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Barekeng, 2022 The nonparametric regression model is applied to regression curves for which the regression curve is unknown. Fourier series estimation is an approach in nonparametric regression, which has high flexibility and is able to adjust to the local nature of ...
Muhammad Danil Pasarella +2 more
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Monotone Nonparametric Regression
The Annals of Statistics, 1988 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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E-Jurnal Matematika, 2022 Regression analysis is one of the statistical analyses used to estimate the relationship between the predictor and the response variable. Data are given in pairs, and the relationship between the predictor and the response variable was assumed to follow ...
NURUL FITRIYANI, I NYOMAN BUDIANTARA
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Nonparametric Bayesian Regression
The Annals of Statistics, 1986 The paper addresses itself to Bayesian estimation of the function \[ F(x_ 1,x_ 2)=m+a(x_ 1)+b(x_ 2)+c(x_ 1,x_ 2) \] in the model \(y_ i=F(x_{1i},x_{2i})+e_ i\). A prior for F is constructed by putting independent priors on m,a,b, and c. They are normal distribution and Brownian motion.
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Asymptotic equivalence and adaptive estimation for robust nonparametric regression [PDF]
, 2009 Asymptotic equivalence theory developed in the literature so far are only for bounded loss functions. This limits the potential applications of the theory because many commonly used loss functions in statistical inference are unbounded.
Cai, T. Tony, Zhou, Harrison H.
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Outliers vs Robustness in Nonparametric Methods of Regression
Acta Universitatis Lodziensis. Folia Oeconomica, 2018 The article addresses the question of how robust methods of regression are against outliers in a given data set. In the first part, we presented the selected methods used to detect outliers.
Joanna Trzęsiok
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Nonparametric seemingly unrelated regression [PDF]
Journal of Econometrics, 2000 No abstract ...
Smith, Michael, Kohn, Robert
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Robust nonparametric estimation via wavelet median regression [PDF]
, 2008 In this paper we develop a nonparametric regression method that is simultaneously adaptive over a wide range of function classes for the regression function and robust over a large collection of error distributions, including those that are heavy-tailed,
Brown, Lawrence D. +2 more
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Bandwidth Selection Problem in Nonparametric Functional Regression [PDF]
Statistika: Statistics and Economy Journal, 2017 The focus of this paper is the nonparametric regression where the predictor is a functional random variable, and the response is a scalar. Functional kernel regression belongs to popular nonparametric methods used for this purpose. The two key problems
Daniela Kuruczová, Jan Koláček
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Nonparametric Regression Estimation for Multivariate Null Recurrent Processes
Econometrics, 2015 This paper discusses nonparametric kernel regression with the regressor being a \(d\)-dimensional \(\beta\)-null recurrent process in presence of conditional heteroscedasticity. We show that the mean function estimator is consistent with convergence rate
Biqing Cai, Dag Tjøstheim
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