While much attention has focused on the modelling of the interdependencies between key aggregates and stock indices in industrialised countries, this thesis is focused on investments in emerging markets and real estate – two research branches that have up to now not been investigated to a...

We consider the problem of model assessment by risk estimation. Variousapproaches to risk estimation are considered in a uni ed framework. This a discussion of various complexity dimensions and approaches to obtainingbounds on covering numbers is also presented.The second type of training sample...

Previous research implementing stratification on the propensity score has generally relied on using five strata, based on prior theoretical groundwork and minimal empirical evidence as to the suitability of quintiles to adequately reduce bias in all cases and across all sample sizes. This study...

In terms of risk measurement, probability and quantile risk estimation have developed enormously in the past decade, from value-at-risk measures to coherent measures such as expected shortfall. These measures allow an investor to determine their risk profile accounting for losses (quantiles) at...

To overcome the curse of dimensionality, dimension reduction is important andnecessary for understanding the underlying phenomena in a variety of fields.Dimension reduction is the transformation of high-dimensional data into ameaningful representation in the low-dimensional space. It can be...

This thesis is concerned with simulation output analysis. In particular, we are inter-ested in estimating the variance parameter of a steady-state output process. The estimationof the variance parameter has immediate applications in problems involving (i) the precisionof the sample mean as a...

Determnation of the structure of a medium from normal-incidenceacoustic reflection data is a basic problem in fieldsas diverse as medical technology and the earth sciences; thisresearch examines the accuracy with which quantitative structureestimates can be made from noise-corrupted...

Log-linear modeling is a popular statistical tool for analysing a contingency table. This presentation focuses on an alternative approach to modeling ordinal categorical data. The technique, based on orthogonal polynomials, provides a much simpler method of model fitting than the conventional...

Log-linear modeling is a popular statistical tool for analysing a contingency table. This presentation focuses on an alternative approach to modeling ordinal categorical data. The technique, based on orthogonal polynomials, provides a much simpler method of model fitting than the conventional...

Thesis (PhD)--University of South Australia, 2009