Despite their success and widespread usage in industry and business, ES methods have received little attention from the statistical community. We investigate three types of statistical models that have been found to underpin ES methods. They are ARIMA models, state space models with multiple...

Component failure in large-scale IT installations such as cluster supercomputers or internet service providers is becoming an ever larger problem as the number of processors, memory chips and disks in a single cluster approaches a million. In this paper, we present and analyze field-gathered...

In this dissertation, we analyze whether the noise ratio statistic of Durlauf and Hall (1989), NRT, can be used as a non-nested model selection tool in a similar fashion to the Rivers and Vuong (2002) framework. For this purpose, we first show that, when scaled by the sample size T, NRT is...

There has been much interest in the area of model-based reasoning within the Artificial Intelligence community, particularly in its application to diagnosis and troubleshooting. The core issue in this thesis, simply put, is, model-based reasoning is fine, but whence the model? Where do the...

The traditional searching method for model-order selection in linear regression is a nested full-parameters-set searching procedure over the desired orders, which we call full-model order selection. On the other hand, a method for model-selection searches for the best sub-model within each...

Biased estimation has the advantage of reducing the mean squared error (MSE) of an estimator. The question of interest is how biased estimation affects model selection. In this paper, we introduce biased estimation to a range of model selection criteria. Specifically, we analyze the performance...

We study model selection strategies based on penalized empirical loss minimization. We point out a tight relationship between error estimation and data-based complexity penalization: any good error estimate may be converted into a data-based penalty function and the performance of the estimate...

Using a model selection approach, this thesis proposes a constructive data-and-theory-combined procedure to identify model structures in the framework of a linear simultaneous equations system based on observed data. A model structure is characterized by restrictions on the structural...

University of Minnesota Ph.D. dissertation. November 2010. Major: Statistics. Advisor: Dr. Yuhong Yang. 1 computer file (PDF); viii, 86 pages.

This dissertation consists of five independent projects. In each project, a novelstatistical method was developed to address a practical problem encountered in genomiccontexts. For example, we considered testing for constant nonparametric effectsin a general semiparametric regression model in...