This article considers the analysis of clustered data via partial linear regression models. Adopting the idea of modeling the within-cluster correlation from the method of generalized estimating ...

In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial regression model, ...

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 ...

Model building via linear regression models. Method of least squares, theory and practice. Checking for adequacy of a model, examination of residuals, checking outliers. Practical hand on experience ...

An international team of mathematicians, led by Lehigh University statistician Taeho Kim, has introduced an innovative method ...

Regression imputation is commonly used to compensate for item nonresponse when auxiliary data are available. It is common practice to compute survey estimators by treating imputed values as observed ...

Offers an alternative to Markowitz’s “Portfolio Selection”. Outlines the nuts and bolts of correlation between past and future performance, or between expected and actual returns. Explains optimal ...

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...