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Browse Dissertations and Theses  Statistics by Title
Now showing items 120 of 131

(1989)This work deals with a decisiontheoretic evaluation of pvalue rules. A test statistic is judged on the behavior of its pvalue with the loss function being an increasing function G of the pvalue.
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(1996)The identifiability and estimability of the parameters for the Unified Cognitive/IRT Model are studies. A calibration procedure for the Unified Model is then proposed. This procedure uses the marginal maximum likelihood ...
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(20100820)The statistical inference based on the ordinary least squares regression is suboptimal when the distributions are skewed or when the quantity of interest is the upper or lower tail of the distributions. For example, the ...
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(2000)Using results from He & Shao (2000), a proof of the consistency and asymptotic normality of item parameter estimates obtained from the Marginal Maximum Likelihood Estimation (Bock & Lieberman, 1970) procedure as both the ...
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(1989)In many areas of application of statistics one has a relevent parametric family of densities and wishes to estimate the density from a random sample. In such cases one can use the family to generate an estimator. We fix a ...
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(1989)Many authors, for example, Fisher (1950), Pearson (1938), Birnbaum (1954), Good (1955), Littell and Folks (1971, 1973), Berk and Cohen (1979), and Koziol, Perlman, and Rasmussen (1988), have studied the problem of combining ...
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(20120201)Bayesian inference provides a flexible way of combiningg data with prior information. However, quantile regression is not equipped with a parametric likelihood, and therefore, Bayesian inference for quantile regression ...
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(2002)This thesis presents a progression from theory development to realdata application. Chapter 1 gives a literature review of other psychometric models for formative assessment, or cognitive diagnosis models, as an introduction ...
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(1993)We consider the problem of regressing a dichotomous response variable on a predictor variable. Our interest is in modelling the probability of occurrence of the response as a function of the predictor variable, and in ...
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(20110525)The latent class model (LCM) is a statistical method that introduces a set of latent categorical variables. The main advantage of LCM is that conditional on latent variables, the manifest variables are mutually independent ...
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(20110525)Quantile regression, as a supplement to the mean regression, is often used when a comprehensive relationship between the response variable and the explanatory variables is desired. The traditional frequentists’ approach ...
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(2007)Clustering and classification have been important tools to address a broad range of problems in fields such as image analysis, genomics, and many other areas. Basically, these clustering problems can be simplified as two ...
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(2000)To effectively build a regression model with a large number of covariates is no easy task. We consider using dimension reduction before building a parametric or spline model. The dimension reduction procedure is based on ...
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(2000)Motivated by consulting in infrastructure studies, we consider the estimation and inference for regression models where the response variable is bounded or censored. In these conditions, least squares methods are not ...
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(2006)The classical approaches to clustering are hierarchical and kmeans. They are popular in practice. However, they can not address the issue of determining the number of clusters within the data. In this dissertation, we ...
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(1991)Consider the model $y\sb{lj} = \mu\sb{l}(t\sb{j})$ + $\varepsilon\sb{lj}$, $l = 1,..,m$ and $j = 1,..,n,$ where $\varepsilon\sb{lj}$ are independent mean zero finite variance random variables. Under the above setting we ...
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(1990)Twostage Bayes procedures, also known as Bayes double sample procedures, for estimating the mean of exponential family distributions are given by Cohen and Sackrowitz (1984). In their study, they develop double sample ...
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(2004)The flexible forms of nonparametric IRT models make test equating more challenging. Though linear equating under parametric IRT models is obvious and appropriate, it might not be appropriate for nonparametric models. Two ...
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Now showing items 120 of 131