Simulation-Based Confidence Intervals for Functions With Complicated Derivatives

Micha Mandel*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

49 Scopus citations

Abstract

In many scientific problems, the quantity of interest is a function of parameters that index the model, and confidence intervals are constructed by applying the delta method. However, when the function of interest has complicated derivatives, this standard approach is unattractive and alternative algorithms are required. This article discusses a simple simulation-based algorithm for estimating the variance of a transformation, and demonstrates its simplicity and accuracy by applying it to several statistical problems.

Original languageAmerican English
Pages (from-to)76-81
Number of pages6
JournalAmerican Statistician
Volume67
Issue number2
DOIs
StatePublished - May 2013

Bibliographical note

Funding Information:
Micha Mandel is Senior Lecturer, Department of Statistics, The Hebrew University of Jerusalem, Jerusalem, Israel (E-mail: msmic@huji.ac.il). The work was partially supported by The Israel Science Foundation (Grant No. 774/11). The author thanks Yosi Rinott, an associate editor, and a referee for their helpful comments. I am grateful to the editor, Ronald Christensen, whose comments and suggestions greatly helped to sharpen and improve the article.

Keywords

  • Asymptotic normal estimator
  • Delta method
  • Multiple sclerosis
  • Parametric bootstrap

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