Nonparametric Quantile Regression with Heavy-Tailed and Strongly Dependent Errors [PDF]
Annals of the Institute of Statistical Mathematics, 2012 We consider nonparametric estimation of the conditional qth quantile for stationary time series. We deal with stationary time series with strong time dependence and heavy tails under the setting of random design.
Toshio Honda
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Global Bahadur Representation for Nonparametric Censored Regression Quantiles and its Applications [PDF]
Econometric Theory, 2011 This paper is concerned with the nonparametric estimation of regression quantiles of a response variable that is randomly censored. Using results on the strong uniform convergence rate of U-processes, we derive a global Bahadur representation for a class of locally weighted polynomial estimators, which is sufficiently accurate for many further ...
Oliver B. Linton +2 more
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Nonparametric Multivariate Conditional Distribution and Quantile Regression
SSRN Electronic Journal, 2008 In nonparametric multivariate regression analysis, one usually seeks methods to reduce the dimensionality of the regression function to bypass the difficulty caused by the curse of dimensionality. We study nonparametric estimation of multivariate conditional distribution and quantile regression via local univariate quadratic estimation of partial ...
Keming Yu, Xiaochen Sun, Gautam Mitra
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The Use of Nonparametric Quantile Regression and Least Median of Squares Regression for Construction of Growth Curves of Weight [PDF]
Turkiye Klinikleri Journal of Medical Sciences, 2013 Objective: This study aimed to investigate the use of the Least Median Squares (LMS) regression and nonparametric quantile regression model comparatively to describe children?s weight growth. Material and Methods: Two different models were used to obtain the percentile curves to identify the weight growth in girls.
Handan Ankaralı +4 more
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Generalized, quantile and constrained nonparametric regression for spatial data
, 2021 The dissertation consists of three research projects to discuss some limitations in spatially varying coefficient models (SVCMs) for spatial data over complicated domains. In the first project, we introduce generalized spatially varying coefficient models (GSVCMs) to extend a class of SVCMs to investigate the effects of local features on various types ...
Myungjin Kim
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Nonparametric Estimation of Conditional Quantile Regression with Mixed Discrete and Continuous Data
SSRN Electronic Journal, 2015 In this paper, we investigate the nonlinear quantile regression with mixed discrete and continuous regressors. A local linear smoothing technique with the mixed continuous and discrete kernel function is proposed to estimate the conditional quantile regression function.
Degui Li, Qi Li, Zheng Li
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Reference Charts for Fetal Cerebellar Vermis Height: A Prospective Cross-Sectional Study of 10605 Fetuses. [PDF]
PLoS ONE, 2016 OBJECTIVE:To establish reference charts for fetal cerebellar vermis height in an unselected population. METHODS:A prospective cross-sectional study between September 2009 and December 2014 was carried out at ALTAMEDICA Fetal-Maternal Medical Centre, Rome,
Pietro Cignini +5 more
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Nonparametric and Semiparametric Quantile Regression via a New MM Algorithm
Journal of Computational and Graphical Statistics, 2023 Bo Kai +3 more
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Nonparametric quantile regression captures regional variability and scaling deviations in Atlantic surfclam length–weight relationships [PDF]
Abstract The universality of the allometric model for describing the length–weight relationship in marine species has been questioned, particularly for some marine invertebrates such as sea urchins, clams, and barnacles. In such cases, nonparametric regression models may offer improved flexibility and capture specific patterns—such as ...
Gorka Bidegain +6 more
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