Hrishikesh D. Vinod, Application of New Ridge Regression Methods to a Study of Bell System Scale Economies, Journal of the American Statistical Association, Vol. 71, No. 356 (Dec., 1976), pp. 835-841 ...

This book also explains the differences and similarities between the many generalizations of the logistic regression model. The following topics are covered: binary logit analysis, logit analysis of ...

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 ...

Na Zhao, Yonghui Yang, Xinyao Zhou, Application of geographically weighted regression in estimating the effect of climate and site conditions on vegetation distribution in Haihe Catchment, China, ...

There are many different techniques available to create a regression model. Some common techniques, listed from less complex to more complex, are: linear regression, linear lasso regression, linear ...

The Regression Discontinuity Design (RDD) is one of the emerging methodology for recent policy impact assessment area. But based on the unique nature of RDD, its assumptions for practical application ...

A solid coverage of the most important parts of the theory and application of regression models, and generalised linear models. Multiple regression and regression diagnostics. Generalised linear ...

Multiple regression and regression diagnostics. Generalised linear models; the exponential family, the linear predictor, link functions, analysis of deviance, parameter estimation, deviance residuals.