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On the consistency of sieve bootstrap prediction intervals for stationary time series

Roman Różański, Adam Zagdański (2004)

Discussiones Mathematicae Probability and Statistics

In the article, we consider construction of prediction intervals for stationary time series using Bühlmann's [8], [9] sieve bootstrapapproach. Basic theoretical properties concerning consistency are proved. We extend the results obtained earlier by Stine [21], Masarotto and Grigoletto [13] for an autoregressive time series of finite order to the rich class of linear and invertible stationary models. Finite sample performance of the constructed intervals is investigated by computer simulations.

On the stability of interacting processes with applications to filtering and genetic algorithms

Pierre Del Moral, Alice Guionnet (2001)

Annales de l'I.H.P. Probabilités et statistiques

One Bootstrap suffices to generate sharp uniform bounds in functional estimation

Paul Deheuvels (2011)

Kybernetika

We consider, in the framework of multidimensional observations, nonparametric functional estimators, which include, as special cases, the Akaike–Parzen–Rosenblatt kernel density estimators ([1, 18, 20]), and the Nadaraya–Watson kernel regression estimators ([16, 22]). We evaluate the sup-norm, over a given set $𝐈$ , of the difference between the estimator and a non-random functional centering factor (which reduces to the estimator mean for kernel density estimation). We show that, under suitable general...

Optimal model selection in density estimation

Matthieu Lerasle (2012)

Annales de l'I.H.P. Probabilités et statistiques

In order to calibrate a penalization procedure for model selection, the statistician has to choose a shape for the penalty and a leading constant. In this paper, we study, for the marginal density estimation problem, the resampling penalties as general estimators of the shape of an ideal penalty. We prove that the selected estimator satisfies sharp oracle inequalities without remainder terms under a few assumptions on the marginal density $s$ and the collection of models. We also study the slope heuristic,...

Ordenes de convergencia para las aproximaciones normal y bootstrap en estimación no paramétrica de la función de densidad.

Ricardo Cao Abad (1990)

Trabajos de Estadística

Este artículos concierne las distribuciones usadas para construir intervalos de confianza para la función de densidad en una situación no paramétrica. Se comparan los órdenes de convergencia para el límite normal, su aproximación "plug in" y el método bootstrap. Se deduce que el bootstrap se comporta mejor que las otras dos aproximaciones tanto en su forma clásica como con la aproximación bootstrap normal.