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For i = 1, …, K, let Y i denote the response variable for the ith individual, and x i = ( x i1 , …, x iv , …, x ip ) ′ be the associated p −dimensional covariate vector. Also, let β be the p −dimensional vector of regression effects of x i on y i . Further suppose that the responses are collected from K independent individuals. It is understandable that if the probability distribution of Y i is not known, then one can not use the well known likelihood approach to estimate the underlying regression parameter β.

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Generalized Quasi-Likelihood (GQL) Inferences, On

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  • 发表日期：

    2025年

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  • Brajendra C. Sutradhar

摘要

For i = 1, …, K, let Y i denote the response variable for the ith individual, and x i = ( x i1 , …, x iv , …, x ip ) ′ be the associated p −dimensional covariate vector. Also, let β be the p −dimensional vector of regression effects of x i on y i . Further suppose that the responses are collected from K independent individuals. It is understandable that if the probability distribution of Y i is not known, then one can not use the well known likelihood approach to estimate the underlying regression parameter β.