A joint modeling approach for spatial earthquake risk variations

Modeling spatial patterns and processes to assess the spatial variations of data over a study region is an important issue in many fields. In this paper, we focus on investigating the spatial variations of earthquake risks after a main shock. Although earthquake risks have been extensively studied in the literatures, to our knowledge, there does not exist a suitable spatial model for assessing the problem. Therefore, we propose a joint modeling approach based on spatial hierarchical Bayesian models and spatial conditional autoregressive models to describe the spatial variations in earthquake risks over the study region during two periods. A family of stochastic algorithms based on a Markov chain Monte Carlo technique is then performed for posterior computations. The probabilistic issue for the changes of earthquake risks after a main shock is also discussed. Finally, the proposed method is applied to the earthquake records for Taiwan before and after the Chi-Chi earthquake.     

附条件逮捕的体系性地位新论

附条件逮捕制度是基于实践需要,经过试点推广并上升到规范层次的一项创新制度,具有非常重要的理论和实践意义。学界和实务界对附条件逮捕颇有争论,理论分析的停滞与实践需要的扩张之间的极大落差,凸显了对附条件逮捕进行重新审视和定位的需要。该文对附条件逮捕制度进行了从前提到条件的层次性剖析,探讨了其与一般逮捕的关系,并试图以概念和体系两个维度来搭建附条件逮捕在法律上的体系性地位。     

Incentives Programs for Girl Children in India: Global Experiences,Local Reflections

ABSTRACT

Success of incentive transfers programs in South America has encouraged policymakers in India to adopt similar demand-driven approaches to reward behaviors that invest in girls and eventually change negative cultural attitudes toward them. Embrace of this approach, however, needs to be nuanced by a closer scrutiny of the gendered impact of such programs; an understanding of the differential sociocultural and public policy contexts; and a questioning of the design of the incentive transfer programs in India. Despite their political uptake, such programs address the practical needs of girls and women while neglecting their strategic interests. Furthermore, without adequate critical engagement, the programs can have unintended consequence of reinforcing gender stereotypes. Rather than importing developmental shortcuts, India should turn its gaze inward to supply-side interventions and homegrown mobilization experiences of the women’s movement.     

Informing the selection of futility stopping thresholds: case study from a late-phase clinical trial

In an environment where (i) potential risks to subjects participating in clinical studies need to be managed carefully, (ii) trial costs are increasing, and (iii) there are limited research resources available, it is necessary to prioritize research projects and sometimes re-prioritize if early indications suggest that a trial has low probability of success. Futility designs allow this re-prioritization to take place. This paper reviews a number of possible futility methods available and presents a case study from a late-phase study of an HIV therapeutic, which utilized conditional power-based stopping thresholds. The two most challenging aspects of incorporating a futility interim analysis into a trial design are the selection of optimal stopping thresholds and the timing of the analysis, both of which require the balancing of various risks. The paper outlines a number of graphical aids that proved useful in explaining the statistical risks involved to the study team. Further, the paper outlines a decision analysis undertaken which combined expectations of drug performance with conditional power calculations in order to produce probabilities of different interim and final outcomes, and which ultimately led to the selection of the final stopping thresholds.     

汉语动态语序结构的优选机制——以动词前处所项与其他语义项的语序关系为例

本文主要讨论汉语语序结构的动态性特征及其制约机制。具体分析了汉语小句中动词前处所项和与之共现率较高的施事项、时间项、与事项、工具项之间的语序关系,讨论了各种语序制约条件并认为它们处于"句法≥语义≥语用≥认知"的等级序列上,该优选评估机制能够比以往研究更全面更系统地解释汉语动态语序结构现象。     

Have cash transfers succeeded in reaching the poor in Latin America and the Caribbean?

We present original estimates of the quality of targeting of conditional cash transfer (CCT) and non‐contributory pension (NCP) programmes in Latin America and the Caribbean. Our contribution is novel because we use both national and international poverty lines; provide differentiated estimates for urban and rural areas; and compare the CCT and NCP programmes. We show that leakage to the non‐poor coexists with pervasive under‐coverage of all poor, including the extreme poor. On average, the CCTs cover only 50.5% of the extreme poor in households with children under 18 years of age. Similarly, the NCPs cover only 50.9% of the extreme poor in households with elderly members who do not receive a contributory pension. At the same time, 40.4% of CCT beneficiaries and 50.1% of NCP beneficiaries are not poor, highlighting the potential need for re‐targeting and re‐certification. In most countries, re‐targeting could produce a substantial double benefit in terms of poverty reduction and fiscal savings.     

Asymptotic Optimality of Sparse Linear Discriminant Analysis with Arbitrary Number of Classes

Many sparse linear discriminant analysis (LDA) methods have been proposed to overcome the major problems of the classic LDA in high‐dimensional settings. However, the asymptotic optimality results are limited to the case with only two classes. When there are more than two classes, the classification boundary is complicated and no explicit formulas for the classification errors exist. We consider the asymptotic optimality in the high‐dimensional settings for a large family of linear classification rules with arbitrary number of classes. Our main theorem provides easy‐to‐check criteria for the asymptotic optimality of a general classification rule in this family as dimensionality and sample size both go to infinity and the number of classes is arbitrary. We establish the corresponding convergence rates. The general theory is applied to the classic LDA and the extensions of two recently proposed sparse LDA methods to obtain the asymptotic optimality.     

择一定理与二层多目标规划的最优性条件

使用关于集值映射的择一定理,在锥一次类凸背景下,对二层多目标规划的最优性条件进行讨论,获得了几个必要条件和充分条件。     

General location multivariate latent variable models for mixed correlated bounded continuous,ordinal, and nominal responses with non-ignorable missing data

Using a multivariate latent variable approach, this article proposes some new general models to analyze the correlated bounded continuous and categorical (nominal or/and ordinal) responses with and without non-ignorable missing values. First, we discuss regression methods for jointly analyzing continuous, nominal, and ordinal responses that we motivated by analyzing data from studies of toxicity development. Second, using the beta and Dirichlet distributions, we extend the models so that some bounded continuous responses are replaced for continuous responses. The joint distribution of the bounded continuous, nominal and ordinal variables is decomposed into a marginal multinomial distribution for the nominal variable and a conditional multivariate joint distribution for the bounded continuous and ordinal variables given the nominal variable. We estimate the regression parameters under the new general location models using the maximum-likelihood method. Sensitivity analysis is also performed to study the influence of small perturbations of the parameters of the missing mechanisms of the model on the maximal normal curvature. The proposed models are applied to two data sets: BMI, Steatosis and Osteoporosis data and Tehran household expenditure budgets.     

Nonparametric Estimation of Nonadditive Random Functions

We present estimators for nonparametric functions that are nonadditive in unobservable random terms. The distributions of the unobservable random terms are assumed to be unknown. We show that when a nonadditive, nonparametric function is strictly monotone in an unobservable random term, and it satisfies some other properties that may be implied by economic theory, such as homogeneity of degree one or separability, the function and the distribution of the unobservable random term are identified. We also present convenient normalizations, to use when the properties of the function, other than strict monotonicity in the unobservable random term, are unknown. The estimators for the nonparametric function and for the distribution of the unobservable random term are shown to be consistent and asymptotically normal. We extend the results to functions that depend on a multivariate random term. The results of a limited simulation study are presented.