How Many More Missing Women? Excess Female Mortality and Prenatal Sex Selection, 1970–2050

Sex‐based discrimination has resulted in severe demographic imbalances between males and females, culminating in a large number of “missing women” in several countries around the world. We provide new estimates and projections of the number of missing females and of the roles played by prenatal and postnatal factors in this imbalance. We estimate time series of the number of missing females, the number of excess female deaths, and the number of missing female births for the world and selected countries. Estimates are provided for 1970–2010 and projections are made from 2010 to 2050. We show that the estimates of these different indicators are consistent with one another and account for the dynamics of the population of missing females over time. We conclude that the number of missing females has steadily risen in the past decades, reaching 126 million in 2010, and the number is expected to peak at 150 million in 2035. Excess mortality was the dominant cause of missing females in the past, and this is expected to remain the case in future decades in spite of the recent rise of prenatal sex selection. The annual number of newly missing females reached 3.4 million in 2010 and is expected to remain above 3 million every year until 2050.     

Designing optimal food safety monitoring schemes using Bayesian network and integer programming: The case of monitoring dioxins and DL-PCBs

Efficient food safety monitoring should achieve optimal resource allocation. In this article, a methodology is presented to optimize the use of resources for food safety monitoring aimed at identifying noncompliant samples and estimating background level of hazards in food products. A Bayesian network (BN) model and an optimization model were combined in a single framework. The framework was applied to monitoring dioxins and dioxin-like polychlorinated biphenyls (DL-PCBs) in primary animal-derived food products in the Netherlands. The BN model was built using a national dataset with monitoring results of dioxins and DL-PCBs in animal-derived food products over a 10-year period (2008–2017). These data were used to estimate the probability of detecting suspect samples with dioxins and DL-PCBs levels above preset thresholds, given certain sample conditions. The results of the BN model were then inserted into the optimization model to compute an optimal monitoring scheme. Model estimates showed that the probability of dioxins and DL-PCBs exceeding threshold limits was higher in laying hen eggs and sheep meat than in other animal-derived food (except deer meat). Compared with the monitoring scheme used in the Netherlands in 2018, the optimal monitoring scheme would save around 10,000 EUR per year. This could be obtained by reallocating monitoring resources from products with lower probability of dioxin and DL-PCBs exceeding threshold limits (e.g., pig meat) to products with higher probability (e.g., bovine animal meat), and by shifting sample collection from the last quarter of the year toward the first three quarters of the year.     

Computing Optimal Beams in Two and Three Dimensions

The problem of computing an optimal beam among weighted regions (called the optimal beam problem) arises in several applied areas such as radiation therapy, stereotactic brain surgery, medical surgery, geological exploration, manufacturing, and environmental engineering. In this paper, we present computational geometry techniques that enable us to develop efficient algorithms for solving various optimal beam problems among weighted regions in two and three dimensional spaces. In particular, we consider two types of problems: the covering problems (seeking an optimal beam to contain a specified target region), and the piercing problems (seeking an optimal beam of a fixed shape to pierce the target region). We investigate several versions of these problems, with a variety of beam shapes and target region shapes in 2-D and 3-D. Our algorithms are based on interesting combinations of computational geometry techniques and optimization methods, and transform the optimal beam problems to solving a collection of instances of certain special non-linear optimization problems. Our approach makes use of interesting geometric observations, such as utilizing some new features of Minkowski sums.     

MODELING MODEL UNCERTAINTY

Recently there has been a great deal of interest in studying monetary policy under model uncertainty. We point out that different assumptions about the uncertainty may result in drastically different “robust” policy recommendations. Therefore, we develop new methods to analyze uncertainty about the parameters of a model, the lag specification, the serial correlation of shocks, and the effects of real‐time data in one coherent structure. We consider both parametric and nonparametric specifications of this structure and use them to estimate the uncertainty in a small model of the U.S. economy. We then use our estimates to compute robust Bayesian and minimax monetary policy rules, which are designed to perform well in the face of uncertainty. Our results suggest that the aggressiveness recently found in robust policy rules is likely to be caused by overemphasizing uncertainty about economic dynamics at low frequencies. (JEL: E52, C32, D81)     

Determination of recovery effort for a probabilistic recovery system under various inventory control policies

In this study we investigate the desired level of recovery under various inventory control policies when the success of recovery is probabilistic. All the used and returned items go into a recovery process that is modelled as a single stage operation. The recovery effort is represented by the expected time spent for it. The effect of increasing recovery effort on the success probability together with unit cost of the operation is included by assuming general forms of dependencies. Alternative to recovered items, demand is satisfied by brand-new items. Four inventory control policies that differ in timing of and information used in purchasing decision are proposed. The objective is to find the recovery level together with inventory control parameter that minimize the long-run average total cost. A numerical study covering a wide range of system parameters is carried out. Finally computational results are presented with their managerial implications.     

Testing Hypotheses About the Number of Factors in Large Factor Models

In this paper we study high‐dimensional time series that have the generalized dynamic factor structure. We develop a test of the null of k₀ factors against the alternative that the number of factors is larger than k₀ but no larger than k₁>k₀. Our test statistic equals max_{k0<k k1}(γ_k−γ_k+1)(γ_k+1−γ_k+2), where γ_i is the ith largest eigenvalue of the smoothed periodogram estimate of the spectral density matrix of data at a prespecified frequency. We describe the asymptotic distribution of the statistic, as the dimensionality and the number of observations rise, as a function of the Tracy–Widom distribution and tabulate the critical values of the test. As an application, we test different hypotheses about the number of dynamic factors in macroeconomic time series and about the number of dynamic factors driving excess stock returns.     

环状区域的车辆路径方案生成系统及优化模型

针对顾客满足环状区域分布的车辆路径问题(VRP),以大幅度地缩减问题求解的状态空间为突破口,引入人工智能和运筹学理论,提出求解这类特殊车辆路径问题的两阶段方法.第1阶段考虑行车时间和车载容量,提出带有控制策略的深度优先搜索算法自动生成备选的车辆路径方案集合.第2阶段将此备选方案集合归结为整数规划模型.采用VB6.0编程语言构建了车辆路径方案生成系统,并实现该系统与运筹学求解软件lindo的集成.通过案例验证了上述方法及自动求解系统的有效性.该项研究为解决环状配送区域的车辆路径问题这一难题提供了新方法.     

Inequalities for Expected Current Record Statistics

In this article, we obtain sharp distribution-free bounds for the expected value of the gap between the current records and record values as well as upper sharp bounds for the spacings between any two upper current records. We also present two-sided bounds on the errors in approximating the means of current records by inverse hazard functions.     

Generalized exponential distribution: Moments of order statistics

In this paper, we consider the generalized exponential distribution (GED) with shape parameter α. We establish several recurrence relations satisfied by the single and the product moments for order statistics from the GED. The relationships can be written in terms of polygamma and hypergeometric functions and used in a simple recursive manner in order to compute the single and the product moments of all order statistics for all sample sizes.