by Sanjoy K. Mitter, Stephen K. Young. / Bibliography: p. 10. / "January 1979." / Supported by NSF Grant ENG76-02860 NSF Grant ENG77-28444

Thesis (Ph. D.)--University of Washington, 1984. / Vita. Bibliography: leaves [335]-348.

Thesis (Ocean E)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 1982. / Supervised by J. Kim Vandiver and Henri O. Berteaux. Includes bibliographical references (leaves 151-153).

In Bayesian analysis, means are commonly used to summarize Bayesian posterior distributions. Problems with a large number of parameters often require numerical integrations over many dimensions to obtain means. In this dissertation, posterior modes with respect to appropriate measures are used to summarize Bayesian posterior distributions, using the Newton-Raphson method to locate modes. Further inference of modes relies on the normal approximation, using asymptotic multivariate normal distributions to approximate posterior distributions. These techniques are applied to two statistical estimation problems. First, Bayesian sequential dose selection procedures are developed for Bioassay problems using Ramsey's prior [28]. Two adaptive designs for Bayesian sequential dose selection and estimation of the potency curve are given. The relative efficiency is used to compare the adaptive methods with other non-Bayesian methods (Spearman-Karber, up-and-down, and Robbins-Monro) for estimating the ED50 . Second, posterior distributions of the order of an autoregressive (AR) model are determined following Robb's method (1980). Wolfer's sunspot data is used as an example to compare the estimating results with FPE, AIC, BIC, and CIC methods. Both Robb's method and the normal approximation for estimation of the order have full posterior results. / Graduation date: 1992

Ball State University LibrariesLibrary services and resources for knowledge buildingMasters ThesesThere is no abstract available for this thesis.

In recent times, the role of statistical methods based on order statistics has become more and more significant in statistical inference. Let Y1 < Y2 < • • • < Yn be the order statistics corresponding to a random sample of a size n from a continuous distribution having probability density function f(x; e), e c S2. The purpose of this thesis is mainly to examine the procedures for estimating the parameter e using order statistics.The usual procedures for estimation of unknown parameters are based on the whole sample without taking into account the order in which the sample is taken or without arranging the observations in order of magnitude. Order statistics and estimations based on order statistics are becoming more popular due to their frequent use in nonparametric inferences and in robust procedures. Procedures based on order statistics are particularly useful when the examined data contain one or more extreme values or outliers.This thesis will provide useful insight to the problem of estimation using order statistics. Some works in this field will be studied, reviewed and updated. Estimation based on order statistics using full and censored samples for small and large data sets will be investigated with reference to continuous distributions, such as the normal distribution. In particular, estimation problems as well as hypothesis testing for location and scale parameters of some continuous distributions and estimation of quantiles based on order statistics will be examined.Ball State UniversityMuncie, IN 47306

Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references (leaves 196-200). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 112-115). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 132-137). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 104-107). Also available on the Internet.