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 101 for “"Semi-parametric."”.
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Essays on Semi-parametric Bayesian Econometric Methods
This dissertation consists of three chapters on semi-parametric Bayesian Econometric methods. Chapter 1 applies a semi-parametric method to demand systems, and compares the abilities to recover the true elasticities of different approaches to linearly estimating the widely used Almost Ideal demand …
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Semi-Parametric Techniques for Multi-Response Optimization
… models for each response are required. The parametric approach, a traditional RSM regression technique, which is inflexible and heavily relies on the assumption of well-estimated models for the response of interests, can lead to highly biased estimates and result in miscalculating optimal …
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Semi-Parametric Likelihood Functions for Bivariate Survival Data
… are provided, combining in a simple parametric form the dependence between the two random variables and a nonparametric likelihood function for the unknown random variable. Associated properties are studied and investigated and applications with simulated and real data are given.</p>
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Longitudinal Growth Charts Based on Semi-Parametric Quantile Regression
… two longitudinal quantile regression models, one parametric and one semi-parametric. Both models incorporate individual prior growth and other covariates without the assumption of normality. The semiparametric model is global in nature and accommodates varying measurement time intervals. Under …
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Three essays in semi-parametric modelling of time-varying distribution
… be time-varying and non-normal. Although various parametric and non-parametric approaches have been developed to model the time-varying nature and the non-normality, they still suffer from intrinsic limitations. This study proposes the dynamic modelling of the non-parametric distribution …
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Semi-Parametric Testing of Single-Nucleotide Polymorphism Effects On Continuous Outcome
… statistical model. In this thesis, we explored a semi-parametric method built upon kernel machines to include multiple SNPs in a model. The information of SNPs similarity calculated based on identity-by-state (IBS) algorithm was included in a kernel matrix. We evaluated the method via various …
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Simulation Comparisons of Semi-Parametric Accelerated Failure Time Model With Different Bandwidths
… the parameters and associated variances in the semi-parametric AFT model. The main aim of this thesis is to investigate the impact of the different bandwidths on the estimations in the profile likelihood method. Additionally, we also evaluate its performances according to the different sample …
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Semi-parametric models for response times and response accuracy in computerized testing
… for response times,however, mainly focus on parametric models that have the advantage of conciseness, but may suffer from a reduced flexibility to fit real data. This thesis presents two types of semi-parametric models that combine the flexibility of nonparametric modeling and the brevity as …
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Evaluating Time-varying Effect in Single-type and Multi-type Semi-parametric Recurrent Event Models
… scheme of the data in this study. Four semi-parametric models are explored: Andersen-Gill model, Andersen-Gill model with stratified baseline functions, frailty model, and frailty model with stratified baseline functions. I derived model estimation procedure and and conducted model …
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Frequentist-Bayesian Hybrid Tests in Semi-parametric and Non-parametric Models with Low/High-Dimensional Covariate
… test for the significant differences between non-parametric functions and the second one is to design a test allowing any departure of predictors of high dimensional X from constant. The implementation is also given in construction of the proposal test statistics for both problems. For the first …
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Statistical model on student performance in UTHM by using non-parametric, semi-parametric and parametric survival analysis
… such as the Kaplan-Meier (KM) method in the non-parametric method, Cox model in semi-parametric model and survival and AFT models in parametric model. The results revealed that students with STPM-entrance qualification had the highest survival rate compared to Diploma and Matriculation holders. …
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Efficient Bayesian inference for partially observed stochastic epidemics and a new class of semi-parametric time series models
… methodology for constructing a wide class of semi-parametric time series models. First, we introduce a general framework for the heterogeneously mixing stochastic epidemic models (HMSE) and we also review some of the existing methods of statistical inference for epidemic models. The …
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Parametric and Semi-Parametric Regression Estimation of the Effect of the United States Soybean Quality Attributes on Export Price
Results of both parametric and nonparametric regression estimations confirmed that some of the quality variables had a nonlinear relationship with export price. The study identified two possible sources of non-linearity of implicit price. The first was related to different degrees of competition in …
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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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Bayesian models for unmeasured confounder in the analysis of time-to-event data.
… data. We also provide both the Bayesian parametric and the semi-parametric "twin regression" approaches with distributional assumptions of an unmeasured confounding variable, and then we compare them with the naive model. This assumes we ignore the effect of the unmeasured confounder. To …
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Nonparametric and semiparametric methods for interval-censored failure time data
… two types of interval-censored data by using nonparametric and semi-parametric approaches. Three new procedures, which include a goodness-of-fit test for the proportional odds mode with interval-censored data, a nonparametric test for comparing two survival functions with interval-censored data …
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Data Driven Nonparametric Detection
… problems have been intensively studied under parametric and semi-parametric models with underlying distributions being fully or partially known, nonparametric scenarios are not well understood yet. This thesis mainly explores nonparametric models with unknown underlying distributions as well …
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Essays in financial economics
… investigate dynamic financial phenomena using semi-parametric competing risks models. The first essay analyzes the determinants of corporate defaults and mergers. The second essay investigates how sell-side analysts make recommendation revisions facing various incentives at different points in …
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Nuisance Parameter Estimation in Survival Models
… means. In particular, we illustrate that a non-parametric estimator of the conditional survival function improves the estimation of censored quantile regression when the semi-parametric assumptions of current methods are badly violated. Unfortunately, the non-parametric estimator is inefficient …
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Bayesian Measurement Error Modeling
… effort.</p><p> In the second part, we discuss a semi-parametric measurement error model based on P-spline with the help of a biomarker. The measurement error model is further incorporated into polytomous logistic regression models to infer interesting factor effects. The advantage of this method …
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