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 339 for “"Quantile"”.
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Smooth regression quantile estimation
… focused on the local linear kernel regression quantile estimation. Different estimators within this class have been proposed, developed asymptotically and applied to real applications. I include algorithmdesign and selection of smoothing parameters. Chapter 2 studies two estimators, first a …
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Finite Population Quantile Estimators
… in sampling theory and a brief overview on quantile regression, two new quantile based estimators are introduced and some of their properties are examined. A proof for consistency of the marginal quantile estimator and for Bahadur-Expansion validity of the conditional quantile estimator is …
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Bayesian quantile linear regression
Quantile regression, as a supplement to the mean regression, is often used when a comprehensive relationship between the response variable and the explanatory variables is desired. The traditional frequentists’ approach to quantile regression was well developed with asymptotic theories and …
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Quantile Regression With Censored Data
Keywords. Survival Analysis; Quantile Regression; Kaplan-Meier; the Cox Proportional Hazards Model; Accelerated Failure Time Model.
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Quantile Regression for Panel Data
Chapter 3 illustrates the use of the penalized quantile regression estimator. Angrist et al. (2002) points out that the primary incentive effect of the Colombia's voucher program should be on those who are near the margin for passing on to the next grade because vouchers were renewable as long as …
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Quantile autoregression with censored data
Quantile autoregression (QAR) provides an alternative way to study asymmetric dynamics and local persistence in time series. It is particularly attractive for censored data, where the classical autoregressive models are unidentifiable without further parametric assumptions on the distributions. …
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Quantile regression and survival analysis
… of six chapters and focus on two topics: quantile regression and survival analysis. Firstly, direct use of regression quantiles to construct confidence intervals and confidence bands for conditional quantiles and prediction intervals for future response variables under homoscedastic linear …
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Statistical process control by quantile approach.
… mixture distributions. This thesis provides a quantile approach to deal with non-normal distributions, in order to construct median rankit control chart. Here, the quantile approach will also be used to calculate process capability index, average run length (ARL), multivariate control chart and …
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Conditional Quantile Estimation With Ordinal Data
… regression method for estimating the conditional quantiles of the ordinal response variable. By assuming a continuous latent variable underlying the observed ordinal response variable and utilizing the equivalence property of quantile regression we obtain the estimates of the conditional quantile …
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Bayesian empirical likelihood for quantile regression
… combiningg data with prior information. However, quantile regression is not equipped with a parametric likelihood, and therefore, Bayesian inference for quantile regression demands careful investigations. This thesis considers the Bayesian empirical likelihood approach to quantile regression. …
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Quantile Regression in a Varying Coefficient Model
Quantile regression extends the statistical quantities of interest beyond conditional means. The regression has been well developed for linear models but less explored for nonparametric models. In this thesis, we consider the estimation of conditional quantiles in a varying-coefficient model. …
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Three Essays Exploring Heterogeneity Using Quantile Regression
This study examines heterogeneity using quantile regression in several different areas in labor economics. Quantile regression calculates different estimates of the relationship between two variables at different points on the conditional distribution and, therefore, allows heterogeneity in these …
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Quantile regression and the duration of unemployment
Powell (1986) proposed a quantile regression estimator for censored regression models on the basis of equivariance of quantiles to monotone transformations. In this thesis, censored quantile regression models are generalized using two-parameter Box-Cox transformation to relax the conventional …
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Portfolio optimization with quantile-based risk measures
… theory, in the cases where the risk measures are quantile-based (such as the Value at Risk (V aR) and the shortfall). We show, using multicriteria theory arguments, that if the measure of risk is convex and the measure of reward concave with respect to the allocation vector, then the expected …
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Bayesian regularized quantile mixed models for longitudinal studies
… we aim at developing novel Bayesian regularized quantile mixed effect models to tackle these challenges. In the first project, we have proposed a Bayesian variable selection method in the mixed effect models for longitudinal lipidomics studies. To dissect important lipid-environment interactions, …
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Clustering Analysis of Zernike Coefficients Through Quantile Regression
… fifteen Zernike coefficients into groups. Quantile regressions are considered to describe the relationship between Zernike coefficients and pupil size. We employ Gibbs sampler and adaptive rejection Metropolis sampling to infer the parameters for each cluster. Bayesian information criterion …
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Quantile Regression Approach For Analyzing Gene Expression Data
… gene data. In this research, we use linear quantile mixed model to analyze gene expression data. One of the notable advantages for quantile re- gression is that models error term do not assume any specific parametric distribution. This method enables the study of gene expression change over …
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Quantile Regression Methods for Recursive Structural Equation Models
… as 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 …
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Prediction interval modeling using Gaussian process quantile regression
… realizations of the forecasted variable by using quantile regression on the observed prediction error distribution, the distribution of which is not assumed. An independent meta-model that runs in parallel to the original point forecast model is responsible for learning and generating the …
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