更富裕是否意味着更幸福?基于横截面时间序列数据的分析(2003-2013)

四十年前,经济学家伊斯特林提出了“幸福感悖论”:短时期内个人和国家的财富水平与幸福感都正向相关,而在长期背景下,经济增长并不会显著提高整体幸福感水平。本文使用横跨十年的全国代表性横截面时间序列数据直接检验了财富与幸福感的关系,结果证实了“幸福感悖论”。研究发现,虽然在短时期内,无论是家庭人均收入还是省份的人均生产总值都与幸福感有显著的正相关,但在经济高速发展的十年内(2003-2013),省份人均生产总值的变化与幸福感的变化之间并没有显著相关。     

三元效率区间下决策单元的全局绩效评价

针对现实生活中投入产出数据的不确定性,许多学者提出从乐观和悲观角度计算决策单元的效率区间,但每个效率区间的上、下界值都是决策单元表现的两种极端情况。本文通过引入心态指标衡量决策者的偏好,获得了一个最有可能的效率值,它与上界值、下界值共同组成了三元效率区间。然后改进了两级排序方法:提出了三元有向距离指数,为所有决策单元获得全序化结果。本文引用前人文中的数例验证了该方法是一种更为精确、可全序化的评价、决策方法,可广泛应用于效率测评中。     

京津冀协同视域下金融发展与技术创新的融合效率

金融发展规模、金融结构和金融效率对推动技术创新发展具有积极作用。京津冀协同发展对区域金融发展与创新水平融合提出了更高要求,因而在对比分析京津冀金融发展差异基础上,通过构建包含金融发展与技术创新11项指标的投入产出动态面板数据,应用DEA Malmquist指数模型测度京津冀金融发展与技术创新融合效率,探讨提升技术创新对区域经济发展贡献度的改进建议,以利于更好落实创新驱动,推动京津冀协同发展。     

基于失效情景的应急设施选址评估指标体系与模型

突发事件具有巨大的破坏性、不确定性,应急设施有可能失效,本文研究基于失效情景的应急设施选址评估指标体系与评估模型。首先基于定义的失效情景研究应急设施选址评估指标。构建全局性、可靠性、时效性、均衡性、经济性评估指标。可靠性作为重要评估指标有助于提高应急设施对需求区域的物资保障程度,保证系统的稳定性,采用多重覆盖率刻画。然后,设计具有不同侧重评估目标的一般情景评估指标体系、设施失效情景评估指标体系以及多区域情景评估指标体系。最后,由于应急设施选址评估具有多影响因素特征,涉及输入和输出多个指标的测度,选取处理多输入\多输出问题具有优势的评估方法—数据包络法,对应急设施选址的合理性进行评估。实例验证评估指标体系的实用性和有效性。     

大数据环境下双层分布式融合决策方法

为了解决大数据环境下双层分布式决策中存在的决策推理难题和信息冗余难题,基于该类决策中存在的参与主体庞杂性和业务关系交叉性分析给出了有关推断信息之间存在相关性的假设,并结合证据理论中的基本概率分配函数提出了具有柔性表达优势的推断信息描述机理,在此基础上分别针对上下两层部门构建了能够剔除管理者影响、上层部门影响且可以对部门内部所有决策主体的元推断信息进行科学融合的定理和推论,最后按照"由上至下"的决策秩序构建了大数据环境下双层分布式融合决策的方法步骤。数值对比分析结果表明提出方法具有科学性和可行性,有利于拓展大数据环境下组织管理决策问题的解决思路,探索处理不完备性数据、相关性数据的"大"模式。     

Likelihood Ratio Type Two‐Sample Tests for Current Status Data

Abstract. We introduce fully non‐parametric two‐sample tests for testing the null hypothesis that the samples come from the same distribution if the values are only indirectly given via current status censoring. The tests are based on the likelihood ratio principle and allow the observation distributions to be different for the two samples, in contrast with earlier proposals for this situation. A bootstrap method is given for determining critical values and asymptotic theory is developed. A simulation study, using Weibull distributions, is presented to compare the power behaviour of the tests with the power of other non‐parametric tests in this situation.     

Estimation of Stratified Mark‐Specific Proportional Hazards Models with Missing Marks

Abstract. An objective of randomized placebo‐controlled preventive HIV vaccine efficacy trials is to assess the relationship between the vaccine effect to prevent infection and the genetic distance of the exposing HIV to the HIV strain represented in the vaccine construct. Motivated by this objective, recently a mark‐specific proportional hazards (PH) model with a continuum of competing risks has been studied, where the genetic distance of the transmitting strain is the continuous ‘mark’ defined and observable only in failures. A high percentage of genetic marks of interest may be missing for a variety of reasons, predominantly because rapid evolution of HIV sequences after transmission before a blood sample is drawn from which HIV sequences are measured. This research investigates the stratified mark‐specific PH model with missing marks where the baseline functions may vary with strata. We develop two consistent estimation approaches, the first based on the inverse probability weighted complete‐case (IPW) technique, and the second based on augmenting the IPW estimator by incorporating auxiliary information predictive of the mark. We investigate the asymptotic properties and finite‐sample performance of the two estimators, and show that the augmented IPW estimator, which satisfies a double robustness property, is more efficient.     

Restricted maximum likelihood estimation of joint mean‐covariance models

The class of joint mean‐covariance models uses the modified Cholesky decomposition of the within subject covariance matrix in order to arrive to an unconstrained, statistically meaningful reparameterisation. The new parameterisation of the covariance matrix has two sets of parameters that separately describe the variances and correlations. Thus, with the mean or regression parameters, these models have three sets of distinct parameters. In order to alleviate the problem of inefficient estimation and downward bias in the variance estimates, inherent in the maximum likelihood estimation procedure, the usual REML estimation procedure adjusts for the degrees of freedom lost due to the estimation of the mean parameters. Because of the parameterisation of the joint mean covariance models, it is possible to adapt the usual REML procedure in order to estimate the variance (correlation) parameters by taking into account the degrees of freedom lost by the estimation of both the mean and correlation (variance) parameters. To this end, here we propose adjustments to the estimation procedures based on the modified and adjusted profile likelihoods. The methods are illustrated by an application to a real data set and simulation studies. The Canadian Journal of Statistics 40: 225–242; 2012 © 2012 Statistical Society of Canada     

Regularized Posteriors in Linear Ill‐Posed Inverse Problems

Abstract. We study the Bayesian solution of a linear inverse problem in a separable Hilbert space setting with Gaussian prior and noise distribution. Our contribution is to propose a new Bayes estimator which is a linear and continuous estimator on the whole space and is stronger than the mean of the exact Gaussian posterior distribution which is only defined as a measurable linear transformation. Our estimator is the mean of a slightly modified posterior distribution called regularized posterior distribution. Frequentist consistency of our estimator and of the regularized posterior distribution is proved. A Monte Carlo study and an application to real data confirm good small‐sample properties of our procedure.     

Error analysis of stochastic flight trajectory prediction models

This paper focuses on the analysis of errors between a flight trajectory prediction model and flight data. A novel stochastic prediction flight model is compared with the popular fly-by and fly-over turn models. The propagated error is measured using either spatial coordinates or angles. Depending on the case, the distribution of error is estimated and confidence bounds for the linear and directional mean are provided for all three stochastic flight models.