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Bayesian Nonparametric Quantile Process Regression
2021Codes to reproduce QUINN, a novel Bayesian nonparametric quantile process regression.
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Nonparametric Quantile Regression‐Based Classifiers for Bankruptcy Forecasting
Journal of Forecasting, 2013AbstractAn improved classification device for bankruptcy forecasting is proposed. The proposed approach relies on mainstream classifiers whose inputs are obtained from a so‐called multinorm analysis, instead of traditional indicators such as the ROA ratio and other accounting ratios. A battery of industry norms (computed by using nonparametric quantile
Lorca, Pedro +2 more
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Nonparametric Multivariate Conditional Distribution and Quantile Regression
SSRN Electronic Journal, 2008In 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 +2 more
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A Bayesian Nonparametric Approach to Inference for Quantile Regression
Journal of Business and Economic Statistics, 2010We develop a Bayesian method for nonparametric model–based quantile regression. The approach involves flexible Dirichlet process mixture models for the joint distribution of the response and the covariates, with posterior inference for different quantile curves emerging from the conditional response distribution given the covariates.
Taddy, Matthew A., Kottas, Athanasios
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Linear and Nonparametric Quantile Regression
2012Quantile regression estimates can be presented in tables alongside linear regression estimates. A possible advantage of this approach to presenting quantile regression results is that it is easy to compare the values of the coefficients and standard errors with OLS estimates and across quantiles. As we have seen, quantile estimates actually contain far
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Nonparametric M -quantile regression using penalised splines
Journal of Nonparametric Statistics, 2009Quantile regression investigates the conditional quantile functions of a response variable in terms of a set of covariates. M-quantile regression extends this idea by a ‘quantile-like’ generalisation of regression based on influence functions. In this work, we extend it to nonparametric regression, in the sense that the M-quantile regression functions ...
PRATESI, MONICA +2 more
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Imputation in nonparametric quantile regression with complex data
Statistics & Probability Letters, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hu, Yanan +3 more
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Nonparametric extremal quantile regression
2013Nonparametric regression quantiles obtained by inverting a kernel estimator of the conditional distribution of the response are long established in statistics [1,3,4]. Attention has been, however, restricted to ordinary quantiles staying away from the tails of the conditional distribution.
Daouia, Abdelaati +2 more
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Comparing Nonparametric Regression Quantiles [PDF]
, 2004 This paper investigates how conditional quantiles of a given distribution relate to each other. Given two conditional quantiles estimated nonparametrically, we investigate their relation by linking them through a parametric transformation. Asymptotic normality of the associated parameter vector is established, and the method is illustrated with data ...
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Non‐parametric Quantile Regression with Censored Data
Scandinavian Journal of Statistics, 2005Abstract. Censored regression models have received a great deal of attention in both the theoretical and applied statistics literature. Here, we consider a model in which the response variable is censored but not the covariates. We propose a new estimator of the conditional quantiles based on the local linear method, and give an algorithm for its ...
Saracco, Jérôme +3 more
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