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Showing 1 to 20 of 118 for “"semiparametric"”.
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Semiparametric Inference
Semi-parametric and nonparametric modeling and inference have been widely studied during the last two decades. In this manuscript, we do statistical inference based on semi-parametric and nonparametric models in several different scenarios. Firstly, we develop a semi-parametric additivity test for …
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Semiparametric mixture models
… consists of three parts that are related to semiparametric mixture models. In Part I, we construct the minimum profile Hellinger distance (MPHD) estimator for a class of semiparametric mixture models where one component has known distribution with possibly unknown parameters while the other …
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Semiparametric Bayesian Count Data Models
… available in the data. Here we study and develop semiparametric count data models which can solve these problems. We have extended the Poisson distribution to account for overdispersion and/or zero inflation. Additionally we have incorporated corresponding components in structured additive form …
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Essays on Semiparametric Bayesian Regression
… so it provide a simple but powerful strategy for semiparametric Bayesian methods.
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Modern Methods in Semiparametric Statistics
… of interest to look beyond parametric models. Semiparametric models, defined in terms of infinite dimensional parameters, provides extra flexibility over potentially misspecified parametric models. The desire to construct estimators for parameters of interest that are optimal over the broad …
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On semiparametric regression and data mining
Semiparametric regression is playing an increasingly large role in the analysis of datasets exhibiting various complications (Ruppert, Wand & Carroll, 2003). In particular semiparametric regression a plays prominent role in the area of data mining where such complications are numerous (Hastie, …
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High Dimensional Inference for Semiparametric Models
In the literature, high dimensional inference refers to statistical inference when
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Semiparametric analysis of multivariate longitudinal data
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT AUTHOR'S REQUEST.] Longitudinal studies are conducted widely in fields such as agriculture and life sciences, business and industry, demography and other social sciences, medicine and public health. In longitudinal studies, individuals …
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Essays on Semiparametric Methods in Finance
… parametrically, in this paper we use the semiparametric survival model of Kooperberg, Stone, and Troung (1995). The primary objective of this paper is to examine how important trade characteristics are on the prices spacings and a measure of instantaneous volatility using the …
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Essays on identification and semiparametric econometrics
… of the first chapter is to propose a two-step semiparametric estimator for the model. In the first step the transition densities and policy function are estimated nonparametrically. In the second step, the parameters of the payoff function are estimated from the optimality conditions of the …
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Semiparametric Techniques for Response Surface Methodology
… settings which are common in RSM. Therefore, semiparametric regression techniques are proposed for use in the RSM setting. These methods will be applied to an elementary RSM problem as well as the robust parameter design problem.
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Semiparametric Bayesian Joint Model With Variable Selection
<p>In longitudinal studies, a popular model is the linear mixed model that includes fixed effects and subject specific random effects. In many clinical trials and other medical and reliability studies, we can often obtain repeated measurements or longitudinal data that includes survival or …
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Semiparametric Estimation of Binary Response Valuation Models
Chapter 3 presents a comparison of the welfare measures obtained through KSE and binary quantile regression. Results obtained indicate that KSE welfare measures are closer to the quantiles approach than the standard logit. However, the differences in the welfare measures suggest that the KSE fail …
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Semiparametric Characteristics-based Models of Asset Returns
… group” of arbitrage characteristics. A Dynamic Semiparametric Characteristics-based Model for Optimal Portfolio Selection This paper develops a two-step semiparametric methodology for portfolio weight selection for characteristics-based factor-tilt and factor-timing investment strategies. We …
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Semiparametric and Nonparametric Methods for Complex Data
… We have then provided several contributions to semiparametric and nonparametric methods for dealing with the following problems: the first is to propose a method for testing the significance of a functional association under the matched study; the second is to develop a method to simultaneously …
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Semiparametric Regression Methods with Covariate Measurement Error
In public health, biomedical, epidemiological, and other applications, data collected are often measured with error. When mismeasured data is used in a regression analysis, not accounting for the measurement error can lead to incorrect inference about the relationships between the covariates and …
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Semiparametric Methods for the Generalized Linear Model
… distribution function. In this dissertation, two semiparametric extensions of the GLM will be developed. In the first part of this dissertation, we have proposed a new model, called a semiparametric generalized linear model with a log-concave random component (SGLM-L). In this model, the estimate …
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Unified Ordinal Regression: Model Assessment and Semiparametric Analysis
In the last part of the thesis we discuss the methods for handling censored data for both categorical and continuous responses. We develop EM algorithms for censored data in general, and also develop a weighted least squares algorithm for cumulative link models for ordinal responses.
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Semiparametric instrumental variable methods for causal response models
… of nonparametric identifying assumptions and semiparametric estimators that provide a parsimoniuous summary of the results. The thesis consists of three essays presented in the form of chapters. The first chapter shows how to estimate linear and nonlinear causal response functions with …
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Semiparametric Varying Coefficient Models for Matched Case-Crossover Studies
Semiparametric modeling is a combination of the parametric and nonparametric models in which some functions follow a known form and some others follow an unknown form. In this dissertation we made contributions to semiparametric modeling for matched case-crossover data. In matched case-crossover …
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