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Full-Text Articles in Applied Statistics

Polynomially Adjusted Saddlepoint Density Approximations, Susan Zhe Sheng Nov 2013

Polynomially Adjusted Saddlepoint Density Approximations, Susan Zhe Sheng

Electronic Thesis and Dissertation Repository

This thesis aims at obtaining improved bona fide density estimates and approximants by means of adjustments applied to the widely used saddlepoint approximation. Said adjustments are determined by solving systems of equations resulting from a moment-matching argument. A hybrid density approximant that relies on the accuracy of the saddlepoint approximation in the distributional tails is introduced as well. A certain representation of noncentral indefinite quadratic forms leads to an initial approximation whose parameters are evaluated by simultaneously solving four equations involving the cumulants of the target distribution. A saddlepoint approximation to the distribution of quadratic forms is also discussed. By …


Image Quality Of Energy-Dependent Approaches For X-Ray Angiography, Jesse Evan Tanguay Sep 2013

Image Quality Of Energy-Dependent Approaches For X-Ray Angiography, Jesse Evan Tanguay

Electronic Thesis and Dissertation Repository

Digital subtraction angiography (DSA) is an x-ray-based imaging method widely used for diagnosis and treatment of patients with vascular disease. This technique uses subtraction of images acquired before and after injection of an iodinated contrast agent to generate iodine-specific images. While it is extremely successful at imaging structures that are near-stationary over a period of several seconds, motion artifacts can result in poor image quality with uncooperative patients and DSA is rarely used for coronary applications.

Alternative methods of generating iodine-specific images with reduced motion artifacts might exploit the energy-dependence of x-ray attenuation in a patient. This could be performed …


Stochastic Simulation And Spatial Statistics Of Large Datasets Using Parallel Computing, Jonathan Sw Lee Sep 2013

Stochastic Simulation And Spatial Statistics Of Large Datasets Using Parallel Computing, Jonathan Sw Lee

Electronic Thesis and Dissertation Repository

Lattice models are a way of representing spatial locations in a grid where each cell is in a certain state and evolves according to transition rules and rates dependent on a surrounding neighbourhood. These models are capable of describing many phenomena such as the simulation and growth of a forest fire front. These spatial simulation models as well as spatial descriptive statistics such as Ripley's K-function have wide applicability in spatial statistics but in general do not scale well for large datasets. Parallel computing (high performance computing) is one solution that can provide limited scalability to these applications. This is …


Seasonal Decomposition For Geographical Time Series Using Nonparametric Regression, Hyukjun Gweon Apr 2013

Seasonal Decomposition For Geographical Time Series Using Nonparametric Regression, Hyukjun Gweon

Electronic Thesis and Dissertation Repository

A time series often contains various systematic effects such as trends and seasonality. These different components can be determined and separated by decomposition methods. In this thesis, we discuss time series decomposition process using nonparametric regression. A method based on both loess and harmonic regression is suggested and an optimal model selection method is discussed. We then compare the process with seasonal-trend decomposition by loess STL (Cleveland, 1979). While STL works well when that proper parameters are used, the method we introduce is also competitive: it makes parameter choice more automatic and less complex. The decomposition process often requires that …


A New Diagnostic Test For Regression, Yun Shi Apr 2013

A New Diagnostic Test For Regression, Yun Shi

Electronic Thesis and Dissertation Repository

A new diagnostic test for regression and generalized linear models is discussed. The test is based on testing if the residuals are close together in the linear space of one of the covariates are correlated. This is a generalization of the famous problem of spurious correlation in time series regression. A full model building approach for the case of regression was developed in Mahdi (2011, Ph.D. Thesis, Western University, ”Diagnostic Checking, Time Series and Regression”) using an iterative generalized least squares algorithm. Simulation experiments were reported that demonstrate the validity and utility of this approach but no actual applications were …