One form of model robust regression (MRR) predicts mean response as a convexcombination of a parametric and a nonparametric prediction. MRR is a semiparametricmethod by which an incompletely or an incorrectly specified parametric model can beimproved through adding an appropriate amount of a...

The standard methodology when building statistical models has been to use one of several algorithms to systematically search the model space for a good model. If the number of variables is small then all possible models or best subset procedures may be used, but for data sets with a large number...

Since the mid 1980's many statisticians have studied methods for combining parametric andnonparametric esimates to improve the quality of fits in a regression problem. Notably in 1987,Einsporn and Birch proposed the Model Robust Regression estimate (MRR1) in which estimatesof the parametric...

The content of this dissertation is divided into two main topics: 1) nonlinear profilemonitoring and 2) an improved approximate distribution for the T^2 statistic based on thesuccessive differences covariance matrix estimator. (Part 1) In an increasing number of cases the quality of a product or...

This research is motivated by an issue frequently encountered in water quality monitoring and ecological assessment. One concern for researchers and watershed resource managers is how the biological community in a watershed is affected by human activities. The conventional single model approach...

This dissertation examines a number of issues related to Static and Dynamic Student t Regression Models. The Static Student t Regression Model is derived and transformed to an operational form. The operational form is then examined in a series of Monte Carlo experiments. The model is judged...