Electronic Journal of Statistics

Local optimization-based statistical inference

Shifeng Xiong

Full-text: Open access

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.

Article information

Source
Electron. J. Statist., Volume 11, Number 1 (2017), 2295-2320.

Dates
Received: November 2016
First available in Project Euclid: 27 May 2017

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1495850626

Digital Object Identifier
doi:10.1214/17-EJS1292

Mathematical Reviews number (MathSciNet)
MR3656493

Zentralblatt MATH identifier
1364.62048

Subjects
Primary: 62F03: Hypothesis testing 62F25: Tolerance and confidence regions 62F40: Bootstrap, jackknife and other resampling methods

Keywords
Bootstrap importance sampling non-regular problem resampling space-filling design stochastic programming

Rights
Creative Commons Attribution 4.0 International License.

Citation

Xiong, Shifeng. Local optimization-based statistical inference. Electron. J. Statist. 11 (2017), no. 1, 2295--2320. doi:10.1214/17-EJS1292. https://projecteuclid.org/euclid.ejs/1495850626


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