Thesis (Ph. D.)--Carleton University, 1992. / Also available in electronic format on the Internet.

Thesis (doctoral)--Universität Bonn, 1988. / Includes bibliographical references (p. 99-102).

No description available.

Approximate formulae are derived for obtaining confidence intervals for the probabilities of a multinomial distribution. The approach used is to consider the Chi-square goodness of fit statistic as a function of the population parameters and to invert this function to obtain a set of simultaneous confidence intervals for the parameters The confidence coefficient for the set of simultaneous confidence intervals obtained by this procedure is conservative, i.e., the true probability that every interval covers its corresponding parameter will in general be greater than the coefficient obtained by this method. As the sample size increases the intervals will converge on the population parameters and will estimate them exactly in the limit. / Master of Science

We present limit theory for tests of homogeneity for finite mixture models. More specifically, we derive the asymptotic distribution of certain random quantities used for testing that a mixture of two distributions is in fact just a single distribution. Our methods apply to cases where the mixture component distributions come from one of a wide class of one-parameter exponential families, both continous and discrete. We consider two random quantities, one related to testing simple hypotheses, the other composite hypotheses. For simple hypotheses we consider the maximum of the standardised score process, which is itself a test statistic. For composite hypotheses we consider the maximum of the efficient score process, which is itself not a statistic (it depends on the unknown true distribution) but is asymptotically equivalent to certain common test statistics in a certain sense. We show that we can approximate both quantities with the maximum of a certain Gaussian process depending on the sample size and the true distribution of the observations, which when suitably normalised has a limiting distribution of the Gumbel extreme value type. Although the limit theory is not practically useful for computing approximate p-values, we use Monte-Carlo simulations to show that another method suggested by the theory, involving using a Studentised version of the maximum-score statistic and simulating a Gaussian process to compute approximate p-values, is remarkably accurate and uses a fraction of the computing resources that a straight Monte-Carlo approximation would.

In this dissertation, the classical problems of testing goodness-of-fit of uniformity and parametric families are reconsidered. A new omnibus test for these problems is proposed and investigated. The new test statistics are a combination of Bayesian and score test ideas. More precisely, singletons that contain only one more parameter than the null describing departures from the null model are introduced. A Laplace approximation to the posterior probability of the null hypothesis is used, leading to test statistics that are weighted sums of exponentiated squared Fourier coefficients. The weights depend on prior probabilities and the Fourier coefficients are estimated based on score tests. Exponentiation of Fourier components leads to tests that can be exceptionally powerful against high frequency alternatives. Comprehensive simulations show that the new tests have good power against high frequency alternatives and perform comparably to some other well-known omnibus tests at low frequency alternatives. Asymptotic distributions of the proposed test are derived under null and alternative hypotheses. An application of the proposed test to an interesting real problem is also presented.

We consider the problem of estimating the reliability of bundles constructed of several fibres, given a particular kind of censored data. The bundles consist of several fibres which have their own independent identically dis-tributed failure stresses (i.e.the forces that destroy the fibres). The force applied to a bundle is distributed between the fibres in the bundle, accord-ing to a load-sharing model. A bundle with these properties is an example of a load-sharing system. Ropes constructed of twisted threads, compos-ite materials constructed of parallel carbon fibres, and suspension cables constructed of steel wires are all examples of load-sharing systems. In par-ticular, we consider bundles where load-sharing is described by either the Equal load-sharing model or the more general Local load-sharing model. In order to estimate the cumulative distribution function of failure stresses of bundles, we need some observed data. This data is obtained either by testing bundles or by testing individual fibres. In this thesis, we develop several theoretical testing methods for both fibres and bundles, and related methods of statistical inference. Non-parametric and parametric estimators of the cumulative distribu-tion functions of failure stresses of fibres and bundles are obtained from different kinds of observed data. It is proved that most of these estimators are consistent, and that some are strongly consistent estimators. We show that resampling, in this case random sampling with replacement from sta-tistically independent portions of data, can be used to assess the accuracy of these estimators. Several numerical examples illustrate the behavior of the obtained estimators. These examples suggest that the obtained estimators usually perform well when the number of observations is moderate.

We present limit theory for tests of homogeneity for finite mixture models. More specifically, we derive the asymptotic distribution of certain random quantities used for testing that a mixture of two distributions is in fact just a single distribution. Our methods apply to cases where the mixture component distributions come from one of a wide class of one-parameter exponential families, both continous and discrete. We consider two random quantities, one related to testing simple hypotheses, the other composite hypotheses. For simple hypotheses we consider the maximum of the standardised score process, which is itself a test statistic. For composite hypotheses we consider the maximum of the efficient score process, which is itself not a statistic (it depends on the unknown true distribution) but is asymptotically equivalent to certain common test statistics in a certain sense. We show that we can approximate both quantities with the maximum of a certain Gaussian process depending on the sample size and the true distribution of the observations, which when suitably normalised has a limiting distribution of the Gumbel extreme value type. Although the limit theory is not practically useful for computing approximate p-values, we use Monte-Carlo simulations to show that another method suggested by the theory, involving using a Studentised version of the maximum-score statistic and simulating a Gaussian process to compute approximate p-values, is remarkably accurate and uses a fraction of the computing resources that a straight Monte-Carlo approximation would.

Thesis (Ph. D.)--University of Sydney, 2002. / Title from title screen (viewed Apr. 28, 2008). Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy to the School of Mathematics and Statistics, Faculty of Science. Includes bibliography. Also available in print form.

Thesis (Ph. D.)--University of Missouri-Columbia, 1997. / Typescript. Vita. Includes bibliographical references (leaves 114-117). Also available on the Internet.