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 language | English |
|---|---|
| Pages (from-to) | 76-81 |
| Number of pages | 6 |
| Journal | American Statistician |
| Volume | 67 |
| Issue number | 2 |
| DOIs | |
| State | Published - May 2013 |
Bibliographical note
Funding Information:Micha Mandel is Senior Lecturer, Department of Statistics, The Hebrew University of Jerusalem, Jerusalem, Israel (E-mail: [email protected]). 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