Nonparametric C- and D-vine-based quantile regression [PDF]
Dependence Modeling, 2022 Quantile regression is a field with steadily growing importance in statistical modeling. It is a complementary method to linear regression, since computing a range of conditional quantile functions provides more accurate modeling of the stochastic ...
Tepegjozova Marija +3 more
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Nonparametric quantile regression captures regional variability and scaling deviations in Atlantic surfclam length–weight relationships [PDF]
Scientific ReportsThe universality of the allometric model for describing the length–weight relationship in marine species has been questioned, particularly for some invertebrates such as sea urchins, clams, and barnacles.
Gorka Bidegain +6 more
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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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Quantile Processes for Semi and Nonparametric Regression
Electronic Journal of Statistics, 2017 A collection of quantile curves provides a complete picture of conditional distributions. Properly centered and scaled versions of estimated curves at various quantile levels give rise to the so-called quantile regression process (QRP). In this paper, we
Chao, Shih-Kang +2 more
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Modeling Spatial Data with Heteroscedasticity Using PLVCSAR Model: A Bayesian Quantile Regression Approach [PDF]
EntropySpatial data not only enables smart cities to visualize, analyze, and interpret data related to location and space, but also helps departments make more informed decisions.
Rongshang Chen, Zhiyong Chen
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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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Bayesian nonparametric quantile process regression and estimation of marginal quantile effects [PDF]
Biometrics, 2021 AbstractFlexible estimation of multiple conditional quantiles is of interest in numerous applications, such as studying the effect of pregnancy‐related factors on low and high birth weight. We propose a Bayesian nonparametric method to simultaneously estimate noncrossing, nonlinear quantile curves. We expand the conditional distribution function of the
Steven G. Xu, Brian J. Reich
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Nonparametric Smoothing for Extremal Quantile Regression with Heavy Tailed Data
Revstat Statistical Journal, 2021 In several different fields, it is interested in analyzing the upper or lower tail quantile of the underlying distribution rather than mean or center quantile.
Takuma Yoshida
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How Do Financial Development and Renewable Energy Affect Consumption-Based Carbon Emissions?
Mathematical and Computational Applications, 2022 This paper bridges the gap in the literature by employing the novel quantile-on-quantile (QQ) approach, the quantile regression approach, and the nonparametric Granger causality test in quantiles to assess the effect of international trade on consumption-
Abraham Ayobamiji Awosusi +3 more
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Nonparametric Quantile Regression Estimation With Mixed Discrete and Continuous Data [PDF]
Journal of Business & Economic Statistics, 2020 In this paper, we investigate the problem of nonparametrically estimating a conditional quantile function with mixed discrete and continuous covariates. A local linear smoothing technique combining both continuous and discrete kernel functions is introduced to estimate the conditional quantile function.
Li, Degui, Li, Qi, Li, Zheng
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