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Convergence Rates for Empirical Bayes Two-Action Problems II. Continuous Case

# Convergence Rates for Empirical Bayes Two-Action Problems II. Continuous Case,10.1214/aoms/1177692557,The Annals of Mathematical Statistics,M. V. John

Convergence Rates for Empirical Bayes Two-Action Problems II. Continuous Case
For a general discussion of empirical Bayes problems and motivation of the present paper see Section 1 of the previous paper [1]. In that paper we studied the convergence to Bayes optimality and its rate properties for empirical Bayes two-action problems in certain discrete exponential families. This paper continues that investigation for the continuous case. Under appropriate conditions, Theorems 3 and 4 yield convergence rates to Bayes risk of $O(n^{-\beta})$ for $0 < \beta < 1$, for the $(n + 1)\mathrm{st}$ stage risk of the continuous case empirical Bayes procedures of Section 2. These theorems provide, for the continuous case, convergence rate results for the empirical Bayes procedures of the general type considered by Robbins [5] and Samuel [6] for two different parameterizations of a model. The rate results given here in the continuous case involve upper bounds and are weaker than the discrete case results in [1] wherein exact rates are reported. Specifically, in Section 2 we present the two cases to be considered and define the appropriate empirical Bayes procedures for each. Section 3 gives some technical lemmas and Section 4 establishes the asymptotic optimality (the asymptotic Bayes property) of the procedures introduced. The main results on rates, Theorems 3 and 4, are given in Section 5. Section 6 examines in detail two specific examples--the negative exponential and the normal distributions--and gives corollaries to Theorems 3 and 4 which state convergence rates depending on moment properties of the unknown prior distribution of the parameters. Section 7 gives an example with $\beta$ arbitrarily close to 1 in the rate $O(n^{-\beta})$. The model we consider is the following. Let $f_\lambda(x)$ be a family of Lebesgue densities indexed by a parameter $\lambda$ in an interval of the real line. As in [1], we wish to test the hypothesis $H_1: \lambda \leqq c \mathrm{vs}. H_2: \lambda > c$ with the loss function being \begin{align*}L_1(\lambda) &= 0 \quad\text{if}\quad \lambda \leqq c \\ &= b(\lambda - c) \quad\text{if}\quad \lambda > c \\ L_2(\lambda) &= b(c - \lambda) \quad\text{if}\quad \lambda \leqq c \\ &= 0 \quad\text{if}\quad \lambda > c\end{align*} where $L_i(\lambda)$ indicates the loss when action $i$ (deciding in favor of $H_i$) is taken, $i = 1, 2$ and $b$ is a positive constant. Let $\delta(x) = \mathrm{Pr}\{\text{accepting} H_1 \mid X = x\}$ be a randomized decision rule for the above two-action problem. If $G = G(\lambda)$ is a prior distribution on $\lambda$, then the risk of the (randomized) decision procedure $\delta$ under prior distribution $G$ is given as in [1] by, \begin{align*}\tag{1}r(\delta, G) &= \int\int \{L_1(\lambda)f_\lambda(x)\delta(x) + L_2(\lambda)f_\lambda(x)(1 - \delta(x))\} dx dG(\lambda) \\ &= b \int \alpha(x)\delta(x) dx + C_G\end{align*} where $C_G = \int L_2(\lambda) dG(\lambda)$ and \begin{equation*}\tag{2}\alpha(x) = \int \lambda f_\lambda(x) dG(\lambda) - cf(x)\end{equation*} with \begin{equation*}\tag{3}f(x) = \int f_\lambda(x) dG(\lambda).\end{equation*} From (1) it is clear that a Bayes rule (the minimizer of (1) given $G$) is \begin{align*}\tag{4}\delta_G(x) &= 1\quad\text{if} \alpha(x) \leqq 0 \\ &= 0\quad\text{if} \alpha(x) > 0.\end{align*} Hence, the minimal attainable risk knowing $G$ (the Bayes risk) is \begin{equation*}\tag{5}r^\ast(G) = \inf_\delta r(\delta, G) = r(\delta_G, G).\end{equation*}
Journal: The Annals of Mathematical Statistics , vol. 43, no. 1972, pp. 934-947, 1972
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## Citation Context (8)

• ...For example, Johns & VR [1] have constructed asymptotically optimal EBT for the continuous one-parameter exponential family; Karunamuni[2] and Liang [3] had improved results of [1] by considering the monotonic of the testing problem in the construction of their proposed EBT...
• ...For example, Johns & VR [1] have constructed asymptotically optimal EBT for the continuous one-parameter exponential family; Karunamuni[2] and Liang [3] had improved results of [1] by considering the monotonic of the testing problem in the construction of their proposed EBT...
• ...Kx r = is a Borel-Measurable bounded function vanishing outside the interval [0,1] ,...

### Chen Jiaqing, et al. Two-Sided Empirical Bayes Test for the Location Parameter of Lognormal...

• ...Johns & VR[2] have constructed asymptotically optimal EBT for the continuous one-parameter exponential family; Karunamuni[3] and Liang[4] had improved results of [2] by considering the monotonic of the testing problem in the construction of their proposed EBT...

### Chen Jiaqing, et al. Optimal rates of empirical Bayes tests for a positive exponential fami...

• ...In the literature, Johns and van Ryzin (1972) and van Houwelingen (1976), among many others, have studied empirical Bayes tests for the continuous exponential family...

### Tachen Liang. Empirical Bayes Testing for Double Exponential Distributions

• ...Johns and van Ryzin 5 and van Houwelingen 6 studied empirical Bayes tests for one-parameter continuous exponential family; Johns and van Ryzin 7 and Liang 8 considered empirical Bayes tests for discrete exponential families...

### Tachen Liang. On empirical Bayes two-tail tests in a positive exponential family

• ...For example, Johns and Van Ryzin [3,4] discussed the EB test problem in discrete and continuous oneparameter exponential families with a linear loss function; Van Houwelingen [5] and Liang [6] studied the monotonic EB test problems for the above exponential families; Hu and Pan [7] considered the EB one-tailed test problem for the scale exponential family with a linear loss function; Singh and Wei [8] studied the EB two-tailed test ...
• ...Lemma 1 [4] Let R(G) and Rn(δn ,G ) be given by (8) and (18), respectively...

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