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...

Most discrete time literature uses the beta that results from a regression of an asset's simple returns on various factors to quantify risk. The departing point for this thesis is the consistent use of log-returns. When log-returns are considered, the relevant measure of systematic risk becomes...

In population based genetic association studies, confounding due to population stratification (PS) arises when differences in both allele and disease frequencies exist in a population of mixed racial/ethnic subpopulations. Propensity scores are often used to address confounding in observational...

What are the natural loss functions for binary class probability estimation? This question has a simple answer: so-called "proper scoring rules". These loss functions, known from subjective probability, measure the discrepancy between true probabilities and estimates thereof. They comprise all...

In non-parametric function estimation, providing a confidence band with the right coverage is a challenging problem. This is especially the case when the underlying function has a wide range of unknown degrees of smoothness. Here we propose two methods of constructing an average coverage...