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Showing 1 to 20 of 195 for “"Quantile regression"”.
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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 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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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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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 expression distribution. However, the mean regression model depends on the normality assumptions of the error terms of the model, which may be impractical to analyze gene data. In this research, we use linear quantile mixed model to analyze gene expression data. One of the notable advantages …
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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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Nonparametric Bayesian Quantile Regression via Dirichlet Process Mixture Models
… propose new nonparametric Bayesian approaches to quantile regression usingDirichlet process mixture (DPM) models. All the existing quantile regression methodsbased on DPMs require the kernel density to satisfy the quantile constraint, hence thekernel densities are themselves usually in the form of …
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Modified mean and quantile regression models for citation analysis
… distribution that best fits. We also focus on quantile regression (QR) for citation analysis. Unlike linear regression, where only the conditional mean of a dependent variable is modeled, in QR the different conditional quantiles of the dependent variable, such as the median, are modeled based …
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Parametric quantile regression based on the generalised gamma distribution
Quantile regression offers an extension to regression analysis where a modified version of the least squares method allows the fitting of quantiles at every percentile of the data rather than the mean only. Using the well-known three-parameter generalised gamma distribution to model variation in …
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Methods For Constructing Confidence Intervals For Quantile Regression Coefficients
… for constructing confidence intervals for quantile regression coefficients. We consider methods based on resampling, sparsity estimation, and test-inversion. In the latter group, along with the popular rank-score, we include methods based on linear and logistic regression that exploit the …
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Longitudinal Growth Charts Based on Semi-Parametric Quantile Regression
… growth charts based on two longitudinal quantile regression models, one parametric and one semi-parametric. Both models incorporate individual prior growth and other covariates without the assumption of normality. The semiparametric model is global in nature and accommodates varying …
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Income Inequality and Wage Differentials: A Quantile Regression Approach
… analysis of the gender wage gap using quantile regression. Many studies have examined the gender wage gap in the United States but this is the first to provide systematic analysis of the gender wage gap using quantile regression over time. Using data from both the March Current …
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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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Parametric modeling of dependence of bivariate quantile regression residuals' signs
… method to study the dependence of the quantiles of a multivariate response conditional on a set of covariates. We define a statistic that measures the conditional probability of concordance of the signs of the residuals of the conditional quantiles of each univariate response. The …
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