Sequential D-optimal designs for generalized linear mixed models

被引：5

作者：

Sinha, Sanjoy K. ^{[1
]}

Xu, Xiaojian ^{[2
]}

机构：

[1] Carleton Univ, Sch Math & Stat, Ottawa, ON K1S 5B6, Canada

[2] Brock Univ, Dept Math, St Catharines, ON L2S 3A1, Canada

来源：

JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2011年 / 141卷 / 04期

基金：

加拿大自然科学与工程研究理事会;

关键词：

D-optimality; Generalized linear model; Longitudinal data; Mixed model; Optimal design; LIKELIHOOD; INFERENCE;

D O I：

10.1016/j.jspi.2010.10.008

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We discuss the construction of D-optimal sequential designs for the analysis of longitudinal data or repeated measurements using generalized linear mixed models (GLMMs). We investigate the performance of the design through a simulation study, which indicates that the proposed design can be very successful in improving the efficiency of the ML estimators in GLMMs relative to some common competitors. Our simulations also suggest that the usual normal-theory inference procedures remain valid under the sequential sampling schemes. We also present an example using real data obtained from a clinical study. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：1394 / 1402

页数：9

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