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.     

Clustering microarray data using model-based double K-means

The microarray technology allows the measurement of expression levels of thousands of genes simultaneously. The dimension and complexity of gene expression data obtained by microarrays create challenging data analysis and management problems ranging from the analysis of images produced by microarray experiments to biological interpretation of results. Therefore, statistical and computational approaches are beginning to assume a substantial position within the molecular biology area. We consider the problem of simultaneously clustering genes and tissue samples (in general conditions) of a microarray data set. This can be useful for revealing groups of genes involved in the same molecular process as well as groups of conditions where this process takes place. The need of finding a subset of genes and tissue samples defining a homogeneous block had led to the application of double clustering techniques on gene expression data. Here, we focus on an extension of standard K-means to simultaneously cluster observations and features of a data matrix, namely double K-means introduced by Vichi (2000). We introduce this model in a probabilistic framework and discuss the advantages of using this approach. We also develop a coordinate ascent algorithm and test its performance via simulation studies and real data set. Finally, we validate the results obtained on the real data set by building resampling confidence intervals for block centroids.     

The negative binomial–beta Weibull regression model to predict the cure of prostate cancer

In this article, for the first time, we propose the negative binomial–beta Weibull (BW) regression model for studying the recurrence of prostate cancer and to predict the cure fraction for patients with clinically localized prostate cancer treated by open radical prostatectomy. The cure model considers that a fraction of the survivors are cured of the disease. The survival function for the population of patients can be modeled by a cure parametric model using the BW distribution. We derive an explicit expansion for the moments of the recurrence time distribution for the uncured individuals. The proposed distribution can be used to model survival data when the hazard rate function is increasing, decreasing, unimodal and bathtub shaped. Another advantage is that the proposed model includes as special sub-models some of the well-known cure rate models discussed in the literature. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes. We analyze a real data set for localized prostate cancer patients after open radical prostatectomy.