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 20 for “"Nonparametric Model"”.
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Building a Nonparametric Model After Dimension Reduction
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 a canonical correlation analysis on the predictor variables and a spline basis …
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Order Reduction, Identification and Localization Studies of Dynamical Systems
… the dynamics of practical bolted joints. By modeling the difference between the dynamics of the bolted structure and the corresponding unbolted one, one constructs a nonparametric model for the joint dynamics. Two applications are given with a bolted beam and a truss joint in order to show …
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Disk Diffusion Breakpoint Determination Using a Bayesian Nonparametric Variation of the Errors-in-Variables Model
… sample dependent and lacks precision.</p> <p>Model-based approaches were first proposed in 2000. These approaches model the underlying true relationship between the two tests and thus focuses on calibrating the probabilities of classification rather than the observed test results. Both a …
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Probabilistic Time-to-Event Modeling Approaches for Risk Profiling
… for machine learning to build statistical models to support clinical practice. Time-to-event analysis, also called survival analysis, stands as one of the most representative examples of such statistical models. Models for predicting the time of a future event are crucial for risk …
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Linear Mixed Model Robust Regression
Mixed models are powerful tools for the analysis of clustered data and many extensions of the classical linear mixed model with normally distributed response have been established. As with all parametric models, correctness of the assumed model is critical for the validity of the ensuing inference. …
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Tree-based Methods for Learning Probability Distributions
… Polya tree process, that is, a new Bayesian nonparametric model, equipped with a new flexible tree prior. With this new prior we can find trees that represent the distributional structures well, and the tree space is efficiently explored with a new sequential Monte Carlo algorithm. The new …
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Data Driven Nonparametric Detection
… 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 as semi-parametric models as an …
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Cure Rate Models with Nonparametric Form of Covariate Effects
… on development of spline-based hazard estimation models for cure rate data. Such data can be found in survival studies with long term survivors. Consequently, the population consists of the susceptible and non-susceptible sub-populations with the latter termed as "cured". The modeling of both the …
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Essays in applied econometrics
… for Brazil?: An Investigation Using Bayesian Model Averaging and Nonparametric Model Selection. Brazil has become one of the major emerging countries in the world, registering a promising development scenario. However, the income inequality in Brazil remains higher if compared to countries …
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Time-to-Event Modeling with Bayesian Perspectives and Applications in Reliability of Artificial Intelligence Systems
… three projects introducing the statistical models and model estimation methods that can be used in the reliability analysis of AI systems. The first project analyzes the recurrent events data from autonomous vehicles (AVs). A nonparametric model is proposed to study the reliability of AI …
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Bayesian time series models and scalable inference
With large and growing datasets and complex models, there is an increasing need for scalable Bayesian inference. We describe two lines of work to address this need. In the first part, we develop new algorithms for inference in hierarchical Bayesian time series models based on the hidden Markov …
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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 …
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Essays on Asset Pricing and Downside Risk
… downside. The consumption-based asset pricing model that emerges from this idea explains the main existing puzzles found within the asset pricing literature. These include the equity premium and the risk-free rate puzzles, the countercyclicality of the equity premium and the procyclicality of …
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Bayesian Applications in Financial Econometrics
… The three chapters apply both Bayesian nonparametric and parametric methods to financial market and macroeconomic time series. Chapter 1 extends popular discrete time short-rate models to include Markov switching of infinite dimension. This is a Bayesian nonparametric model that allows …
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Parametric and nonparametric identification of shell and tube heat exchanger mathematical model
Parametric and nonparametric models of a shell and tube heat exchanger are studied. Such models are very important because they provide information about controlling a system operation. Without the model, the control task would be difficult for tuning of controller. For many years, researchers have …
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Empirical investigation of nonlinear asset pricing kernel with human capital and housing wealth
… are captured by simple linear asset pricing models. They include Capital Asset Pricing Model (CAPM) and Fama-French threefactor model. However, the empirical study shows that the asset returns are fat tailed, that cannot be accurately predicted by normal distribution. Kurtosis and skewness …
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Nonparametric choice modeling : applications to operations management
… predictions, one uses what is called a choice model, which models each choice occasion as follows: given an offer set, a preference list over alternatives is sampled according to a certain distribution, and the individual chooses the most preferred alternative according to the sampled …
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Semiparametric Bayesian Approach using Weighted Dirichlet Process Mixture For Finance Statistical Models
… (DPM) has been widely used as exible prior in nonparametric Bayesian literature, and Weighted Dirichlet process mixture (WDPM) can be viewed as extension of DPM which relaxes model distribution assumptions. Meanwhile, WDPM requires to set weight functions and can cause extra computation burden. …
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Gaussian process models for SCADA data based wind turbine performance/condition monitoring
… process (GP) is a stochastic, nonlinear and nonparametric model whose distribution function is the joint distribution of a collection of random variables; it is widely suitable for classification and regression problems. GP is a machine learning algorithm that uses a measure of similarity …
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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 …