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...

Parametric regression fitting (such as OLS) to a data setrequires specification of an underlying model. If thespecified model is different from the true model, then theparametric fit suffers to a degree that varies with the extentof model misspecification. Mays and Birch (1996)addressed this...

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 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...

Simple time trend variables in factor demand models can be statistically powerful variables, but may tell the researcher very little. Even more complex specification of technical change, e.g. factor biased, are still the economentrician's measure of ignorance'' about the shifts that occur in the...

A previous report explored and discussed statistical methods and procedures that may be applied to validate the survivability of a complex system of systems that cannot be tested as an entity. It described a methodology where Monte Carlo simulation was used to develop the system survivability...

As part of an overall effort to upgrade and streamline methodologies for safety analyses of nonreactor nuclear facilities at the Savannah River Site (SRS), a human error data base has been developed and is presented in this report. The data base fulfills several needs of risk analysts supporting...

Measurement error modeling is a statistical approach to the estimation of unknown model parameters which takes into account the measurement errors in all of the data. Approaches which ignore the measurement errors in so-called independent variables may yield inferior estimates of unknown model...