减排约束下中国天然气供需安全评价:2006-2014年

为准确把握中国天然气供需安全状态,以2006-2014年的数据为样本,在模糊隶属度函数量化指标值的基础上,运用层次分析法(AHP)和熵值法确定指标权重,基于线性加权和函数计算均方根进行综合评价。结果表明:(1)中国天然气供需安全指数呈现"N形"波动,2006-2007年天然气供需安全指数上升。2007-2013年天然气供需安全指数年均下降1.7979。2013年以来天然气供需安全指数开始回升,天然气供需安全压力有所缓解。(2)天然气供应安全指数从2006年的17.5914下降到2013年的0.1556,峰谷差17.4359。天然气需求安全指数呈现"W形"波动。天然气市场安全指数相对稳定,2013年以来略有上升,天然气市场风险逐年下降。(3)天然气供需安全很大程度上取决于天然气供应是否安全。因此,建议从确保天然气稳定供应、提高天然气使用效率和降低市场风险方面采取措施,维护天然气供需安全。     

Inference in Semi‐Parametric Dynamic Models for Repeated Count Data

This paper deals with a longitudinal semi‐parametric regression model in a generalised linear model setup for repeated count data collected from a large number of independent individuals. To accommodate the longitudinal correlations, we consider a dynamic model for repeated counts which has decaying auto‐correlations as the time lag increases between the repeated responses. The semi‐parametric regression function involved in the model contains a specified regression function in some suitable time‐dependent covariates and a non‐parametric function in some other time‐dependent covariates. As far as the inference is concerned, because the non‐parametric function is of secondary interest, we estimate this function consistently using the independence assumption‐based well‐known quasi‐likelihood approach. Next, the proposed longitudinal correlation structure and the estimate of the non‐parametric function are used to develop a semi‐parametric generalised quasi‐likelihood approach for consistent and efficient estimation of the regression effects in the parametric regression function. The finite sample performance of the proposed estimation approach is examined through an intensive simulation study based on both large and small samples. Both balanced and unbalanced cluster sizes are incorporated in the simulation study. The asymptotic performances of the estimators are given. The estimation methodology is illustrated by reanalysing the well‐known health care utilisation data consisting of counts of yearly visits to a physician by 180 individuals for four years and several important primary and secondary covariates.     

Empirical likelihood based detection procedure for change point in mean residual life functions under random censorship

The mean residual life (MRL) function is one of the basic parameters of interest in survival analysis that describes the expected remaining time of an individual after a certain age. The study of changes in the MRL function is practical and interesting because it may help us to identify some factors such as age and gender that may influence the remaining lifetimes of patients after receiving a certain surgery. In this paper, we propose a detection procedure based on the empirical likelihood for the changes in MRL functions with right censored data. Two real examples are also given: Veterans' administration lung cancer study and Stanford heart transplant to illustrate the detecting procedure. Copyright © 2016 John Wiley & Sons, Ltd.     

基于改进突变级数法的农产品物流产业评估模型研究

对农产品物流产业发展水平进行客观、准确的分析与评估,可为政府部门优化与调整产业发展、合理制定产业规划提供科学依据。运用突变级数法与离差最大化法构建农产品物流产业评估模型,并选取广东和山东两省20102014年相关数据对构建的评估模型进行实例分析。结果表明：广东省农产品物流产业发展的优势主要体现在信息化水平与经济基础两方面,基础设施建设则是当前制约其产业发展的关键因素;而庞大的产业规模是现阶段支撑山东省农产品物流产业发展的重要力量。改进的突变级数评估模型既克服了主观赋权的局限,又不失科学性和合理性,计算简单,具有重要实用价值。     

儒家的尚"中"与道家的崇"大"——儒道和谐观的差异与融合

"和谐"作为中国传统文化的一个核心范畴,是一个多层次、立体的概念,儒家的尚"中"思想及由此而发展起来的"中和"观念与道家的崇"大"思想及由此而来的"大和"观念是构成这一理论的两大基石,前者为"和谐"观念的形成提供了秩序平衡与个体节中,后者则带来了本体追求与辩证观念.在《易传》与《中庸》中二者达到融合.     

论"中"

“中”的思想在中国历史渊源流长,成为中华民族人文精神的核心,也是中国哲学的精髓。《周易》倡导“中和”精神,儒家践行中庸之道,佛家崇尚以中观法,道家提倡守中致道,都从不同角度深刻地阐发了“中”的思想。万物皆有中,中是一切存在的根本属性,“中律”是宇宙万物运行的根本法则,中和是创新的灵魂。     

Outlier detection and robust variable selection via the penalized weighted LAD-LASSO method

This paper studies the outlier detection and robust variable selection problem in the linear regression model. The penalized weighted least absolute deviation (PWLAD) regression estimation method and the adaptive least absolute shrinkage and selection operator (LASSO) are combined to simultaneously achieve outlier detection, and robust variable selection. An iterative algorithm is proposed to solve the proposed optimization problem. Monte Carlo studies are evaluated the finite-sample performance of the proposed methods. The results indicate that the finite sample performance of the proposed methods performs better than that of the existing methods when there are leverage points or outliers in the response variable or explanatory variables. Finally, we apply the proposed methodology to analyze two real datasets.     

Smoothed bootstrap bandwidth selection for nonparametric hazard rate estimation

A smoothed bootstrap method is presented for the purpose of bandwidth selection in nonparametric hazard rate estimation for iid data. In this context, two new bootstrap bandwidth selectors are established based on the exact expression of the bootstrap version of the mean integrated squared error of some approximations of the kernel hazard rate estimator. This is very useful since Monte Carlo approximation is no longer needed for the implementation of the two bootstrap selectors. A simulation study is carried out in order to show the empirical performance of the new bootstrap bandwidths and to compare them with other existing selectors. The methods are illustrated by applying them to a diabetes data set.     

The effect of small sample on optimal designs for logistic regression models

Asymptotic methods are commonly used in statistical inference for unknown parameters in binary data models. These methods are based on large sample theory, a condition which may be in conflict with small sample size and hence leads to poor results in the optimal designs theory. In this paper, we apply the second order expansions of the maximum likelihood estimator and derive a matrix formula for the mean square error (MSE) to obtain more precise optimal designs based on the MSE. Numerical results indicate the new optimal designs are more efficient than the optimal designs based on the information matrix.     

MSE performance of the weighted average estimators consisting of shrinkage estimators when each individual regression coefficient is estimated

In this paper, we analytically derive the exact formula for the mean squared error (MSE) of two weighted average (WA) estimators for each individual regression coefficient. Further, we execute numerical evaluations to investigate small sample properties of the WA estimators, and compare the MSE performance of the WA estimators with the other shrinkage estimators and the usual OLS estimator. Our numerical results show that (1) the WA estimators have smaller MSE than the other shrinkage estimators and the OLS estimator over a wide region of parameter space; (2) the range where the relative MSE of the WA estimator is smaller than that of the OLS estimator gets narrower as the number of explanatory variables k increases.