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Detecting heteroscedasticity in non‐parametric regression using weighted empirical processes.

Autor: Chown, Justin¹ justin.chown@ruhr-uni-bochum.de, Müller, Ursula U.²

Publikováno v: Journal of the Royal Statistical Society: Series B (Statistical Methodology). Nov2018, Vol. 80 Issue 5, p951-974. 24p.

Efficiently estimating the error distribution in nonparametric regression with responses missing at random

Autor: Chown, Justin, Müller, Ursula U.

Publikováno v: Chown, J. and M\"uller, U.U. (2013). Efficiently estimating the error distribution in nonparametric regression with responses missing at random. J. Nonparametr. Stat. 25, 665-677

This article considers nonparametric regression models with multivariate covariates and with responses missing at random. We estimate the regression function with a local polynomial smoother. The residual-based empirical distribution function that on

Externí odkaz: http://arxiv.org/abs/1610.08360

Estimation of the error distribution in a varying coefficient regression model.

Autor: Schick, Anton, Zhu, Yilin, Du, Xiaojie

Publikováno v: Journal of Nonparametric Statistics; Jun2018, Vol. 30 Issue 2, p392-429, 38p

A Cramér-von Mises test for symmetry of the error distribution in asymptotically stationary stochastic models.

Autor: Ngatchou-Wandji, Joseph, Harel, Michel

Publikováno v: Statistical Inference for Stochastic Processes; Oct2013, Vol. 16 Issue 3, p207-236, 30p