The Annals of Statistics

On Sequential Distinguishability

Rasul A. Khan

Full-text: Open access

Abstract

Let $X_1, X_2,\cdots$ be a sequence of independent and identically distributed random variables governed by an unknown member of a countable family $\mathscr{P} = \{P_\theta: \theta \in \Omega\}$ of probability measures. The family $\mathscr{P}$ is said to be sequentially distinguishable if for any $\varepsilon (0 < \varepsilon < 1)$ there exist a stopping time $t$ and a terminal decision function $\delta(X_1,\cdots, X_t)$ such that $P_\theta\{t < \infty\} = 1 \forall\theta\in\Omega$ and $\sup_{\theta\in\Omega} P_\theta(\delta(X_1,\cdots, X_t) \neq \theta) \leqq \varepsilon$. Robbins [12] defined a general stopping time (see Section 2) as an approach to this problem. This paper is a study of this stopping time with applications to some exponential distributions.

Article information

Source
Ann. Statist., Volume 1, Number 5 (1973), 838-850.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176342505

Digital Object Identifier
doi:10.1214/aos/1176342505

Mathematical Reviews number (MathSciNet)
MR345355

Zentralblatt MATH identifier
0274.62058

JSTOR
links.jstor.org

Subjects
Primary: 62L10: Sequential analysis
Secondary: 62L99: None of the above, but in this section

Keywords
Sequential distinguishability countable family stopping rule sequential probability ratio test optimality Kullback-Leibler information measure asymptotic optimality

Citation

Khan, Rasul A. On Sequential Distinguishability. Ann. Statist. 1 (1973), no. 5, 838--850. doi:10.1214/aos/1176342505. https://projecteuclid.org/euclid.aos/1176342505


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