Global ETD Search
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Showing 1 to 18 of 18 for “"Semiparametric model"”.
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Essays on Semiparametric Methods in Finance
… can be recorded. Unlike previous studies that model the time between transactions completely 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 …
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Longitudinal Growth Charts Based on Semi-Parametric Quantile Regression
… based on 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 …
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Sample Size Calculation Based on the Semiparametric Analysis of Short-term and Long-term Hazard Ratios
… test statistic for the Cox proportional hazards model. Such methods, however, are inappropriate when the proportional hazards assumption is violated. We develop methods to calculate the sample size based on the semiparametric analysis of short-term and long-term hazard ratios. The methods are …
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Cure Rate Model with Spline Estimated Components
… The existing methods for cure rate models have been limited to parametric and semiparametric models. More specifically, the hazard function part is estimated by parametric or semiparametric model where the effect of covariate takes a parametric form. And the cure rate part is often …
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Some Advanced Model Selection Topics for Nonparametric/Semiparametric Models with High-Dimensional Data
Model and variable selection have attracted considerable attention in areas of application where datasets usually contain thousands of variables. Variable selection is a critical step to reduce the dimension of high dimensional data by eliminating irrelevant variables. The general objective of …
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Advanced Topics in Introductory Statistics
… the simplicity of low-dimensional parametric models. In the context of genomics studies, we propose a frequentist-assisted-by-Bayes (FAB) method for conducting hypothesis tests for the means of normal models when auxiliary information about the means is available. If the auxiliary information …
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Statistical methods for genetics and genomics studies
… through random genetic marker data and then modeling the relationship between trait values, genotypic scores at a candidate marker, and genetic background variables through a semiparametric model, where the error distribution for fully observed data or the baseline survival function for …
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A modulated renewal Hawkes process and its application to modelling extreme mid-price drops on cryptocurrencies
… for the modulated renewal Hawkes process model is not trivial. However, by modifying the likelihood evaluation algorithm for renewal Hawkes process in Chen & Stindl (2018), we are able to propose an algorithm to evaluate the exact likelihood of the modulated renewal Hawkes process model. …
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Flexible models and methods for longitudinal and multilevel functional data
… methods for functional mixed effects models with varying coefficients. This work is motivated by a clinical study of Complicated Grief (Shear et al. 2005). In the Complicated Grief Study, patients receive active treatment during a treatment period and then enter a follow-up period …
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Bayesian regularized quantile mixed models for longitudinal studies
… novel Bayesian regularized quantile mixed effect models to tackle these challenges. In the first project, we have proposed a Bayesian variable selection method in the mixed effect models for longitudinal lipidomics studies. To dissect important lipid-environment interactions, our model can …
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Statistical Methods for Genetic Pathway-Based Data Analysis
… data. One is to propose a semi- parametric model for identifying pathways related to the zero inflated clinical outcomes; the other is to propose a multilevel Gaussian graphical model for exploring both pathway and gene level network structures. For the first problem, we develop a …
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A bayesian approach to wireless location problems
… and hierarchical Bayesian graphical models that use prior knowledge about physics of signal propagation, as well as different modifications of Bayesian bivariate spline models. The hierarchical Bayesian model that incorporates information about locations of access points achieves …
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Measuring the effects of online advertising on human behavior using natural and field experiments
… on the Yahoo! Front Page, aided by a flexible semiparametric model, to identify the causal effects of display ad frequency on internet users' responses as measured at the individual level by clicks and new-account sign-ups. Performance is heterogeneous regarding frequency and clickability; some …
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Essays in Econometrics
… estimation and inference in moment condition models, one on semiparametric estimation in the presence of missing data, and one on survival analysis with competing risks data. The first chapter considers estimation of moment condition models when some data are missing. The inverse probability …
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Statistical Methods for Multi-type Recurrent Event Data Based on Monte Carlo EM Algorithms and Copula Frailties
… Chapter 2 develops a multi-type recurrent events model with multivariate Gaussian random effects (frailties) for the intensity functions. In this chapter, we present nonparametric baseline intensity functions and a multivariate Gaussian distribution for the multivariate correlated random effects. …
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Statistical Modeling of Longitudinal Medical Cost Data
… three aims. In Aim 1, we developed a two-stage semiparametric likelihood-based method to estimate the conditional distribution of longitudinal medical cost trajectory given the time of terminal event. The cost data is assumed normal, which does not reflect the reality. So, for Aim 2, we …
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Model Robust Regression Based on Generalized Estimating Equations
One form of model robust regression (MRR) predicts mean response as a convex combination of a parametric and a nonparametric prediction. MRR is a semiparametric method by which an incompletely or an incorrectly specified parametric model can be improved through adding an appropriate amount of a …
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Profile Monitoring with Fixed and Random Effects using Nonparametric and Semiparametric Methods
… The essential idea for profile monitoring is to model the profile via some parametric, nonparametric, and semiparametric methods and then monitor the fitted profiles or the estimated random effects over time to determine if there have been changes in the profiles. The majority of previous studies …