Text

Inference for High-Dimensional Linear Mixed-Effects Models : A Quasi-Likelihood Approach

Linear mixed-effects models are widely used in analyzing clustered or repeated measures data. We propose a quasi-likelihood approach for estimation and inference of the unknown parameters in linear mixed-effects models with high-dimensional fixed effects. The proposed method is applicable to general settings where the dimension of the random effects and the cluster sizes are possibly large. Regarding the fixed effects, we provide rate optimal estimators and valid inference procedures that do not rely on the structural information of the variance components. We also study the estimation of variance components with high-dimensional fixed effects in general settings. The algorithms are easy to implement and computationally fast. The proposed methods are assessed in various simulation settings and are applied to a real study regarding the associations between body mass index and genetic polymorphic markers in a heterogeneous stock mice population.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (JASA); Vol.117 No.540 December 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 1835-1846

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

LONGITUDINAL DATA
VARIANCE COMPONENTS
CLUSTERED DATA
RANDOM EFFECTS
DEBIASED LASSO

Info Detail Spesifik

https://doi.org/10.1080/01621459.2021.1888740

Pernyataan Tanggungjawab

Sai Li, T. Tony Cai, Hongzhe Li

Versi lain/terkait

Lampiran Berkas

Komentar