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 37 for “"Nonparametric methods"”.
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Assessing Multivariate Heritability through Nonparametric Methods
… estimate is extremely difficult. This study uses nonparametric methods, namely the randomization test and the bootstrap, to obtain both a measure of the extremity of the observed heritability and an assessment of the uncertainty.
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Nonparametric Methods in Financial Time Series Analysis
… features of data. This thesis develops some new nonparametric tools that can be exploited in this context; the efficacy and validity of their use are supported by computational advancements and surging availability of large/complex (`big') data sets. Chapter 1 investigates the conditional first …
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Semiparametric and Nonparametric Methods for Complex Data
… 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 identify important …
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Pricing 2-colour rainbows : nonparametric methods using copulae
… marginal risk-neutral distributions were derived nonparametrically from each assets option price spread. This was achieved in a very simple manner by assuming that, for each of the underlying assets in question, a continuum of option prices exist. Cubic splines were used to fit this continuum to …
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Nonparametric Methods for Analysis and Modeling of Complex Multivariate Distributions
… ``parametric'' statistical models and methods are limited, either in their ability to capture nuances that cannot be generated by low dimensional models or in applying restrictive assumptions to inferential procedures that are rarely met. In this work, we present three novel …
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Some Nonparametric Methods for Clinical Trials and High Dimensional Data
… propose an empirical likelihood based method to nonparametrically adjust for baseline covariates in randomized clinical trials and in chapter 3, I develop a survival analysis framework for multivariate K-sample problems. (I): Covariate adjustment is an important tool in the analysis of randomized …
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Semiparametric and nonparametric methods for the analysis of panel count data
Panel count data are one type of event-history data concerning recurrent events. Ideally for an event-history study, subjects should be monitored continuously, so for the events that may happen recurrently over time, the exact time of each event occurrence is recordable. Data obtained in such cases …
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Efficient Bayesian Nonparametric Methods for Model-Free Reinforcement Learning in Centralized and Decentralized Sequential Environments
… to accommodate new experience. Bayesian nonparametric methods (BNPMs) both allow the complexity of models to be adaptive to data, and provide a principled way for discovering and representing new knowledge.</p><p>In this thesis, we investigate approaches for RL in centralized and …
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Robust and Nonparametric Methods for Topology Error Identification and Voltage Calibration in Power Systems Engineering
There is a growing interest in robust and nonparametric methods with engineering applications, due to the nature of the data. Here, we study two power systems engineering applications that employ or recommend robust and nonparametric methods; topology error identification and voltage calibration. …
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Semi-Parametric Techniques for Multi-Response Optimization
… when the user's model is incorrectly specified. Nonparametric methods have been suggested as an alternative, yet they often result in highly variable estimates, especially for sparse data with a small sample size which are the typical properties of traditional RSM experiments. Therefore, in this …
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Bayesian nonparametric approaches for reinforcement learning in partially observable domains
… of stochastic systems using Bayesian nonparametric statistics. Bayesian nonparametric methods allow the sophistication of a representation to scale gracefully with the complexity in the data. We show how the representations learned using Bayesian nonparametric methods result in better …
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A Random-Linear-Extension Test Based on Classic Nonparametric Procedures
Most distribution free nonparametric methods depend on the ranks or orderings of the individual observations. This dissertation develops methods for the situation when there is only partial information about the ranks available. A random-linear-extension exact test and an empirical version of the …
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Semiparametric Techniques for Response Surface Methodology
… in serious bias of the estimated response. Nonparametric methods have been suggested as an alternative as they can capture structure in the data that a misspecified parametric model cannot. Yet nonparametric fits may be highly variable especially in small sample settings which are common in …
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Prediction Intervals: The Effects and Identification of Sparse Regions for Nonparametric Regression Methods
… this work, we provide an overview of different nonparametric methods for prediction interval estimation and investigate how well they perform when making predictions in sparse regions of the predictor space. This sparsity is an extension to the more common concept of extrapolation in linear …
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Confidence Interval Estimation of Cumulative Incidence for Clustered Competing Risks
… of the primary outcome occurring. Traditional methods of estimating the cumulative incidence for an outcome and its associated confidence interval under competing risks do not account for the effect of clustering. This may cause incorrect estimation of confidence intervals because outcomes …
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A radial basis function approach to pricing and hedging options incorporating transaction costs
Nonparametric methods of pricing options have been available for some time, providing a viable alternative to traditional parametric methods. A general class of methods known as learning networks has been making significant inroads in option pricing literature. This thesis will adopt McLoone's …
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Censored Regression Models With Applications to Infrastructure Degradation Studies
… 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 using Student's t distribution instead of the normal. Thus additional flexibility is achieved varying the …
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Statistical methods for gene set based significance analysis.
… the existing approaches have largely relied on nonparametric methods and require repeated computation of permutation and resampling data to assess the significance of a gene set. In this dissertation, we study parametric approaches for GSEA by formulating the enrichment analysis into a simple …
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Model-robust quantal regression
… and cannot be assumed by the user, one can use nonparametric methods, such as kernel regression or local linear regression, to estimate the dose-response curve, effective doses, and confidence intervals. This research proposes another alternative to analyzing quantal dose-response data called …
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Multivariate analysis of the effect of source and supply and carrier on processing and shipping times for issue priority group one requisitions
… models, Generalized Linear Models (GLMs) and nonparametric methods to explore the structure of the historical requisition datasets. OLS linear models were found to be inadequate, but both the GLMs and nonparametric tests proved to be valid and yielded results from which inferences could be …
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