精准扶贫背景下贫困山区旅游扶贫效率研究——以安徽皖南山区18个县(区、市)为例

皖南山区旅游扶贫效率接近中等水平,区域差异大但呈波状缩小趋势。现阶段,旅游扶贫的主要支撑为规模效率,快速的旅游发展、不断扩大的旅游业规模是提升旅游扶贫效率的有效途径。技术进步是导致全要素生产率指数波动的主要因素,旅游体制机制、旅游模式创新是进一步提高其扶贫效率的核心路径。旅游扶贫效率由"局部突出,环线梯度"的点状格局向"四周连片,中部塌陷"的漏斗状格局演化,旅游产业出现外溢。为提升区域旅游扶贫效率,应根据潜力期、朝阳期、黄金期、夕阳期不同的效率形态类型,实施精准的旅游扶贫模式。     

基于主成分分析的我国银行体系稳定性测度研究

通过文献梳理比较,选择较为客观、科学的主成分分析法,并从盈利性、信贷风险、市场风险、流动性四个方面构建系统指标体系,实证测度我国2003年至2017年银行体系稳定性。研究发现,我国银行体系稳定性2003年以来呈倒"U"型态势,大致可分成两个阶段:一是2003年至2012年,随着系列金融改革的红利释放,各项指标逐步趋于好转,稳定性逐步上升,至2012年升至近年来最高水平;二是2013年至2017年,我国经济步入新常态,"三期叠加"矛盾凸显,金融体系稳定性有所下降,防范化解金融风险仍是当前金融工作的重点。     

政务大厅服务效能影响因素实证分析

政务大厅在积极开展创新实践的同时,也存在区域发展不平衡、部门协调难、窗口忙闲不均等诸多问题。量化政务大厅的服务效能,有效识别影响服务效能的关键因素,对于更快更好发挥大厅在"放管服"改革中的作用具有重要的现实意义。文献回顾与理论推演相结合提炼出大厅服务效能评价指标以及效能影响因素指标,基于普查数据量化指标,采用主成分分析法评价服务效能,构建回归模型,同时考虑大厅发展的区域性差异,分别从总体和分区域进行实证分析。研究结果表明,服务模式、生产要素、服务内容均会对大厅服务效能产生影响,且各区域大厅的具体影响因素和因素的影响程度均存在较大差异。基于此,应因地制宜开展大厅服务模式创新;推动大厅建设标准制定;落实审批事项全面进驻大厅,丰富大厅服务种类,优化服务结构。     

Statistical Properties of a Modified Accelerated Design for Phase I Cancer Clinical Trials

We consider the semiparametric profile likelihood inference for the distribution function under doubly censored data. For further developments of the statistical inference based on the profile likelihood ratio and alternative tools such as the score or Wald-type inference, we discuss the structures of the profile likelihood estimators and their derivatives included in the score function and the Fisher function of the profile likelihood, establishing the consistencies of their estimators.     

On Two Types of Breakdown Points of Weighted L 2-median

Zuo (2004) investigated the simplified replacement finite sample breakdown point of weighted L ^p -depth and L ^p -median for some appropriate weight functions. The addition breakdown point of weighted L ^p -depth functions is studied firstly in this article. In addition, for some other weight functions different from those in Zuo (2004 Zuo , Y. ( 2004 ). Robustness of weighted L ^p -depth and L ^p -median . Allgemeines Statistics Archiv. 88 : 215 – 234 . [Google Scholar]), we establish the lower bounds of these two types of breakdown point of weighted L ²-median.     

On the Bias of the Maximum Likelihood Estimator for the Two-Parameter Lomax Distribution

The Lomax (Pareto II) distribution has found wide application in a variety of fields. We analyze the second-order bias of the maximum likelihood estimators of its parameters for finite sample sizes, and show that this bias is positive. We derive an analytic bias correction which reduces the percentage bias of these estimators by one or two orders of magnitude, while simultaneously reducing relative mean squared error. Our simulations show that this performance is very similar to that of a parametric bootstrap correction based on a linear bias function. Three examples with actual data illustrate the application of our bias correction.     

Precise Asymptotics in Complete Moment Convergence of Parameter Estimator in the Gaussian Autoregressive Process

In this article, we study the precise asymptotic behaviors of the least-squares estimator in the Gaussian autoregressive process. Two kinds of complete moment convergence of this estimator can be obtained by the methods of deviation inequalities for this estimator and nonuniform Berry-Esseen bound for martingales.     

Extended BIC for linear regression models with diverging number of relevant features and high or ultra-high feature spaces

In many conventional scientific investigations with high or ultra-high dimensional feature spaces, the relevant features, though sparse, are large in number compared with classical statistical problems, and the magnitude of their effects tapers off. It is reasonable to model the number of relevant features as a diverging sequence when sample size increases. In this paper, we investigate the properties of the extended Bayes information criterion (EBIC) (Chen and Chen, 2008) for feature selection in linear regression models with diverging number of relevant features in high or ultra-high dimensional feature spaces. The selection consistency of the EBIC in this situation is established. The application of EBIC to feature selection is considered in a SCAD cum EBIC procedure. Simulation studies are conducted to demonstrate the performance of the SCAD cum EBIC procedure in finite sample cases.     

The MLE of Geometric Parameter for a Geometric Process

In this article, we provide the MLE of the ratio parameter of a geometric process and discuss its consistency and asymptotic normality.     

Positive False Discovery Rate Estimate in Step-Wise Variable Selection

Selecting predictors to optimize the outcome prediction is an important statistical method. However, it usually ignores the false positives in the selected predictors. In this article, we advocate a conventional stepwise forward variable selection method based on the predicted residual sum of squares, and develop a positive false discovery rate (pFDR) estimate for the selected predictor subset, and a local pFDR estimate to prioritize the selected predictors. This pFDR estimate takes account of the existence of non null predictors, and is proved to be asymptotically conservative. In addition, we propose two views of a variable selection process: an overall and an individual test. An interesting feature of the overall test is that its power of selecting non null predictors increases with the proportion of non null predictors among all candidate predictors. Data analysis is illustrated with an example, in which genetic and clinical predictors were selected to predict the cholesterol level change after four months of tamoxifen treatment, and pFDR was estimated. Our method's performance is evaluated through statistical simulations.