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Nonparametric regression to the mean [PDF]
Proceedings of the National Academy of Sciences, 2003 Available data may reflect a true but unknown random variable of interest plus an additive error, which is a nuisance. The problem in predicting the unknown random variable arises in many applied situations where measurements are contaminated with errors; it is known as the regression-to-the-mean problem.
Müller, Hans-Georg +2 more
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Nonparametric C- and D-vine-based quantile regression
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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An Algorithm of Nonparametric Quantile Regression
Journal of Statistical Theory and Practice, 2023 Extreme events, such as earthquakes, tsunamis, and market crashes, can have substantial impact on social and ecological systems. Quantile regression can be used for predicting these extreme events, making it an important problem that has applications in many fields. Estimating high conditional quantiles is a difficult problem.
Mei Ling Huang +2 more
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Nonparametric relative recursive regression
Dependence Modeling, 2020 In this paper, we propose the problem of estimating a regression function recursively based on the minimization of the Mean Squared Relative Error (MSRE), where outlier data are present and the response variable of the model is positive.
Slaoui Yousri, Khardani Salah
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Tests for Independence in Nonparametric Regression [PDF]
SSRN Electronic Journal, 2006 Consider the nonparametric regression model Y = m(X)+e, where the function m is smooth, but unknown.We construct tests for the independence of e and X, based on n independent copies of (X; Y ).The testing procedures are based on differences of neighboring Y 's.We establish asymptotic results for the proposed tests statistics, investigate their finite ...
Einmahl, J.H.J., Keilegom, I. van
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Nonparametric Regression Based on Discretely Sampled Curves
Revstat Statistical Journal, 2020 In the context of nonparametric regression, we study conditions under which the consistency (and rates of convergence) of estimators built from discretely sampled curves can be derived from the consistency of estimators based on the unobserved whole ...
Liliana Forzani +2 more
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Nonparametric Regression via StatLSSVM
Journal of Statistical Software, 2013 We present a new MATLAB toolbox under Windows and Linux for nonparametric regression estimation based on the statistical library for least squares support vector machines (StatLSSVM).
Kris De Brabanter +2 more
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Variational Multiscale Nonparametric Regression: Algorithms and Implementation
Algorithms, 2020 Many modern statistically efficient methods come with tremendous computational challenges, often leading to large-scale optimisation problems. In this work, we examine such computational issues for recently developed estimation methods in nonparametric ...
Miguel del Alamo +3 more
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Nonparametric Shape-Restricted Regression [PDF]
Statistical Science, 2018 This is a survey ...
Guntuboyina, Adityanand +1 more
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Bayesian nonparametric monotone regression
Environmetrics, 2020 AbstractIn many applications there is interest in estimating the relation between a predictor and an outcome when the relation is known to be monotone or otherwise constrained due to the physical processes involved. We consider one such application‐inferring time‐resolved aerosol concentration from a low‐cost differential pressure sensor. The objective
Ander Wilson +3 more
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