Improving prediction by means of a two parameter approach in linear mixed models
Yükleniyor...
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Taylor & Francis
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this article, two parameter estimator and two parameter predictor are defined via the penalized log-likelihood approach in linear mixed models. The recommended approach is quite useful when there is a strong linear relationship among the variables of fixed effects design matrix. The necessary and sufficient condition for the superiority of the two parameter predictor over the best linear unbiased predictor of linear combinations of fixed and random effects in the sense of matrix mean square error criterion is examined. Additionally, to enhance the practical utility of the two parameter estimator and the two parameter predictor, we focus on the selection issue of two biasing parameters. Thus, 10 different methods for choosing the unknown biasing parameters are offered. Two real data sets are analysed to test the performance of our new two parameter approach. In addition, a comprehensive Monte Carlo simulation is performed.
Açıklama
Anahtar Kelimeler
Multicollinearity, Penalized log-likelihood approach, Two parameter estimator, Two parameter predictor, Mean square error
Kaynak
Journal of Statistical Computation and Simulation
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
91
Sayı
18
Künye
Kuran, Ö. ve Özbay, N (2021). Improving prediction by means of a two parameter approach in linear mixed models. Journal of Statistical Computation and Simulation, 91(18), 3721-3743.
