Bootstrapping the shorth for regression

Cécile Durot; Karelle Thiébot

Displaying similar documents to “Bootstrapping the shorth for regression”

Goodness of fit test for isotonic regression

Cécile Durot, Anne-Sophie Tocquet (2001)

ESAIM: Probability and Statistics

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We consider the problem of hypothesis testing within a monotone regression model. We propose a new test of the hypothesis $H_{0}$ : “ $f = f_{0}$ ” against the composite alternative $H_{a}$ : “ $f \neq f_{0}$ ” under the assumption that the true regression function $f$ is decreasing. The test statistic is based on the $𝕃_{1}$ -distance between the isotonic estimator of $f$ and the function $f_{0}$ , since it is known that a properly centered and normalized version of this distance is asymptotically standard normally distributed under $H_{0}$ . We study...

A nonparametric method of regression analysis from censored data

Petr Volf (1988)

Kybernetika

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Change-point estimator in continuous quadratic regression

Daniela Jarušková (2001)

Commentationes Mathematicae Universitatis Carolinae

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The paper deals with the asymptotic distribution of the least squares estimator of a change point in a regression model where the regression function has two phases --- the first linear and the second quadratic. In the case when the linear coefficient after change is non-zero the limit distribution of the change point estimator is normal whereas it is non-normal if the linear coefficient is zero.

Approximating distribution functions by iterated function systems.

Iacus, Stefano Maria, La Torre, Davide (2005)

Journal of Applied Mathematics and Decision Sciences

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Asymptotic distribution of robust k-nearest neighbour estimator for functional nonparametric models

Mohammed Kadi Attouch, Tawfik Benchikh (2012)

Matematički Vesnik

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Exact adaptive pointwise estimation on Sobolev classes of densities

Cristina Butucea (2001)

ESAIM: Probability and Statistics

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The subject of this paper is to estimate adaptively the common probability density of $n$ independent, identically distributed random variables. The estimation is done at a fixed point $x_{0} \in ℝ$ , over the density functions that belong to the Sobolev class $W_{n} (β, L)$ . We consider the adaptive problem setup, where the regularity parameter $β$ is unknown and varies in a given set $B_{n}$ . A sharp adaptive estimator is obtained, and the explicit asymptotical constant, associated to its rate of convergence is found. ...