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 16 of 16 for “"non-parametric regression"”.
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ENHANCING pm2.5 AIR POLLUTION ANALYSIS IN BEIJING: A TRANSITION FROM NON-PARAMETRIC REGRESSION TO ADVANCED MACHINE LEARNING METHODOLOGY
… in Beijing, transitioning from traditional non-parametric methods to the advanced Random Forest Plus (RF+) methodology. The original research, which used non-parametric regression and bandwidth selection frequency, to assess PM2.5 levels as well as feature importance, may result in …
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Data-driven Process-Structure-Property Models for Additive Manufactured Ni-base Superalloys
… for the establishment of the PSP models. Both parametric and non-parametric regression techniques are employed to construct models to illustrate the suitability of the different ML methods. From well-established regression techniques, non-parametric support vector regression (SVR), and …
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Adaptive Data Representation and Analysis
… it illustrates two proposed algorithms that use non-parametric regression to reveal the underlying os- cillatory patterns of the targeted 1-dimensional signal, as well as to estimate the instantaneous information, e.g., instantaneous frequency, phase, or amplitude func-</p><p>tions, by a …
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Self learning strategies for experimental design and response surface optimization
… approach which combines concepts from nonlinear optimization, non-parametric regression, statistical analysis, and response surface optimization. The proposed strategies uses the information gained from the previous experiments to design the subsequent experiment by simultaneously …
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Functional Data Models for Raman Spectral Data and Degradation Analysis
… difference. In the second part, we propose a non-parametric regression procedure to obtain a locally sparse estimate of functional contrast. Our work is motivated by a biomedical study using Raman spectroscopy to monitor hemodialysis treatment near real-time. With contrast test and sparse …
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Data-Driven Operations in Changing Environments
… state-of-the-art dynamic regret bounds for non-stationary bandit and reinforcement learning settings. These settings capture applications such as advertisement allocation, dynamic pricing, and inventory control in changing environments. Our main contribution is a general algorithmic recipe …
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On The Performance Of The Maximum Likelihood Over Large Models
This dissertation investigates non-parametric regression over large function classes, specifically, non-Donsker classes. We will present the concept of non-Donsker classes and study the statistical performance of Least Squares Estimator (LSE) --- which also serves as the Maximum Likelihood …
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Evidence Synthesis and targeting further research for adherence and stratification in health economic evaluations
… effects is summarised. Bayesian model-based meta-regression is used to explore stratification on one or two measure of treatment severity. The value of collecting further data on factors relating to stratification has been explored by using and extending recent non-parametric regression methods. …
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The Influence of dust deposition, carbonates and erosion on the formation of Clanwilliam heuweltjies
Heuweltjies (Afrikaans for "little hills") are non-anthropogenic, regularly dispersed earth mounds up to 32 meters in diameter and approximately 1.4 meters in height, that dot about 25% of the land surface of south-western southern Africa. The zoogenic "termite" hypothesis has been widely accepted …
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Modelling British Columbia’s ecosystems and avian richness using landscape-scale indirect indicators of biodiversity
… our species-modelling goal we employ a flexible non-parametric regression tree model (Random Forests) to establish the power of landscape-scale indicators (productivity, ambient energy, and heterogeneity) to predict the spatial distribution of breeding bird richness and establish the dominant …
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Essays on Experimental Design
… discrete latent type, which can be estimated by regression clustering methods. In this paper, I show that such models can be misspecified, even when the panel has significant discrete cross-sectional structure. Motivated by this finding, I generalize previous approaches to discrete unobserved …
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Continuous-Time Models of Arrival Times and Optimization Methods for Variable Selection
… concern</p><p>the development of Bayesian semi-parametric models for arrival times. Chapter 2</p><p>considers Bayesian inference for a Gaussian process modulated temporal inhomogeneous Poisson point process, made challenging by an intractable likelihood. The intractable likelihood is …
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Development of risk-based groundwater operating rules: a case study of Siloam Village, South Africa
… were infilled and/or extended using Output Error-Nonlinear Hammerstein Weiner (OE-NLHW) and non-parametric regression (NPR), respectively. Performances of these models were based on relative error (RE), correlation coefficient (COR), root mean square error (RMSE), coefficient of determination (R2) …
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Measurement and modelling of water and sediment fluxes in meso-scale dryland catchments
… (SRCs), generalized linear models (GLMs) and non-parametric regression using Random Forests (RF) and Quantile Regression Forests (QRF). The observed SSCs are highly variable and range over six orders of magnitude. For these data, traditional SRCs performed poorly, as did GLMs, despite …
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Epidemiology of diabetes and related mortality: early screening, socioecological determinants, and the value of prevention
… titled “Diabetes Risk Prediction: Multivariate Nonlinear Interaction Approach,” I argue that the success in preventing or delaying the incidence of type 2 diabetes and subsequent complications depend on the early detection of undiagnosed cases and identifying people at high-risk. However, early …
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A Bayesian Approach to Dose-Response Assessment and Drug-Drug Interaction Analysis: Application to In Vitro Studies
… First, we developed a hierarchical meta-regression dose-response model that accounts for various sources of variation and uncertainty and allows one to incorporate knowledge from prior studies into the current analysis, thus offering a more efficient and reliable inference. Second, in the …