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Asymptotic theory for bootstrap methods in statistics

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The article was published on 1991-11-01 and is currently open access. It has received 101 citations till now. The article focuses on the topics: Asymptotic analysis & Asymptotology.

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Statistical inference in nonparametric frontier models: the state of the art

TL;DR: In this article, the authors define a statistical model allowing determination of the statistical properties of the nonparametric estimators in the multi-output and multi-input case, and provide the asymptotic sampling distribution of the FDH estimator in a multivariate setting and of the DEA estimator for the bivariate case.
Journal ArticleDOI

A general methodology for bootstrapping in non-parametric frontier models

TL;DR: This paper proposes a general methodology for bootstrapping in frontier models, extending the more restrictive method proposed in Simar & Wilson (1998) by allowing for heterogeneity in the structure of efficiency.
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Nonparametric Regression Techniques in Economics

TL;DR: In this article, a brief overview of the class of models under study and central theoretical issues such as the curse of dimensionality, the bias-variance trade-off and rates of convergence are discussed.
Book

Estimation and Inference in Nonparametric Frontier Models: Recent Developments and Perspectives

TL;DR: In this paper, a link between frontier estimation and extreme value theory has been established, and several approaches exist for introducing environmental variables into production models; both two-stage approaches, in which estimated efficiencies are regressed on environmental variables, and conditional efficiency measures, as well as the underlying assumptions required for either approach, are examined.
Journal ArticleDOI

Bootstrap Methods For Time Series

TL;DR: It is argued that methods for implementing the bootstrap with time‐series data are not as well understood as methods for data that are independent random samples, and there is a considerable need for further research.