This paper describes an iterative procedure for obtaining maximum likelihood estimates of the parameters of a generalized regression model when direct maximization with respect to all parameters is ...

Linear regression remains a cornerstone of statistical analysis, offering a framework for modelling relationships between a dependent variable and one or more independent predictors. Over the past ...

Matrix-covariate is now frequently encountered in many biomedical researches. It is common to fit conventional statistical models by vectorizing matrix-covariate. This strategy results in a large ...

Envelope models represent a significant advancement in multivariate regression analysis, offering an efficient dimension reduction tool that enhances both estimation precision and predictive ...

Learn how hedonic regression helps estimate factors affecting prices in real estate and consumer goods, aiding in precise ...

This article develops five regression models to estimate pipeline construction component costs for different types of pipelines in different regions. Researchers have long used historical pipeline ...

Linear regression is a powerful and long-established statistical tool that is commonly used across applied sciences, economics and many other fields. Linear regression considers the relationship ...

Troy Segal is an editor and writer. She has 20+ years of experience covering personal finance, wealth management, and business news. Eric's career includes extensive work in both public and corporate ...