Conditional covariance penalties for mixed models

The prediction error for mixed models can have a conditional or a marginal perspective depending on the research focus. We introduce a novel conditional version of the optimism theorem for mixed models linking the conditional prediction error to covariance penalties for mixed models. Different possibilities for estimating these conditional covariance penalties are introduced. These are bootstrap methods, cross-validation, and a direct approach called Steinian. The behavior of the different estimation techniques is assessed in a simulation study for the binomial-, the t-, and the gamma distribution and for different kinds of prediction error. Furthermore, the impact of the estimation techniques on the prediction error is discussed based on an application to undernutrition in Zambia.     

Conditional Akaike Information Criteria for a Class of Poisson Mixture Models with Random Effects

Focusing on the model selection problems in the family of Poisson mixture models (including the Poisson mixture regression model with random effects and zero‐inflated Poisson regression model with random effects), the current paper derives two conditional Akaike information criteria. The criteria are the unbiased estimators of the conditional Akaike information based on the conditional log‐likelihood and the conditional Akaike information based on the joint log‐likelihood, respectively. The derivation is free from the specific parametric assumptions about the conditional mean of the true data‐generating model and applies to different types of estimation methods. Additionally, the derivation is not based on the asymptotic argument. Simulations show that the proposed criteria have promising estimation accuracy. In addition, it is found that the criterion based on the conditional log‐likelihood demonstrates good model selection performance under different scenarios. Two sets of real data are used to illustrate the proposed method.     

Dynamic Forecasting Conditional Probability of Bombing Attacks Based on Time‐Series and Intervention Analysis

In recent years, various types of terrorist attacks occurred, causing worldwide catastrophes. According to the Global Terrorism Database (GTD), among all attack tactics, bombing attacks happened most frequently, followed by armed assaults. In this article, a model for analyzing and forecasting the conditional probability of bombing attacks (CPBAs) based on time‐series methods is developed. In addition, intervention analysis is used to analyze the sudden increase in the time‐series process. The results show that the CPBA increased dramatically at the end of 2011. During that time, the CPBA increased by 16.0% in a two‐month period to reach the peak value, but still stays 9.0% greater than the predicted level after the temporary effect gradually decays. By contrast, no significant fluctuation can be found in the conditional probability process of armed assault. It can be inferred that some social unrest, such as America's troop withdrawal from Afghanistan and Iraq, could have led to the increase of the CPBA in Afghanistan, Iraq, and Pakistan. The integrated time‐series and intervention model is used to forecast the monthly CPBA in 2014 and through 2064. The average relative error compared with the real data in 2014 is 3.5%. The model is also applied to the total number of attacks recorded by the GTD between 2004 and 2014.     

Quantile Regression for Location‐Scale Time Series Models with Conditional Heteroscedasticity

This paper considers quantile regression for a wide class of time series models including autoregressive and moving average (ARMA) models with asymmetric generalized autoregressive conditional heteroscedasticity errors. The classical mean‐variance models are reinterpreted as conditional location‐scale models so that the quantile regression method can be naturally geared into the considered models. The consistency and asymptotic normality of the quantile regression estimator is established in location‐scale time series models under mild conditions. In the application of this result to ARMA‐generalized autoregressive conditional heteroscedasticity models, more primitive conditions are deduced to obtain the asymptotic properties. For illustration, a simulation study and a real data analysis are provided.     

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.