Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 1567 for “"Regression models"”.
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Robust mixtures of regression models
… two projects that are related to robust mixture models. In the robust project, we propose a new robust mixture of regression models (Bai et al., 2012). The existing methods for tting mixture regression models assume a normal distribution for error and then estimate the regression param- eters by …
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Regression Models for Paired Comparisons
… employed in the middle of the 19th Century, many models have been developed over the years to analyze data from such experiments, for instance the Thurstone and Bradley-Terry-Luce models. This dissertation proposes an integrative framework in which the vast majority of extant scaling procedures …
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Variational Approximation for Complex Regression Models
… has resulted in the need for more flexible models and fast computational approximations. My thesis reflects these themes by considering some very flexible regression models and developing fast variational approximation methods for fitting them under a Bayesian framework. Models considered …
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Portfolio construction using index regression models
… by Hossain, Troskie and Guo (2005b). These models are extended to the multi index framework. We then empirically investigate the impact of the models on portfolio creation over an extensive data set. Next we extend these models by modelling the regression residuals as ARMA and GARCH(l, 1) …
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Properties Of Logistic Regression Models With Correlated Observations
The correlated logistic regression model, a new model for correlated binary observations in the presence of covariates, is introduced and its relationship with other logistic models established. Comparisons are made with other models for correlated binary outcomes, particularly those that allow …
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The Doubly Inflated Poisson and Related Regression Models
… two Doubly Inflated Poisson (DIP) probability models, DIP (<em>p</em>, λ) and DIP (<em> p</em>1, <em>p</em>2, λ), are discussed for situations where there is another inflated value <em>k</em> > 0 besides the inflated zeros. The distributional properties such as identifiability, moments, and …
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Bivariate Doubly Inflated Poisson and Related Regression Models
… calculations. We also discuss the BDIP regression model that incorporates covariates into the BDIP model. We illustrate applicability of the BDIP regression model to analyze a subset of the Australian health survey data. Finally we conclude with an introduction to BDIP2 distribution, …
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Sparse functional regression models: minimax rates and contamination
In functional linear regression and functional generalized linear regression models, the effect of the predictor function is usually assumed to be spread across the index space. In this dissertation we consider the sparse functional linear model and the sparse functional generalized linear models …
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Poisson regression models for interval censored count data.
In this dissertation, we develop Bayesian models for interval censored Poisson counts in the presence of zero inflation and missing data. As a motivating example, we consider data arising from a Human Immunodeficiency Virus (HIV) vaccine trial featuring imprecise counts, missing data, and an …
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Some sequential estimation problems in logistic regression models
… be a random sample satisfying a logistic regression model; that is, for each i, log($P(Y\sb{i}$ = $1\vert{\bf X}\sb{i})/P(Y\sb{i}$ = 0$\vert{\bf X}\sb{i})\rbrack$ = ${\bf X}\sbsp{i}{T}\beta\sb0,$ where $Y\sb{i}\in\{$0,1$\},$ ${\bf X}\sb{i}\in{\bf R}\sp{p}$ and $\beta\sb0\in{\bf R}\sp{p}$ …
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Hypothesis testing procedures for non-nested regression models
… families of hypotheses. Two hypothesized models are said to be non-nested when one model is neither a restricted case nor a limiting approximation of the other. These non-nested hypotheses cannot be tested using conventional likelihood ratio procedures. In recent years, however, several …
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Empirical Bayes procedures in time series regression models
… estimators for the coefficients in time series regression models are presented. Due to the uncontrollability of time series observations, explanatory variables in each stage do not remain unchanged. A generalization of the results of O'Bryan and Susarla is established and shown to be an …
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Modified mean and quantile regression models for citation analysis
… and countries. When fitting statistical models to citation count data it frequently occurs that the number of uncited articles (0s) differs from that expected under the best fitting model. This problem might be remedied by fitting a zero-modified, i.e. zero-inflated or a zero-deflated, …
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Detection of Outliers and Influential Observations in Regression Models
<p>Observations arising from a linear regression model, lead one to believe that a particular observation or a set of observations are aberrant from the rest of the data. These may arise in several ways: for example, from incorrect or faulty measurements or by gross errors in either response or …
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Estimation of Parameters in Replicated Time Series Regression Models
<p>The time series regression model was widely studied in the literature by several authors. However, statistical analysis of replicated time series regression models has received little attention. In this thesis, we study the application of quasi-least squares, a relatively new method, to estimate …
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Censored Regression Models With Applications to Infrastructure Degradation 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 appropriate, although they are widely used. This dissertation develops a generalization of the Tobit censored regression model …
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Efficient Estimation of Linear Regression Models With Autocorrelated Errors
Made available in DSpace on 2014-12-14T06:06:04Z (GMT). No. of bitstreams: 1 7804091.pdf: 5452448 bytes, checksum: 10c8e65fdd1bc2bcffec86dfdd8aa565 (MD5) Previous issue date: 1977
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Bayesian variable selection in high dimensional censored regression models
… selection problem in a high-dimensional censored regression model that can handle gene expression data with hundreds of thousands of features. We propose an EM-like iterative algorithm for accelerated failure models (AFT models) with censored survival data under no distributional assumption. …
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