上党神话与长治旅游业之间的共生关系研究

一个地区的旅游业发展与该地的人文、地理、环境等有很大联系。近年来，随着长治旅游业的不断发展，上党神话作为长治地区特有的人文内涵与长治旅游业之间逐渐形成了相互影响、相互促进、相互合作和相互依赖的共生关系。上党神话在长治旅游业的旅游景区方面赋予了神秘的色彩，在旅游产品方面丰富了本地旅游产品的内涵，在旅游需求方面扩展了长治旅游消费市场；同时，长治旅游业对上党神话的普及、发展及延伸也发挥了补充作用。     

A robust and efficient estimation method for nonparametric models with jump points

Nonparametric models with jump points have been considered by many researchers. However, most existing methods based on least squares or likelihood are sensitive when there are outliers or the error distribution is heavy tailed. In this article, a local piecewise-modal method is proposed to estimate the regression function with jump points in nonparametric models, and a piecewise-modal EM algorithm is introduced to estimate the proposed estimator. Under some regular conditions, the large-sample theory is established for the proposed estimators. Several simulations are presented to evaluate the performances of the proposed method, which shows that the proposed estimator is more efficient than the local piecewise-polynomial regression estimator in the presence of outliers or heavy tail error distribution. What is more, the proposed procedure is asymptotically equivalent to the local piecewise-polynomial regression estimator under the assumption that the error distribution is a Gaussian distribution. The proposed method is further illustrated via the sea-level pressures.     

Outlier detection in high-dimensional regression model

An outlier is defined as an observation that is significantly different from the others in its dataset. In high-dimensional regression analysis, datasets often contain a portion of outliers. It is important to identify and eliminate the outliers for fitting a model to a dataset. In this paper, a novel outlier detection method is proposed for high-dimensional regression problems. The leave-one-out idea is utilized to construct a novel outlier detection measure based on distance correlation, and then an outlier detection procedure is proposed. The proposed method enjoys several advantages. First, the outlier detection measure can be simply calculated, and the detection procedure works efficiently even for high-dimensional regression data. Moreover, it can deal with a general regression, which does not require specification of a linear regression model. Finally, simulation studies show that the proposed method behaves well for detecting outliers in high-dimensional regression model and performs better than some other competing methods.     

基于“重要性-影响力”框架的非首都功能疏解路径研究——以北京市动物园服装批发市场搬迁为例

利益相关者分析法是分析复杂管理问题的重要方法,广泛应用于企业管理、项目管理及公共政策领域。随着北京市非首都核心功能的疏解思路的提出,作为低端产业代表的动物园批发市场的外迁势在必行。运用"重要性-影响力"利益相关者分析框架,对动批搬迁政策中涉及的利益相关者进行界定,分别分析其利益诉求并进行合理划分,依据各利益相关者对政策的重要性程度和影响力大小以及各相关者之间的利益冲突分别制定解决对策,同时将各种外部阻力纳入考虑范围,为北京市动物园批发市场搬迁提出可行路径,以期为其他类似公共政策制定问题提供参考。     

Robust bootstrap estimates in heteroscedastic semi-varying coefficient models and applications in analyzing Australia CPI data

This article deals with the estimation of the parametric component, which is of primary interest, in the heteroscedastic semi-varying coefficient models. Based on the bootstrap technique, we present a procedure for estimating the parameters, which can provide a reliable approximation to the asymptotic distribution of the profile least-square (PLS) estimator. Furthermore, a bootstrap-type estimator of covariance matrix is developed, which is proved to be a consistent estimator of the covariance matrix. Moreover, some simulation experiments are conducted to evaluate the finite sample performance for the proposed methodology. Finally, the Australia CPI dataset is analyzed to demonstrate the application of the methods.     

空气中多种醛酮液相色谱法侧定的研究

本文研究了空气中多种醛酮的测定。目前国内仅对空气中甲醛的测定研究较多,国外报导的方法需用萃取、浓缩等一系列前处理过程,方法麻烦,易引入杂质造成污染。本文报导的方法系采用2.4—二硝基苯肼的甲醇强酸溶液吸收空气中醛酮直接注入液相色谱仪中进行分离测定。研究表明:本法分离效果好,重复性好,方法简便,速度快,测定的精密度和准确度符合微量分析误差的要求。     

Bayesian bridge-randomized penalized quantile regression estimation for linear regression model with AP(q) perturbation

Bridge penalized regression has many desirable statistical properties such as unbiasedness, sparseness as well as ‘oracle’. In Bayesian framework, bridge regularized penalty can be implemented based on generalized Gaussian distribution (GGD) prior. In this paper, we incorporate Bayesian bridge-randomized penalty and its adaptive version into the quantile regression (QR) models with autoregressive perturbations to conduct Bayesian penalization estimation. Employing the working likelihood of the asymmetric Laplace distribution (ALD) perturbations, the Bayesian joint hierarchical models are established. Based on the mixture representations of the ALD and generalized Gaussian distribution (GGD) priors of coefficients, the hybrid algorithms based on Gibbs sampler and Metropolis-Hasting sampler are provided to conduct fully Bayesian posterior estimation. Finally, the proposed Bayesian procedures are illustrated by some simulation examples and applied to a real data application of the electricity consumption.     

基于统计检验的企业生命周期判定方法比较

文章选取32家中国上市汽车企业作为研究对象,分别用综合指标法和主成分分析法对企业所处生命周期进行判定,再利用Wilcoxon符号秩检验和kappa—致性检验对两种判定结果进行检验对比,从而得出结论:综合指标法和主成分分析法均科学有效,但在具体应用时各有所长.Wilcoxon符号秩检验和kappa—致性检验叠加运用效果较好,且检验结果具有一致性.     

A new confidence interval in errors-in-variables model with known error variance

This paper considers constructing a new confidence interval for the slope parameter in the structural errors-in-variables model with known error variance associated with the regressors. Existing confidence intervals are so severely affected by Gleser–Hwang effect that they are subject to have poor empirical coverage probabilities and unsatisfactory lengths. Moreover, these problems get worse with decreasing reliability ratio which also result in more frequent absence of some existing intervals. To ease these issues, this paper presents a fiducial generalized confidence interval which maintains the correct asymptotic coverage. Simulation results show that this fiducial interval is slightly conservative while often having average length comparable or shorter than the other methods. Finally, we illustrate these confidence intervals with two real data examples, and in the second example some existing intervals do not exist.     

Diagnostics for quasi-likelihood nonlinear models

Quasi-likelihood nonlinear models (QLNMs) are an extension of generalized linear model and include a widen class of models as special cases. This article investigates some diagnostic methods in QLNMs. An equivalency between a case-deletion model and a mean-shift outlier model in QLNM is established. Two simulation study and a real dataset are used to illustrate the proposed diagnostic methods.