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 114 for “"regression methods"”.
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Robust regression methods for insurance risk classification
… Currently, insurance companies mainly use ad-hoc methods for risk classification, more often based on the type of expenses covered than on the distribution of the corresponding losses. The selection of classification variables is also, in general, based on rate-making variables rather than on an …
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Semiparametric Regression Methods with Covariate Measurement Error
… with error. When mismeasured data is used in a regression analysis, not accounting for the measurement error can lead to incorrect inference about the relationships between the covariates and the response. We investigate measurement error in the covariates of two types of regression models. For …
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Robust Regression Methods For Massively Decayed Intelligence Data
… of vital importance. </p> <p>Using Monte Carlo methods, three regression techniques (ordinary least squares, least-trimmed, and maximum likelihood) were tested against different data decay models presumed to be found in homeland security research studies in order to test whether these techniques …
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Quantile Regression Methods for Recursive Structural Equation Models
… the peers outcome measurement and the quantile regression estimation method. It is found that better performance by lower attainment peers has a significant positive effect on performance of all students in both language and math. This effect is most pronounced at the lower conditional quantiles …
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Exploring nonlinear regression methods, with application to association studies
The field of nonlinear regression is a long way from reaching a consensus. Once a method decides to explore nonlinear combinations of predictors, a number of questions are raised, such as what nonlinear combinations to permit and how best to search the resulting model space. Genetic Association …
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Prediction Intervals: The Effects and Identification of Sparse Regions for Nonparametric Regression Methods
… 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 regression …
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Spatial regression methods capture prediction uncertainty in species distribution model projections through time
… is largely ignored, perhaps because many current methods have been shown to produce biased estimates. Failure to account for spatial autocorrelation (SAC) of residual error explains much of this bias. Generalized linear mixed models (GLMM) have the ability to account for SAC through the inclusion …
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Spatial regression methods capture prediction uncertainty in species distribution model projections through time
… is largely ignored, perhaps because many current methods have been shown to produce biased estimates. Failure to account for spatial autocorrelation (SAC) of residual error explains much of this bias. Generalized linear mixed models (GLMM) have the ability to account for SAC through the inclusion …
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Regression Methods for Categorical Dependent Variables: Effects on a Model of Student College Choice
… dependent variables with the classical linear regression model (CLRM) violates many of the model's assumptions and may result in biased estimates (Long, 1997; O'Connell, Goldstein, Rogers, & Peng, 2008). Many dependent variables of interest to educational researchers (e.g., professorial rank, …
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Penalized Regression Methods with Application to Generalized Linear Models, Generalized Additive Models, and Smoothing
Recently, penalized regression has been used for dealing problems which found in maximum likelihood estimation such as correlated parameters and a large number of predictors. The main issues in this regression is how to select the optimal model. In this thesis, Schall’s algorithm is proposed as an …
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The estimation of missing values in hydrological records using the EM algorithm and regression methods
… objective of this thesis is to review existing methods for estimating missing values in rainfall records and to propose a number of new procedures. Two classes of methods are considered. The first is based on the theory of variable selection in regression. Here the emphasis is on finding …
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Analysis of the Repeated Measurement Data using the Hierarchical Clustering and Nonlinear Regression Methods in Asthma Pharmacogenetic Study
OBJECTIVE: We examined several analytic methods for predicting therapeutic response to drug in Korea asthmatics and tried to develop a clinical model to predict the drug response to asthma drug. BACKGROUND: Long acting β2-agonists (LABA) is the most powerful bronchodilator and inhaled …
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Penalised regression for high-dimensional data: an empirical investigation and improvements via ensemble learning
… exceeds the sample size n. Penalised likelihood methods are widely used to tackle regression problems in these high-dimensional settings. In this thesis, we carry out an extensive empirical comparison of the performance of popular penalised regression methods in high-dimensional settings and …
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Comparing single-level and multi-level regression models in analysing Rasch measures of numeracy
… the empirical differences between two competing regression methods used to produce value-added performance indicator information for the monitoring of school effectiveness, and the practical consequences for schools when using this information for school improvement and accountability purposes. …
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Covariate-adjusted ROC regressions and the extensions in trend tests.
… 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 motivation for the dissertation is …
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Telecommunications and Regulatory Reform: Methods and Applications
… US telecommunications industry using quantile regression methods. Under certain assumptions such a relationship is used to measure the welfare impact of infrastructure development. It also provides a quantification of the revenue incentives firms have to invest in additional infrastructure. We …
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Distribution and abundance of Cape hakes (Merluccius capensis and Merluccius paradoxus) in relation to environmental variation in the Southern Benguela system
… Generalized Additive Models (GAM), nonparametric regressions without the assumptions of normality or linearity of traditional regression methods, were used to test the hypothesis that trends in hake (M. paradoxus and M. capensis) distribution and abundance are related to ocean environmental …
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Long term seasonal and annual changes in rainfall duration and magnitude in Luvuvhu River Catchment, South Africa
… (1965- 2015), using Mann Kendal (MK), linear regression (LR) and quantile regression methods. The MK, LR and quantile regression methods have indicated dominance of decreasing trends of the annual, seasonal rainfall and duration of seasonal rainfall although they were not statistically …
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Effects of Students' Characteristics and Locus of Control on Their Satisfaction with Online Distance Education Experience
… Pearson correlations and Multiple Linear Regression methods were used to detect the correlations and analyze the relationships among different variables. Results of the study did not find correlations between students\' online satisfaction and their locus of control, as well as their …
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Essays on Quantile Regression for Dynamic Panel Data Models
The third chapter develops penalized quantile regression methods for dynamic panel data with fixed effects. We consider a penalized strategy designed to improve the properties of the dynamic panel data quantile regression instrumental variables estimator. The penalty involves l1 shrinkage of the …
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