Robust population designs for longitudinal linear regression model with a random intercept

Xiao-Dong Zhou (), Yun-Juan Wang and Rong-Xian Yue
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Xiao-Dong Zhou: Shanghai University of International Business and Economics
Yun-Juan Wang: Shanghai University of Engineering Science
Rong-Xian Yue: Shanghai Normal University

Computational Statistics, 2018, vol. 33, issue 2, No 15, 903-931

Abstract: Abstract In this paper, optimal population designs for linear regression model with a random intercept for longitudinal data are considered. The design space is assumed to be a set of equally spaced time points. Taking the sampling scheme for each subject as a multidimensional point in the space of admissible sampling sequence, we determine the optimal number and allocation of sampling times in order to estimate the fixed effects model as accurately as possible. To make comparisons between different designs in a meaningful manner, we take experimental costs into account when defining the D-optimal design criterion function. We take a Bayesian method to overcome the uncertainty of the parameters in the design criterion to derive Bayesian optimal population designs. For complicated cases, we propose a hybrid algorithm to find optimal designs. Meanwhile, we apply the Equivalence Theorem to check the global optimality of these designs.

Keywords: Optimal design; Mixed effects models; Equivalence theorem; Particle swarm optimization (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00180-017-0767-6

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