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An Essentially Complete Class of Decision Functions for Certain Standard Sequential Problems
Milton Sobel
The Annals of Mathematical Statistics
Vol. 24, No. 3 (Sep., 1953), pp. 319337
Published by: Institute of Mathematical Statistics
Stable URL: http://www.jstor.org/stable/2236284
Page Count: 19
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Topics: Integers, Closed intervals, Zero, Experimentation, Ambivalence, A priori knowledge, Real lines, Mathematical functions, Logical givens, Mathematical independent variables
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Abstract
A sequential problem is considered in which independent observations are taken on a chance variable X whose distribution can be represented by \begin{equation*}\tag{1}dG_\theta(x) = \psi(\theta)e^{\theta x} d\mu(x),\end{equation*} where the parameter θ belongs to a given interval Ω of the real line but is otherwise unknown. The problem is to test H1:θ ≤ θ* against $H_2:\theta > \theta^\ast$, where θ* is a given point in Ω. Under certain assumptions the following class A is shown to be essentially complete relative to the class of decision rules with bounded risk functions. The decision rule δ ε A if and only if after taking n observations (i) δ depends on the observations only through n and vn = ∑n i = 1 xi and (ii) δ specifies a closed interval Jn:[ a1n, a2n ] for each n and the following rule of action (a) Stop experimentation as soon as $v_n \not\varepsilon J_n$ and (1) accept $H_1 \text{if} v_n < a_{1n}$ (2) accept $H_2 \text{if} v_n > a_{2n}$. (b) If $a_{1n} < a_{2n}$ take another observation if $a_{1n} < v < a_{2n}$. (c) If $a_{1n} < a_{2n}$ and v = ain, accept Hi or take another observation or randomize between these two (i = 1, 2). The KoopmanDarmois family of probability laws given above contains discrete members such as the binomial and Poisson distributions as well as absolutely continuous members such as the normal and exponential. It is interesting to note that the members of the class A can be obtained by starting with the sequential probability ratio test for testing some point θ* 1 ≤ θ* against another point $\theta^\ast_2 > \theta^\ast$, namely, continue as long as $B < \frac{\prod^n_{i = 1} \psi(\theta^\ast_2)e^{\theta^\ast_2 x_i}}{\prod^n_{i = 1} \psi(\theta^\ast_1)e^{\theta^\ast_1 x_i}} < A$ and replacing the constants B, A by two arbitrary sequences Bn, An such that Bn ≤ An (n = 1, 2, ⋯).
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The Annals of Mathematical Statistics © 1953 Institute of Mathematical Statistics