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...

Mixed models are powerful tools for the analysis of clustered data and many extensions of the classical linear mixed model with normally distributed response have been established. As with all parametric models, correctness of the assumed model is critical for the validity of the ensuing...