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 32 for “"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 overfitting, …
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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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On Logistic Regression Approach to Survival Data and Power Divergence Statistics for Life Tables
… (1988) proposes the use of standard logistic regression techniques to estimate hazard rates and survival curves from survival data. These techniques allow statisticians to use parametric regression modeling on survival data in a flexible way that provides both estimates and standard errors. In …
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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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The role of polls for election forecasting in German state elections
… to be customized to generate daily data to apply parametric regression based models. To forecast single vote shares in multi-party elections, the range of methods varies from basic methods like averaging over nonparametric regression based methods to dynamic linear models.
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Emergency Diesel-Electric Generator Set Maintenance and Test Periodicity
… tested by Fehr (2014), this research developed a parametric regression survival model of the reliability of modern diesel-electric generator sets in emergency standby duty as a function of maintenance, age, and cumulative run hours. A survival regression technique leveraging Cox’s (1972) methods …
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Nonparametric and semiparametric methods for interval-censored failure time data
… functions for different treatment groups and regression analysis. In this dissertation, we consider these research questions on 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 …
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Impact of data-dependent model selection on inference
… problem of accounting for model uncertainty in a parametric regression model with focus on the uncertainty involved in selection of the optimal transformation of a continuous predictor in the Cox proportional hazards model (Cox, 1972). I use the minimum AIC approach to select a posteriori the …
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Outlier Resistant Model Robust Regression
Parametric regression fitting (such as OLS) to a data set requires specification of an underlying model. If the specified model is different from the true model, then the parametric fit suffers to a degree that varies with the extent of model misspecification. Mays and Birch (1996) addressed this …
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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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Flexible Modellierung kategorialer Responsevariablen
Multicategorical regression models are an established tool in statistical data analysis. The present thesis extends common parametric regression models for nominal and ordinal responses to more flexible nonparametric approaches. In order to obtain a flexible form of the functional effects of …
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Adaptive Data Representation and Analysis
… 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 statistical …
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Self learning strategies for experimental design and response surface optimization
… 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 reducing the region of interest …
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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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Covariate-adjusted ROC regressions and the extensions in trend tests.
… covariate effects to model the ROC using parametric and semi-parametric regression methods. The latest addition to this ongoing investigation into problems of this type made use of the beta regression for placement values and the property that their CDF is the ROC curve. The underlying …
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Data-Driven Operations in Changing Environments
… modeling to develop a data-driven online non-parametric regression method. Numerical experiments show that our method is capable of reducing the forecasting error in terms of WMAPE (i.e., weighted mean absolute percentage error) and MSE (i.e., mean squared error) by more than 50% for AB InBev.
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Spatial-Longitudinal Bent-Cable Model with an Application to Atmospheric CFC Data
… of modelling spatial data. The bent-cable is a parametric regression model to study data that exhibits a trend change over time. It comprises two linear segments to describe the incoming and outgoing phases, joined by a quadratic bend to model the transition period. For spatial longitudinal …
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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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Sequential design augmentation with model misspecification
… A new methodology, based on a modified kernel regression procedure called HATLINK, is presented that incorporates model misspecification into the sequential augmentation of points in the new region. HATLINK is a combination of parametric and nonparametric regressions and is designed to perform …
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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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