Semiparametric Hierarchical Model with Heteroscedasticity

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Recent work on hierarchical data analysis mainly focuses on the multilevel structure of the mean response. Little research for hierarchical heteroscedasticity was done in the literature. In this paper, we propose a class of hierarchical models with heteroscedasticity and then investigate the semi-parametric statistical inferences. Laplace’s approximation is employed to obtain an approximated marginal likelihood function and splines method is used to estimate the unknown functions. We also provide the consistency of the estimators. Simulation studies and real data analysis show that the proposed estimation procedures work well.

Bibliographical metadata

Original languageEnglish
Pages (from-to)413-424
Number of pages12
JournalStatistics and its Interface
Issue number3
StatePublished - 30 Jan 2017