We are inviting IDEALS users, both people looking for materials in IDEALS and those who want to deposit their work, to give us feedback on improving this service through an interview. Participants will receive a $20 VISA gift card. Please sign up via webform. # Browse Dissertations and Theses - Statistics by Title • (1989) This work deals with a decision-theoretic evaluation of p-value rules. A test statistic is judged on the behavior of its p-value with the loss function being an increasing function G of the p-value. application/pdf PDF (1MB) • (1959) application/pdf PDF (1MB) • (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 ... application/pdf PDF (4MB) • (2010-08-20) The statistical inference based on the ordinary least squares regression is sub-optimal when the distributions are skewed or when the quantity of interest is the upper or lower tail of the distributions. For example, the ... application/pdf PDF (1MB) • (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 ... application/pdf PDF (5MB) • (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 ... application/pdf PDF (4MB) • (1967) application/pdf PDF (1MB) • (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 ... application/pdf PDF (7MB) • (2012-02-01) 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 ... application/pdf PDF (446kB) • (2002) This thesis presents a progression from theory development to real-data application. Chapter 1 gives a literature review of other psychometric models for formative assessment, or cognitive diagnosis models, as an introduction ... application/pdf PDF (9MB) • (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 ... application/pdf PDF (6MB) • (2011-05-25) 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 ... application/pdf PDF (5MB) • (2011-05-25) 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 ... application/pdf PDF (374kB) • (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 ... application/pdf PDF (2MB) • (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 ... application/pdf PDF (4MB) • (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 ... application/pdf PDF (3MB) • (2006) The classical approaches to clustering are hierarchical and k-means. 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 ... application/pdf PDF (2MB) • (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)
Two-stage 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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