İki SUR Model Altında Ön Tahmin Edicilerin Kovaryans Matrisleri Üzerine Bazı Notlar

Year 2020, Volume: 7 Issue: 1, 273 - 281, 28.06.2020

Nesrin Güler Nevin Yüce

https://doi.org/10.35193/bseufbd.660664

Abstract

Görünürde ilişkisiz regresyon (SUR) modelleri denklemler arasında hataların ilişkili olduğu çoklu regresyon denklemlerinin ele alındığı lineer regresyon modellerinin uzantılarıdır. Bu çalışmada, SUR modelleri altında ön tahmin problemi ele alınmıştır. İki SUR modeli altında tüm bilinmeyen vektörlerin en iyi lineer yansız ön tahmin edicilerinin (BLUP’ larının) istatistiksel özellikleri üzerine çeşitli sonuçlar verilmiştir. Özellikle, matrislerin bazı rank formülleri kullanılarak iki model altında BLUP’ların kovaryans matrisleri üzerine bazı sonuçlar elde edilmiştir.

Keywords

BLUP, kovaryans matrisi, rank, SUR model

Some Notes on Covariance Matrices of Predictors under two SUR Models

Abstract

Seemingly unrelated regression (SUR) models are extensions of linear regression models by considering multiple regression equations with correlated errors among equations. In this study, prediction problem under SUR models are considered. Several results are given on statistical properties of the best linear unbiased predictors (BLUPs) of all unknown vectors under two SUR models. Especially, some results established on covariance matrices of BLUPs under two models by using some rank formulas of matrices.

Keywords

BLUP, covariance matrix, rank, SUR model

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