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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quantreg.nonpar: An R Package for Performing Nonparametric Series Quantile Regression [PDF]
, 2016 The R package quantreg.nonpar implements nonparametric quantile regression methods to estimate and make inference on partially linear quantile models. quantreg.nonpar obtains point estimates of the conditional quantile function and its derivatives based ...
Michael Lipsitz +3 more
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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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Nonparametric Quantile Regression: Non-Crossing Constraints and Conformal Prediction [PDF]
, 2022 We propose a nonparametric quantile regression method using deep neural networks with a rectified linear unit penalty function to avoid quantile crossing. This penalty function is computationally feasible for enforcing non-crossing constraints in multi-dimensional nonparametric quantile regression.
Wenlu Tang +3 more
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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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Nonparametric quantile regression for time series with replicated observations and its application to climate data [PDF]
Statistical Science, 2021 This paper proposes a model-free nonparametric estimator of conditional quantile of a time series regression model where the covariate vector is repeated many times for different values of the response. This type of data is abound in climate studies. To tackle such problems, our proposed method exploits the replicated nature of the data and improves on
Soudeep Deb, Kaushik Jana
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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 Estimation of an Additive Quantile Regression Model [PDF]
, 2004 This paper is concerned with estimating the additive components of a nonparametric additive quantile regression model. We develop an estimator that is asymptotically normally distributed with a rate of convergence in probability of $n^{-r/(2r+1)}$ when ...
Sokbae Lee, Joël L. Horowitz
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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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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.
Degui Li, Qi Li, Zheng Li
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