农户土地流转有助于农户群体“提低扩中”吗?

提高中低收入农户群体的收入水平是缩小居民收入差距、实现共同富裕的关键任务。利用CFPS 2018数据,使用基于RIF的ITE模型等计量方法进行分析研究发现：(1)土地流转的增收效应具有非对称性,转出土地的中低收入农户能显著增收,但转入土地的中低收入农户增收不明显;(2)东部和西部地区转出土地的中低收入农户收入增长显著;(3)土地流转(尤其是转出)有助于缩小农户间收入差距、优化农户群体的收入分配结构。实证结果稳健地说明农户土地流转对实现农户群体“提低扩中”目标具有积极意义,在此基础上提出在西部和东中部欠发达农村地区,鼓励和支持中低收入农户流转土地,以增加其兼业和非农收入等政策建议。     

长江流域城市人口分布及空间相关性研究

本文选取长江流域104个城市人口样本作为研究对象,采用人口分布的结构指数和空间自相关分析方法,以及借用GIS技术来研究长江流域城市人口分布的空间动态特征和城市人口之间的功能联系。结果表明,长江流域城市人口空间结构呈均衡发展态势;人口密度分布呈现趋同趋势;城市人口与邻近城市人口之间的空间正相关要多于空间负相关;长江流域城市人口之间的空间关联性被分为八种类型。     

不同信息透明度对分销系统及各成员的影响

考虑由一个供应商和多个零售商组成的分销系统。研究高、中、低三种不同信息透明度模式下系统的最优补货及分配策略以及相应的系统和各个零售商的期望成本。证明无论从系统的角度还是从零售商的角度,并非信息透明度越高,期望成本越低。从整个系统的角度来讲,虽然系统的期望成本总在高信息透明度模式下取得最低,但是,中低两种信息透明度模式谁取得第二低的系统期望成本取决于系统内各节点之间的距离以及零售商所面对客户需求的性质。从零售商的角度,高信息透明度并不能降低零售商的期望成本。零售商是否可以从较高的信息透明度水平中获益则取决于零售商在送货路线上所处的位置,系统内各节点之间的距离,以及零售商所面对客户需求的性质。     

对数正态分布参数的最大似然估计

利用最大似然估计法求出了对数正态分布两个参数的估计量,并讨论了它们的无偏性和相合性。     

环境规制的范式及其政治经济学分析

自20世纪60年代公共选择学派提出政治行为人的“经济人”假定以来,经济学家们对于传统规制理论的市场纠正说不断提出质疑。一个新的规制理论,即利益集团理论逐渐兴起。该理论把政府看作为一个不完美的代理机构,并对不同利益集团的行为作出反应。在这种分析框架下,环境规制就是政府在污染者、环保主义者以及未组织起来的一般社会公众等各种利益群体的行为约束下对环境所有权的一个再分配过程。     

Estimation for Birnbaum–Saunders Distribution in Simple Step Stress–accelerated Life Test with Type-II Censoring

This paper develops the Bayesian estimation for the Birnbaum–Saunders distribution based on Type-II censoring in the simple step stress–accelerated life test with power law accelerated form. Maximum likelihood estimates are obtained and Gibbs sampling procedure is used to get the Bayesian estimates for shape parameter of Birnbaum–Saunders distribution and parameters of power law–accelerated model. Asymptotic normality method and Markov Chain Monte Carlo method are employed to construct the corresponding confidence interval and highest posterior density interval at different confidence level, respectively. At last, the results are compared by using Monte Carlo simulations, and a numerical example is analyzed for illustration.     

The empirical Bayes estimators of the parameter of the Poisson distribution with a conjugate gamma prior under Stein's loss function

For the hierarchical Poisson and gamma model, we calculate the Bayes posterior estimator of the parameter of the Poisson distribution under Stein's loss function which penalizes gross overestimation and gross underestimation equally and the corresponding Posterior Expected Stein's Loss (PESL). We also obtain the Bayes posterior estimator of the parameter under the squared error loss and the corresponding PESL. Moreover, we obtain the empirical Bayes estimators of the parameter of the Poisson distribution with a conjugate gamma prior by two methods. In numerical simulations, we have illustrated: The two inequalities of the Bayes posterior estimators and the PESLs; the moment estimators and the Maximum Likelihood Estimators (MLEs) are consistent estimators of the hyperparameters; the goodness-of-fit of the model to the simulated data. The numerical results indicate that the MLEs are better than the moment estimators when estimating the hyperparameters. Finally, we exploit the attendance data on 314 high school juniors from two urban high schools to illustrate our theoretical studies.     

A new stratified three-stage unrelated randomized response model for estimating a rare sensitive attribute based on the Poisson distribution

This article suggests an efficient method of estimating a rare sensitive attribute which is assumed following Poisson distribution by using three-stage unrelated randomized response model instead of the Land et al. model (2011 Land, M., S. Singh, and S. A. Sedory. 2011. Estimation of a rare sensitive attribute using poisson distribution. Statistics 46 (3):351–60. doi:10.1080/02331888.2010.524300.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) when the population consists of some different sized clusters and clusters selected by probability proportional to size(:pps) sampling. A rare sensitive parameter is estimated by using pps sampling and equal probability two-stage sampling when the parameter of a rare unrelated attribute is assumed to be known and unknown.

We extend this method to the case of stratified population by applying stratified pps sampling and stratified equal probability two-stage sampling. An empirical study is carried out to show the efficiency of the two proposed methods when the parameter of a rare unrelated attribute is assumed to be known and unknown.