Local optimization-based statistical inference

DOI10.1214/17-EJS1292zbMATH Open1364.62048arXiv1502.00465MaRDI QIDQ2396346

Shifeng Xiong

Publication date: 8 June 2017

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Abstract: This paper introduces a local optimization-based approach to test statistical hypotheses and to construct confidence intervals. This approach can be viewed as an extension of bootstrap, and yields asymptotically valid tests and confidence intervals as long as there exist consistent estimators of unknown parameters. We present simple algorithms including a neighborhood bootstrap method to implement the approach. Several examples in which theoretical analysis is not easy are presented to show the effectiveness of the proposed approach.

Full work available at URL: https://arxiv.org/abs/1502.00465

zbMATH Keywords

bootstrapstochastic programmingimportance samplingresamplingspace-filling designnon-regular problem

Mathematics Subject Classification ID

Parametric tolerance and confidence regions (62F25) Parametric hypothesis testing (62F03) Bootstrap, jackknife and other resampling methods (62F40)

Related Items (3)

Local statistical methods of knowledge formation ⋮ Testing that a local optimum of the likelihood is globally optimum using reparameterized embeddings. Applications to wavefront sensing ⋮ Machine Learning: ECML 2004

This page was built for publication: Local optimization-based statistical inference

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q2396346)