Efficient Estimation of an Additive Quantile Regression Model

Abstract. In this paper, two non‐parametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a more viable alternative to existing kernel‐based approaches. The second estimator involves sequential fitting by univariate local polynomial quantile regressions for each additive component with the other additive components replaced by the corresponding estimates from the first estimator. The purpose of the extra local averaging is to reduce the variance of the first estimator. We show that the second estimator achieves oracle efficiency in the sense that each estimated additive component has the same variance as in the case when all other additive components were known. Asymptotic properties are derived for both estimators under dependent processes that are strictly stationary and absolutely regular. We also provide a demonstrative empirical application of additive quantile models to ambulance travel times.     

Choosing Priors for Constrained Analysis of Variance: Methods Based on Training Data

Abstract. This article combines the best of both objective and subjective Bayesian inference in specifying priors for inequality and equality constrained analysis of variance models. Objectivity can be found in the use of training data to specify a prior distribution, subjectivity can be found in restrictions on the prior to formulate models. The aim of this article is to find the best model in a set of models specified using inequality and equality constraints on the model parameters. For the evaluation of the models an encompassing prior approach is used. The advantage of this approach is that only a prior for the unconstrained encompassing model needs to be specified. The priors for all constrained models can be derived from this encompassing prior. Different choices for this encompassing prior will be considered and evaluated.     

Testing for qualitative interaction of multiple sources of informative dropout in longitudinal data

Longitudinal studies suffer from patient dropout. The dropout process may be informative if there exists an association between dropout patterns and the rate of change in the response over time. Multiple patterns are plausible in that different causes of dropout might contribute to different patterns. These multiple patterns can be dichotomized into two groups: quantitative and qualitative interaction. Quantitative interaction indicates that each of the multiple sources is biasing the estimate of the rate of change in the same direction, although with differing magnitudes. Alternatively, qualitative interaction results in the multiple sources biasing the estimate of the rate of change in opposing directions. Qualitative interaction is of special concern, since it is less likely to be detected by conventional methods and can lead to highly misleading slope estimates. We explore a test for qualitative interaction based on simultaneous confidence intervals. The test accommodates the realistic situation where reasons for dropout are not fully understood, or even entirely unknown. It allows for an additional level of clustering among participating subjects. We apply these methods to a study exploring tumor growth rates in mice as well as a longitudinal study exploring rates of change in cognitive functioning for Alzheimer's patients.     

A robust version of the hurdlemodel

The excess of zeros is not a rare feature in count data. Statisticians advocate the Poisson-type hurdle model (among other techniques) as an interesting approach to handle this data peculiarity. However, the frequency of gross errors and the complexity intrinsic to some considered phenomena may render this classical model unreliable and too limiting. In this paper, we develop a robust version of the Poisson hurdle model by extending the robust procedure for GLM of Cantoni and Ronchetti (2001) to the truncated Poisson regression model. The performance of the new robust approach is then investigated via a simulation study, a real data application and a sensitivity analysis. The results show the reliability of the new technique in the neighborhood of the truncated Poisson model. This robust modelling approach is therefore a valuable complement to the classical one, providing a tool for reliable statistical conclusions and to take more effective decisions.     

Approximate bounded influence estimation for longitudinal data with outliers and measurement errors

Mixed effects models or random effects models are popular for the analysis of longitudinal data. In practice, longitudinal data are often complex since there may be outliers in both the response and the covariates and there may be measurement errors. The likelihood method is a common approach for these problems but it can be computationally very intensive and sometimes may even be computationally infeasible. In this article, we consider approximate robust methods for nonlinear mixed effects models to simultaneously address outliers and measurement errors. The approximate methods are computationally very efficient. We show the consistency and asymptotic normality of the approximate estimates. The methods can also be extended to missing data problems. An example is used to illustrate the methods and a simulation is conducted to evaluate the methods.     

货币政策与财政政策的分区域产业效应比较

 在具有总量效应的同时,货币政策和财政政策在产业效应方面各自表现如何？本文运用1990～2008年东、中、西部地区的动态面板数据模型进行实证分析,结果显示：东部与中部地区的货币政策和财政政策都存在产业效应,而西部地区货币政策和财政政策的效应均不理想,同时,货币政策和财政政策对三次产业的效应都存在明显的区域性。并且,结合相关理论,文章从产业和区域两个层面对实证结果予以分析,揭示出产业间在增量资本产出率和政府扶持力度以及区域间在银行集中度和基础设施投资方面存在的差异,是实证结果的主要致因。     

Still Partners After All These Years: Ulrich's and ISSN

Since 2000, an Ulrich's Periodicals Directory (Ulrich's) employee has been located in the Library of Congress's U.S. ISSN Center at the Library of Congress to assign ISSN and create records for both Ulrich's and the international ISSN Register/CONSER database. How this arrangement came about, how it has succeeded over the past ten years, and the broad potential for this partnership becoming a model in both organizations for future automated data sharing and data linking is the subject of this article.     

数据资产证券化探索的法律困境与解决路径

数据已成为推动经济发展的核心驱动力,盘活数据资产价值、促进数据资产运用是当前推进数据要素市场化配置、全面建设数字中国的主要目标。由于数据价值实现进程中受到资金与技术投入的制约,数据潜能释放面临瓶颈。在此背景下,数据资产证券化作为数据资产与证券融资结合的创新业态,能够为企业激活数据资产价值拓展融资渠道、降低融资成本,所以,一经提出便备受关注并持续传递新动向。目前,这一证券新模式正处于初步探索阶段,有必要基于数据资源化到数据资产化再至数据资本化的三重价值演进形态,释明其价值实现的内在理路,即是历经初始生成数据资源到加工获取数据资产再到实现数据资产证券化的动态实践,体现了数据要素价值增值的“变现”之路。在此动态进程中,数据要素的独特属性使得数据资产证券化区别于传统资产证券化,呈现出与新兴科技尤其是区块链技术的深度联系,其技术原生特性有助于促进证券全流程的数字化重塑。通过检视当前“数据+资本”双要素耦合的证券新模式发展现状,发现数据资产证券化探索存在产权规范不明、交易机制缺失、监督管理失配等诸多问题,成为激活数据资产价值的掣肘因素。鉴于此,立足兼顾金融创新与风险防控的二元目标,应从法律与技术协...     

中文编目数据工作过程中难以控制因素的分析

现阶段,随着图书馆馆藏图书的增多,使中文编目数据的工作量逐渐增大,为了进一步提高工作效率和质量,有必要对中文编目数据过程中难以控制的因素进行分析,并采取相应的手段加以控制,就中文编目数据工作过程中难以控制的因素展开探讨。     

西部地区工业竞争力综合评价

本文基于面板数据的因子分析,从经济效益、科技与人才、能源与环保三个维度对西部十一省工业综合竞争力进行评估。结果表明,西部地区工业竞争力总体水平不高且不平衡明显。其中四川、重庆、陕西、广西、云南是西部具有较强的工业竞争力的地区,而贵州、甘肃、内蒙古、新疆、青海、宁夏竞争力较弱。因此要有针对性地实施发展规划,以促进西部地区工业差距缩小与总体发展水平的提升。