PriMera Scientific Medicine and Public Health (ISSN: 2833-5627)

Review Article

Volume 6 Issue 6

Generalized Regression Models with an Increasing Number of Unknown Parameters

Asaf Hajiyev* and Jiuping Xu

June 04, 2025

DOI : 10.56831/PSMPH-06-218

Abstract

The paper considers the general form of regression models with an increasing number of unknown parameters and different and unknown random error variances. Such models are typical for applications and allow us to solve some practical problems that face difficulties. Linear and nonlinear regression models can be considered as a partial form of this model. The iterated process for calculating the least square estimators for generalized regression models is constructed. The approach for estimating the elements of the covariance matrix of the deviation vector is suggested. Using these results, the method of constructing a confident band for unknown functions in regression models is suggested.

Keywords: generalized regression model; increasing number of unknown parameters; least square estimator; unknown variances of random errors; design matrix; Fisher’s matrix; iterated process

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