The Annals of Statistics

Tolerance regions and multiple-use confidence regions in multivariate calibration

Thomas Mathew, Kenneth Nordström, and Manoj Kumar Sharma

Full-text: Open access

Abstract

Let $\mathrm{y}~ N(Bx_i,\Sigma),i=1,2\ldots,N$, and $\mathrm{y}~N(B\theta, \Sigma)$ be independent multivariate observations, where the $x_i$'s are known vectors, $B,\theta$ and $\Sigma$ are unknown, $\Sigma$ being a positive definite matrix. The calibration problem deals with statistical inference concerning $\theta$ and the problem that we have addressed is the construction of confidence regions. In this article, we have constructed a region for $\theta$ based on a criterion similar to that satisfied by a tolerance region. The situation where $\theta$ is possibly a nonlinear function, say $\mathrm{h}(\xi)$ of fewer unknown parameters denoted by the vector $(\xi)$, is also considered. The problem addressed in this context is the construction of a region for $\xi$. The numerical computations required for the practical implementation of our region are explained in detail and illustrated using an example. Limited numerical results indicate that our regions satisfy the coverage probability requirements of multiple­use confidence regions.

Article information

Source
Ann. Statist., Volume 26, Number 5 (1998), 1989-2013.

Dates
First available in Project Euclid: 21 June 2002

Permanent link to this document
https://projecteuclid.org/euclid.aos/1024691366

Digital Object Identifier
doi:10.1214/aos/1024691366

Mathematical Reviews number (MathSciNet)
MR1673287

Zentralblatt MATH identifier
0929.62071

Subjects
Primary: 62F25
Secondary: 62H99: None of the above, but in this section

Keywords
Calibration matrix variate beta distribution matrix variate $F$ distribution multiple-use confidence region multivariate linear model noncentral chi-square tolerance region Wishart distribution

Citation

Mathew, Thomas; Sharma, Manoj Kumar; Nordström, Kenneth. Tolerance regions and multiple-use confidence regions in multivariate calibration. Ann. Statist. 26 (1998), no. 5, 1989--2013. doi:10.1214/aos/1024691366. https://projecteuclid.org/euclid.aos/1024691366


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