Empirical Uncertain Bayes Methods in Area‐level Models

Random effects model can account for the lack of fitting a regression model and increase precision of estimating area‐level means. However, in case that the synthetic mean provides accurate estimates, the prior distribution may inflate an estimation error. Thus, it is desirable to consider the uncertain prior distribution, which is expressed as the mixture of a one‐point distribution and a proper prior distribution. In this paper, we develop an empirical Bayes approach for estimating area‐level means, using the uncertain prior distribution in the context of a natural exponential family, which we call the empirical uncertain Bayes (EUB) method. The regression model considered in this paper includes the Poisson‐gamma and the binomial‐beta, and the normal‐normal (Fay–Herriot) model, which are typically used in small area estimation. We obtain the estimators of hyperparameters based on the marginal likelihood by using a well‐known expectation‐maximization algorithm and propose the EUB estimators of area means. For risk evaluation of the EUB estimator, we derive a second‐order unbiased estimator of a conditional mean squared error by using some techniques of numerical calculation. Through simulation studies and real data applications, we evaluate a performance of the EUB estimator and compare it with the usual empirical Bayes estimator.     

Uniform in bandwidth consistency for various kernel estimators involving functional data

The paper investigates various nonparametric models including regression, conditional distribution, conditional density and conditional hazard function, when the covariates are infinite dimensional. The main contribution is to prove uniform in bandwidth asymptotic results for kernel estimators of these functional operators. Then, the application issues, involving data-driven bandwidth selection, are discussed.     

Collapsibility of Conditional Graphical Models

Abstract. In this paper, we consider two kinds of collapsibility, that is, the model‐collapsibility and the estimate‐collapsibility, of conditional graphical models for multidimensional contingency tables. We show that these two definitions are equivalent, and propose a sufficient and necessary condition for them in terms of the interaction graph, which allows the collapsibility to be characterized and judged intuitively and conveniently.     

A Qualitative Evaluation of a Conditional Cash Transfer Program in Turkey: The Beneficiaries’ and Key Informants’ Perspectives

ABSTRACT

Since the 2001 economic crisis, conditional cash transfers (CCTs) have been provided to poor families in Turkey to improve education and health outcomes of children. Under the framework of CCT programs, grants provide an incentive for poor households to use available basic social services. The aim of this study is to explore beneficiaries’ perspectives in relation to a CCT program in Turkey. The in-depth study design entailed semistructured qualitative interviews with beneficiary households and key informants. A total of 397 in-depth interviews were conducted. Analysis results suggest that the CCT program not only positively affected health and educational outcomes, but also contributed to the empowerment of women and improved the self-esteem and self-confidence of beneficiary children. Recommendations for future research are made including the need for an impact evaluation analysis that employs quantitative research methods designed to improve the provision and quality of the basic social services.     

A diagnostic statistic for functional-coefficient autoregressive models

In this paper, functional coefficient autoregressive (FAR) models proposed by Chen and Tsay (1993) are considered. We propose a diagnostic statistic for FAR models constructed by comparing between parametric and nonparametric estimators of the functional form of the FAR models. We show asymptotic properties of our statistic mathematically and it can be applied to the estimation of the delay parameter and the specification of the functional form of FAR models.     

通过试点与实践推进制度创新——以L县检察院附条件不起诉的试点为样本

中国刑事诉讼制度正在发生深刻的变迁,现代性制度正在实践中艰难形成。L县检察院关于附条件不起诉制度的探索与实践,便体现了这种制度变迁的方式与实践创新。地方性司法机构的"试点性改革"与"试错性试验",无疑推动了中国刑事诉讼制度的发展。应保持一种开放的胸襟与开放的心态,通过实践中的不断试点改革来确认与完善新的制度设置,以走出一条推进中国式法治建设有效、成功的道路。     

Enriching Resources for the Poor: Lessons from the Philippines

The paper provides an overview of the poverty situation in the Philippines, followed by a brief profile and assessment of two anti‐poverty programs: the Kapit‐Bisig Laban sa Kahirapan‐Comprehensive and Integrated Delivery of Social Services (KALAHI‐CIDSS) program and the Pantawid Pamilyang Pilipino Program (4Ps). Official statistics disclose that the global economic crisis in 2008 adversely affected Filipino households, leading to higher levels of poverty especially in rural areas. In response, government introduced an Economic Resiliency Program with social protection as one component. Sixty‐six social protection programs were launched, including the two under review. KALAHI‐CIDSS and the 4Ps apply poverty reduction strategies that focus on the development of human and social capital, rather than economic capital. They involve processes and relationships that aim to improve the other dimensions of poverty, such as lack of access to opportunities or deprivation of basic human necessities. Qualitative and quantitative reports describe generally favorable outcomes from both programs. However, these gains at the household level need to be supported by relevant policy, enhanced public services and badly‐needed infrastructure programs from government, in order to sustain achievements culled from social protection programs and attain broader developmental impacts.     

Conditional Inference With a Functional Nuisance Parameter

This paper shows that the problem of testing hypotheses in moment condition models without any assumptions about identification may be considered as a problem of testing with an infinite‐dimensional nuisance parameter. We introduce a sufficient statistic for this nuisance parameter in a Gaussian problem and propose conditional tests. These conditional tests have uniformly correct asymptotic size for a large class of models and test statistics. We apply our approach to construct tests based on quasi‐likelihood ratio statistics, which we show are efficient in strongly identified models and perform well relative to existing alternatives in two examples.     

供需不确定条件下基于CVaR的零售商库存鲁棒优化模型

在经典报童模型下考虑供应和需求不确定性,研究了具有风险厌恶的零售商库存优化问题。采用条件风险值（CVaR）对库存绩效进行度量,构建了基于CVaR的零售商库存运作模型;在此基础上,考虑上游供应商供货能力和下游市场需求不确定性,并采用一系列未知概率的离散情景进行描述,给出了供需不确定条件下基于CVaR的零售商库存鲁棒优化模型。进一步,采用区间不确定集对未知情景概率进行建模,给出了基于最大最小准则的鲁棒对应模型。针对同时考虑供需不确定性导致的模型非凸性,采用标准对偶理论将其转化为易于求解的数学规划问题。最后,通过数值计算分析了不同风险厌恶程度和不确定性程度对零售商库存决策以及库存绩效的影响。结果表明,供需不确定性的存在虽然会导致零售商库存绩效损失,但损失值较小。特别地,依据文中模型得到的鲁棒库存策略在多数情况下能够保证零售商获得更优的库存绩效。此外,不确定性和风险厌恶程度的增加虽然会影响零售商库存决策和运作绩效,但在同等风险厌恶态度下,随着不确定性程度的增加,基于文中方法得到的鲁棒库存策略仍能确保零售商获得理想的库存绩效,表明文中所建模型在应对供需不确定性方面具有良好的鲁棒性。     

Linear Increments with Non‐monotone Missing Data and Measurement Error

Linear increments (LI) are used to analyse repeated outcome data with missing values. Previously, two LI methods have been proposed, one allowing non‐monotone missingness but not independent measurement error and one allowing independent measurement error but only monotone missingness. In both, it was suggested that the expected increment could depend on current outcome. We show that LI can allow non‐monotone missingness and either independent measurement error of unknown variance or dependence of expected increment on current outcome but not both. A popular alternative to LI is a multivariate normal model ignoring the missingness pattern. This gives consistent estimation when data are normally distributed and missing at random (MAR). We clarify the relation between MAR and the assumptions of LI and show that for continuous outcomes multivariate normal estimators are also consistent under (non‐MAR and non‐normal) assumptions not much stronger than those of LI. Moreover, when missingness is non‐monotone, they are typically more efficient.