma, Padma
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.10.2025 Springer Nature B.V
Témata:	Artificial Intelligence Computer Science Normal distribution Original Paper Probability and Statistics in Computer Science Statistical Theory and Methods Statistics and Computing/Statistics Programs Nonparametric empirical Bayes Shrinkage prediction Kernelized Stein’s discrepancy Mixed models
ISSN:	0960-3174, 1573-1375
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