Bootstrap adjustments for empirical bayes interval estimates of small-area proportions

Empirical Bayes approaches have often been applied to the problem of estimating small-area parameters. As a compromise between synthetic and direct survey estimators, an estimator based on an empirical Bayes procedure is not subject to the large bias that is sometimes associated with a synthetic estimator, nor is it as variable as a direct survey estimator. Although the point estimates perform very well, naïve empirical Bayes confidence intervals tend to be too short to attain the desired coverage probability, since they fail to incorporate the uncertainty which results from having to estimate the prior distribution. Several alternative methodologies for interval estimation which correct for the deficiencies associated with the naïve approach have been suggested. Laird and Louis (1987) proposed three types of bootstrap for correcting naïve empirical Bayes confidence intervals. Calling the methodology of Laird and Louis (1987) an unconditional bias-corrected naïve approach, Carlin and Gelfand (1991) suggested a modification to the Type III parametric bootstrap which corrects for bias in the naïve intervals by conditioning on the data. Here we empirically evaluate the Type II and Type III bootstrap proposed by Laird and Louis, as well as the modification suggested by Carlin and Gelfand (1991), with the objective of examining coverage properties of empirical Bayes confidence intervals for small-area proportions.     

无定形橡木陈酿白兰地的工艺研究

模拟木桶陈酿，研究并确定了无定形橡木陈酿白兰地的工艺过程和技术参数，取得了与木桶陈酿一致的结果．     

参变随机过程定型性质初探

改进和发展了ＰＲＰ参变样本函数的定型性质，提出了广义定型性质的定义，并研究了它的基本性质。     

二叉树形冲突分解算法研究

采用了一种新的二叉树形算法分析技术，对随机存取系统中报文分组冲突分解进行了研究，得到了冲突报文分组平均分解次数的解析式和动态吞吐率和静态吞吐率的解析式。给出了报文分组冲突分解的计算机模拟实验值。改进了Ｊ．Ｉ．Ｃａｐｅｔａｎａｋｉｓ的二叉树形冲突分解算法分析。     

INFERENCE IN DIRICHLET PROCESS MIXED GENERALIZED LINEAR MODELS BY USING MONTE CARLO EM

Generalized linear mixed models are widely used for describing overdispersed and correlated data. Such data arise frequently in studies involving clustered and hierarchical designs. A more flexible class of models has been developed here through the Dirichlet process mixture. An additional advantage of using such mixture models is that the observations can be grouped together on the basis of the overdispersion present in the data. This paper proposes a partial empirical Bayes method for estimating all the model parameters by adopting a version of the EM algorithm. An augmented model that helps to implement an efficient Gibbs sampling scheme, under the non‐conjugate Dirichlet process generalized linear model, generates observations from the conditional predictive distribution of unobserved random effects and provides an estimate of the average number of mixing components in the Dirichlet process mixture. A simulation study has been carried out to demonstrate the consistency of the proposed method. The approach is also applied to a study on outdoor bacteria concentration in the air and to data from 14 retrospective lung‐cancer studies.     

Dual Sourcing Under Random Supply Capacities: The Role of the Slow Supplier

Sourcing from multiple suppliers with different characteristics is common in practice for various reasons. This paper studies a dynamic procurement planning problem in which the firm can replenish inventory from a fast and a slow supplier, both with uncertain capacities. The optimal policy is characterized by two reorder points, one for each supplier. Whenever the pre‐order inventory level is below the reorder point, a replenishment order is issued to the corresponding supplier. Interestingly, the reorder point for the slow supplier can be higher than that of the fast even if the former has a higher cost, lower reliability, and smaller capacity than the latter, suggesting the possibility of ordering exclusively from an inferior slow supplier in the short term. Moreover, the firm may allocate a larger portion of the long‐term total order quantity to the slow supplier than to the fast, even if the former does not possess any cost or reliability advantage over the latter. Such phenomena, different from the observations made in previous studies, happen when the demand is uncertain and the supply is limited or unreliable. Our observations highlight the importance of incorporating both demand uncertainty and supplier characteristics (i.e., cost, lead time, capacity and uncertainty) in a unified framework when formulating supplier selection and order allocation strategies.     

Menu‐Dependent Stochastic Feasibility

We examine the role of stochastic feasibility in consumer choice using a random conditional choice set rule (RCCSR) and uniquely characterize the model from conditions on stochastic choice data. Feasibility is modeled to permit correlation in availability of alternatives. This provides a natural way to examine substitutability/complementarity. We show that an RCCSR generalizes the random consideration set rule of [Manzini and Mariotti, 2014]. We then relate this model to existing literature. In particular, an RCCSR is not a random utility model.     

欠采样超宽带LFM信号参数估计

针对Nyquist采样频率过高、硬件实现困难的问题,提出一种基于欠采样的超宽带线性调频信号的调制斜率和初始频率估计方法。该方法利用互谱ESPRIT算法和余数定理对欠采样信号进行解模糊,在低信噪比情况下仍能较精确地估计超宽带线性调频信号的调制斜率和初始频率。计算机仿真证实了该方法的有效性。     

随机分组算法的研究与实现

从运动员比赛分组的问题入手,结合实际需求,利用随机分组算法实现了双随机分组,解决了体育比赛中的公平分组问题。