Fast, closed-form, and efficient estimators for hierarchical models with AR(1) covariance and unequal cluster sizes. Issue 5 (28th May 2018)

• Record Type: Journal Article
• Authors: Hermans, Lisa; Nassiri, Vahid; Molenberghs, Geert; Kenward, Michael G.; Van der Elst, Wim; Aerts, Marc; Verbeke, Geert
• Is Part Of: Communications in statistics. Volume 47:Issue 5(2018)
• Page Start: 1492
• Page End: 1505
• Publication Date: 2018-05-28
• Subjects: Maximum likelihood -- Pseudo-likelihood -- Unequal cluster size; 62B05 -- 62F10; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2017.1316395
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: This article is concerned with statistically and computationally efficient estimation in a hierarchical data setting with unequal cluster sizes and an AR(1) covariance structure. Maximum likelihood estimation for AR(1) requires numerical iteration when cluster sizes are unequal. A near optimal non-iterative procedure is proposed. Pseudo-likelihood and split-sample methods are used, resulting in computing weights to combine cluster size specific parameter estimates. Results show that the method is statistically nearly as efficient as maximum likelihood, but shows great savings in computation time.