高质量发展指标体系构建及测度——以河北省为例

我国经济已由高速增长阶段转向高质量发展阶段。准确测度经济发展现状,需要构建高质量发展指标体系。根据高质量发展的内涵,构建了包含6个一级指标和24个二级指标构成的高质量发展指标体系,并以河北省为例运用2005-2016年经济运行数据进行实证测算。结果表明,河北省经济发展质量总体呈波动上升态势,但各维度发展并不均衡,经济活力和生态友好两个维度指标持续向好,而创新驱动、结构优化、民生改善这三个维度指标都出现了下滑和较大波动的情况,成为制约高质量发展的短板。     

制造业集聚对新型城镇化的影响研究——基于空间溢出效应的视角

文章依据2014—2020年《国家新型城镇化规划》,构建了中国新型城镇化水平综合评价体系,并基于熵权法测算了2003—2015年中国大陆31个省份新型城镇化水平。在此基础上,通过构建空间杜宾模型,考察了中国制造业集聚的空间交互效应,并在控制了对外出口、金融发展以及财政支出的情况下研究了制造业集聚对新型城镇化的影响。研究结果表明：中国制造业集聚具有显著的空间依赖特征,表现出明显的"集聚俱乐部"现象。制造业集聚显著地推动了中国新型城镇化建设,同时其空间溢出效应明显,在推动本地区新型城镇化的同时还能有力地带动周边地区新型城镇化的发展。地方财政支出的增加有助于显著推动新型城镇化建设,并且地区之间财政行为存在空间互动,邻近地区通过扩大财政支出也能刺激本地区的新型城镇化建设。同时,对外出口和金融发展也是新型城镇化建设中不可或缺的力量,但其空间溢出效应却不明显,并未对周边地区产生明显的影响。对此,建议应该以产业集聚区作为城市功能外溢的核心地区,不断完善集聚区相关配套的基础设施和公共服务体系,实现产城融合的良性发展态势。     

王国维“通人之学”与“专人之学”的尺短寸长

王国维“通人之学”“专人之学”等学术思想是中国学术研究转型的重大创新,通过对王国维学术成长的梳理、学术成果的分析、学术思想的论证,对“静安之学”进行了全面系统的研究。研究认为,王国维凭借厚实的知识贮备,批判地继承西方哲学理论与中国古代哲学思想,在构建中国大文科的学科体系、研究文史哲的学术体系和中国学者的学术话语体系方面做出了全面系统实践,取得重大进展和开创性成果,堪称开一代风气的大学者;王国维以创辟胜解、通方知类的学术思辨,创立二重证据方法,探索出一条由“通人之学”到“专人之学”学问研究蹊径。系统审读、反思静安之学,对我们严肃读书风气、匡正学问研究态度、提倡学术争鸣、甄别解决知识产权争端、清除学术造假现象、规范学术研究流程,能够起到见贤思齐、弃旧图新之作用。     

A New Goodness-of-Fit Test Based on the Empirical Characteristic Function

In this article, we present a goodness-of-fit test for a distribution based on some comparisons between the empirical characteristic function c_n(t) and the characteristic function of a random variable under the simple null hypothesis, c₀(t). We do this by introducing a suitable distance measure. Empirical critical values for the new test statistic for testing normality are computed. In addition, the new test is compared via simulation to other omnibus tests for normality and it is shown that this new test is more powerful than others.     

Inverse Regression in Binary Response LDV Model

Sliced inverse regression, a link-free and distribution-free method, is applied to binary response limited dependent variable models. An inverse regression property of binary response LDV model is found. Based on this property, if the distributions of X _j (j = 1, 2,…, p) satisfy the linearity condition, then β can be estimated up to a positive multiplicative scalar without any assumptions on the distribution of error ε. Moreover, the estimator can be proved to be asymptotically normal based on which testing hypotheses are considered. Simulations results are reported.     

Generalized Linear Failure Rate Distribution

The exponential and Rayleigh are the two most commonly used distributions for analyzing lifetime data. These distributions have several desirable properties and nice physical interpretations. Unfortunately, the exponential distribution only has constant failure rate and the Rayleigh distribution has increasing failure rate. The linear failure rate distribution generalizes both these distributions which may have non increasing hazard function also. This article introduces a new distribution, which generalizes linear failure rate distribution. This distribution generalizes the well-known (1) exponential distribution, (2) linear failure rate distribution, (3) generalized exponential distribution, and (4) generalized Rayleigh distribution. The properties of this distribution are discussed in this article. The maximum likelihood estimates of the unknown parameters are obtained. A real data set is analyzed and it is observed that the present distribution can provide a better fit than some other very well-known distributions.     

Estimation of Survivorship Function Based on Doubly Censored Data with Application to HIV/AIDS

Left censoring concept has been defined in different ways in statistical applications. Turnbull (1974 Turnbull , B. W. ( 1974 ). Nonparametric estimation of a survivorship function with doubly censored data . J. Amer. Statist. Assoc. 69 : 169 – 173 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) defines it in a particular way. Whereas in recent literature, especially in epidemiological studies, it has been defined in another way. This difference between the two approaches is the main reason that despite simplicity, Turnbull method cannot be applicable in all cases of doubly censored data. In this article we present a modified Turnbull method for analysis of doubly censored data adequate with recent definition. Comparison has been done with other statistical methods, including imputation estimator, full likelihood-based and conditional likelihood-based approach using Iranian HIV data.     

Inference for a Simple Step-Stress Model with Type-I Censoring and Lognormally Distributed Lifetimes

Accelerated life-testing (ALT) is a very useful technique for examining the reliability of highly reliable products. It allows the experimenter to obtain failure data more quickly at increased stress levels than under normal operating conditions. A step-stress model is one special class of ALT, and in this article we consider a simple step-stress model under the cumulative exposure model with lognormally distributed lifetimes in the presence of Type-I censoring. We then discuss inferential methods for the unknown parameters of the model by the maximum likelihood estimation method. Some numerical methods, such as the Newton–Raphson and quasi-Newton methods, are discussed for solving the corresponding non-linear likelihood equations. Next, we discuss the construction of confidence intervals for the unknown parameters based on (i) the asymptotic normality of the maximum likelihood estimators (MLEs), and (ii) parametric bootstrap resampling technique. A Monte Carlo simulation study is carried out to examine the performance of these methods of inference. Finally, a numerical example is presented in order to illustrate all the methods of inference developed here.     

Free Knot Splines with RJMCMC in Survival Data Analysis

The B-spline representation is a common tool to improve the fitting of smooth nonlinear functions, it offers a fitting as a piecewise polynomial. The regions that define the pieces are separated by a sequence of knots. The main difficulty in this type of modeling is the choice of the number and the locations of these knots. The Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm provides a solution to simultaneously select these two parameters by considering the knots as free parameters. This algorithm belongs to the MCMC techniques that allow simulations from target distributions on spaces of varying dimension. The aim of the present investigation is to use this algorithm in the framework of the analysis of survival time, for the Cox model in particular. In fact, the relation between the hazard ratio function and the covariates being assumed to be log-linear, this assumption is too restrictive. Thus, we propose to use the RJMCMC algorithm to model the log hazard ratio function by a B-spline representation with an unknown number of knots at unknown locations. This method is illustrated with two real data sets: the Stanford heart transplant data and lung cancer survival data. Another application of the RJMCMC is selecting the significant covariates, and a simulation study is performed.     

Delta Method,Moment Convergence,and Inference

Statistics, as functions of the observations, are usually given by well-behaved functions. This fact is used to obtain limit distributions for statistics whose components are given by asymptotically linear functions. These results are then extended to the moments of distributions, covariance matrices and confidence regions for parameters of interest. These regions may be used to test, through duality, hypothesis on these parameters. A theoretical application is presented.