Pan, Junhao. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 121-135). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

In this thesis we develop a two component mixture model to perform a Bayesian regression. We implement our model computationally using the Gibbs sampler algorithm and apply it to a dataset of differences in time measurement between two clocks. The dataset has ``good" time measurements and ``bad" time measurements that were associated with the two components of our mixture model. From our theoretical work we show that latent variables are a useful tool to implement our Bayesian normal mixture model with two components. After applying our model to the data we found that the model reasonably assigned probabilities of occurrence to the two states of the phenomenon of study; it also identified two processes with the same slope, different intercepts and different variances. / McAnulty College and Graduate School of Liberal Arts; / Computational Mathematics / MS; / Thesis;

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 215-219).

The purpose of this paper is to generalize regression models for repeated categorical data based on maximizing a conditional likelihood. Some existing methods, such as those proposed by Duncan (1985), Fischer (1989), and Agresti (1993, and 1997) are special cases of this latent variable approach, used to account for dependencies in clustered observations. The generalization concerns the incorporation of rather general data structures such as subject-specific time-dependent covariates, a variable number of observations per subject and time periods of arbitrary length in order to evaluate treatment effects on a categorical response variable via a linear parameterization. The response may be polytomous, ordinal or dichotomous. The main tool is the log-linear representation of appropriately parameterized Rasch-type models, which can be fitted using standard software, e.g., R. The proposed method is applied to data from a psychiatric study on the evaluation of psychobiological variables in the therapy of depression. The effects of plasma levels of the antidepressant drug Clomipramine and neuroendocrinological variables on the presence or absence of anxiety symptoms in 45 female patients are analyzed. The individual measurements of the time dependent variables were recorded on 2 to 11 occasions. The findings show that certain combinations of the variables investigated are favorable for the treatment outcome. (author´s abstract) / Series: Research Report Series / Department of Statistics and Mathematics

published_or_final_version / Community Medicine / Doctoral / Doctor of Philosophy

Reliability issues are always salient as behavioral researchers observe human behavior and classify individuals from criterion-referenced test scores. This has created a need for studies to assess agreement between observers, recording the occurrance of various behaviors, to establish the reliability of their classifications. In addition, there is a need for measuring the consistency of dichotomous and polytomous classifications established from criterion-referenced test scores. The development of several log linear univariate models for measuring agreement has partially met the demand for a probability-based measure of agreement with a directly interpretable meaning. However, multi-variate repeated measures agreement produres are necessary because of the development of complex intrasubject and intersubject research designs. The present investigation developed applications of the log linear, latent class, and weighted least squares procedures for the analysis of multivariate repeated measures designs. These computations tested the model-data fit and calculated the multivariate measure of the magnitude of agreement under the quasi-equiprobability and quasi-independence models. Applications of these computations were illustrated with real and hypothetical observational data. It was demonstrated that employing log linear, latent class, and weighted least squares computations resulted in identical multi-variate model-data fits with equivalent chi-square values. Moreover, the application of these three methodologies also produced identical measures of the degree of agreement at each point in time and for the multivariate average. The multivariate methods that were developed also included procedures for measuring the probability of agreement for a single response classification or subset of classifications from a larger set. In addition, procedures were developed to analyze occurrences of systematic observed disagreement within the multivariate tables. The consistency of dichotomous and polytomous classifications over repeated assessments of the identical examinees was also suggested as a means of conceptualizing criterion-referenced reliability. By applying the univariate and multivariate models described, the reliability of these classifications across repeated testings could be calculated. The procedures utilizing the log linear, latent structure, and weighted least squares concepts for the purpose of measuring agreement have the advantages of (1)yielding a coefficient of agreement that varies between zero and one and measures agreement in terms of the probability that the observers' judgements will agree, as estimated under a quasi-equiprobability or quasi-independence model, (2)correcting for the proportion of "chance" agreement, and (3) providing a directly interpretable coefficient of "no agreement." Thus, these multivariate procedures may be regarded as a more refined psychometric technology for measuring inter-observer agreement and criterion-referenced test reliability.

Thesis (Ph. D.)--University of Texas Health Science Center at Houston, School of Public Health, 2007. / Includes bibliographical references (leaves 58-59).

Thesis (Ph. D.)--University of California, San Diego and San Diego State University, 2007. / Title from first page of PDF file (viewed May 29, 2007). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 99-105).

Thesis (Ph. D.)--University of Washington, 2006. / Vita. Includes bibliographical references (p. 115-121).

Orientador: Mario Jino / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-15T04:58:41Z (GMT). No. of bitstreams: 1 Silva_DanielGuerreiroe_M.pdf: 2351174 bytes, checksum: 7f8ba90b6462fe7be00711143e365482 (MD5) Previous issue date: 2009 / Resumo: O planejamento das atividades de teste tem papel essencial para qualquer equipe independente de testes que realize testes de diferentes sistemas de software, desenvolvidos por diferentes equipes de desenvolvimento. Dado que o esforço empreendido no processo de testes pode chegar até a metade do esforço total de desenvolvimento de um sistema, estimar adequadamente o esforço de testes pode evitar custos desnecessários e contribuir para a boa qualidade dos produtos. Para superar este desafio, ferramentas de aprendizado de máquina têm sido usadas em pesquisa para estimar esforço e para solucionar outros problemas de engenharia de software, principalmente porque eles constituem uma classe de problemas complexos com muitas limitações à sua solução por abordagens matemáticas clássicas. Este trabalho estuda a aplicação das ferramentas de aprendizado de máquina - redes neurais artificiais e máquinas de vetor de suporte - e de ferramentas de seleção de variáveis na solução do problema de estimar esforço de execução de testes funcionais. Um estudo do processo de execução de testes é desenvolvido e são conduzidos experimentos em duas bases de dados reais com o objetivo de propor uma metodologia adequada para abordar sistematicamente o problema, tanto em termos de qualidade de resultados como em praticidade de uso. As principais contribuições deste trabalho são: a proposta de realizar a seleção de variáveis para a síntese da base de dados; a adoção de um modelo de rede neural treinada por uma função custo assimétrica; e um estudo comparativo de desempenho dos modelos preditores / Abstract: Planning and scheduling of testing activities play a key role for any independent test team that performs tests for different software systems, produced by different development teams. Since the effort that is applied in the test process can amount to up to half of the total effort of software development, adequate estimation of test effort can prevent unnecessary costs and improve the quality of delivered products. To overcome this challenge, machine learning tools have been used in research to estimate effort and to solve other software engineering problems, mainly because they constitute a class of complex problems with many limitations to their solution by classical mathematical approaches. This work studies the application of machine learning tools - artificial neural networks and support vector machines - and variable selection tools to solve the problem of estimating the execution effort of functional tests. An analysis of the test execution process is done and experiments are performed with two real databases aimed at proposing a suitable methodology to systematically tackle this problem, considering both the quality of results and ease of application. The main contributions of this work are: the proposal of applying variable selection for database synthesis; the adoption of an artificial neural network trained with an asymmetric cost function; and a comparative study of performance with the predictive models / Mestrado / Engenharia de Computação / Mestre em Engenharia Elétrica