Sankhyā: The Indian Journal of Statistics, Series B (2008-), Vol. 74, No. 1 (May 2012), pp. 107-125 (19 pages) We develop objective Bayesian analysis for the linear regression model with random errors ...

We’ll use the R software language to run some examples of multiple linear regression and probit regression using the bayesm package that will illustrate these concepts. Hopefully you'll come away with ...

Linear regression can be done under the two schools of statistics (frequentist and Bayesian) with some important differences. Briefly, frequentist statistics relies on repeated sampling and ...

We propose Bayesian parametric and semiparametric partially linear regression methods to analyze the outcome-dependent follow-up data when the random time of a follow-up measurement of an individual ...

Computer tools (R). Applications: Linear models in Regression and Classification (Bayesian Linear Regression, Generalized Linear Models, Logistic Regression), Hierarchical/ Multilevel Models, Cluster ...

[3] Log-Linear Bayesian Additive Regression Trees for Multinomial Logistic and Count Regression Models. Journal of the American Statistical Association (2021).

Discover how linear regression works, from simple to multiple linear regression, with step-by-step examples, graphs and real-world applications.

Computer tools (R). Applications: Linear models in Regression and Classification (Bayesian Linear Regression, Generalized Linear Models, Logistic Regression), Hierarchical/ Multilevel Models, Cluster ...