An improved meta-analysis for analyzing cylindrical-type time series data with applications to forecasting problem in environmental study. Issue 3 (17th February 2018)

• Record Type: Journal Article
• Authors: Wang, Shuo; Kim, Sungsu; Peiris, Thelge Buddika
• Is Part Of: Journal of applied statistics. Volume 45:Issue 3(2018)
• Page Start: 474
• Page End: 486
• Publication Date: 2018-02-17
• Subjects: Meta-analysis -- covariance matrix -- generalized least square -- linear-circular regression -- synthesis of regression slopes; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2017.1280451
• Languages: English
• ISSNs: 0266-4763

Abstract

ABSTRACT: In this paper, we propose an improved generalized least square (GLS) meta-analysis in a linear-circular regression, and show its utility in the analysis of a certain environmental issue. The existing GLS meta-analysis proposed in Becker and Wu has a serious flaw since information about the covariance among coefficients across studies is not utilized. In our proposed meta-analysis, we take the correlations between adjacent studies into account, and improve the existing GLS meta-analysis. We provide numerical examples to compare the proposed method with several other existing methods by using Akaike's Information Criterion, Bayesian Information Criterion and mean square prediction errors with applications to forecasting problem in Environmental study.