Interval estimation for the breakpoint in segmented regression: a smoothed score‐based approach

This paper is concerned with interval estimation for the breakpoint parameter in segmented regression. We present score‐type confidence intervals derived from the score statistic itself and from the recently proposed gradient statistic. Due to lack of regularity conditions of the score, non‐smoothness and non‐monotonicity, naive application of the score‐based statistics is unfeasible and we propose to exploit the smoothed score obtained via induced smoothing. We compare our proposals with the traditional methods based on the Wald and the likelihood ratio statistics via simulations and an analysis of a real dataset: results show that the smoothed score‐like statistics perform in practice somewhat better than competitors, even when the model is not correctly specified.     

Bayesian analysis of a multiple-recapture model

In the Bayesian analysis of a multiple-recapture census, different diffuse prior distributions can lead to markedly different inferences about the population size N. Through consideration of the Fisher information matrix it is shown that the number of captures in each sample typically provides little information about N. This suggests that if there is no prior information about capture probabilities, then knowledge of just the sample sizes and not the number of recaptures should leave the distribution of Nunchanged. A prior model that has this property is identified and the posterior distribution is examined. In particular, asymptotic estimates of the posterior mean and variance are derived. Differences between Bayesian and classical point and interval estimators are illustrated through examples.     

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.     

Subjective priors for the dirichlet process

This paper reviews difficulties with the interpretation and use of the prior parameter u required in the Dirichlet approach to nonpararnetric Bayesian statistics. Two subjective prior distributions are introduced and studied. These priors are obtained computationally by requiring that the experimenter specify certain constraints.     

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.     

Bayesian inference for zero-and-one-inflated geometric distribution regression model using Pólya-Gamma latent variables

Abstract

In the fields of internet financial transactions and reliability engineering, there could be more zero and one observations simultaneously. In this paper, considering that it is beyond the range where the conventional model can fit, zero-and-one-inflated geometric distribution regression model is proposed. Ingeniously introducing Pólya-Gamma latent variables in the Bayesian inference, posterior sampling with high-dimensional parameters is converted to latent variables sampling and posterior sampling with lower-dimensional parameters, respectively. Circumventing the need for Metropolis-Hastings sampling, the sample with higher sampling efficiency is obtained. A simulation study is conducted to assess the performance of the proposed estimation for various sample sizes. Finally, a doctoral dissertation data set is analyzed to illustrate the practicability of the proposed method, research shows that zero-and-one-inflated geometric distribution regression model using Pólya-Gamma latent variables can achieve better fitting results.