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 ...

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 ...

Beside the model, the other input into a regression analysis is some relevant sample data, consisting of the observed values of the dependent and explanatory variables for a sample of members of the ...

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 ...

Suzanne is a content marketer, writer, and fact-checker. She holds a Bachelor of Science in Finance degree from Bridgewater State University and helps develop content strategies. Hedonic regression ...

Logistic regression is a powerful statistical method that is used to model the probability that a set of explanatory (independent or predictor) variables predict data in an outcome (dependent or ...