Sixty years of change in Tibetan fertility: An assessment

Existing knowledge of Tibetan historical population development is mostly based on ‘best-guess’ estimates and is heavily politicized. Using census data, I reconstruct the development of Tibetan fertility in China since the 1940s, with the objective of providing an independent assessment that can be used as benchmark for future studies and debates on Tibetan demography. Following major social and economic transformations starting in the 1950s, Tibetan fertility unexpectedly increased from the late 1950s to the late 1960s. As noted in several existing studies, Tibetan fertility in China then declined swiftly from the early 1980s onwards and has now reached values close to replacement level. Focusing on the 1950–70 period, I examine factors that contributed to shaping the Tibetan fertility increase in more detail. This confirms that changes in nuptiality and disease-related infertility both played a role in pushing up fertility rates among Tibetan women in China.     

利益相关者视角下的企业社会责任研究——以山东省1400家企业问卷调查为例

企业履行社会责任已经成为90年代以来企业界和理论界的共识,利益相关者理论为企业社会责任研究提供了新的视角和框架,按照利益相关者属性的不同可对其进行分类研究.在对山东省1400家企业的调查研究基础上,提出利益相关者的三维金字塔模型,以企业规模、企业性质和企业生命周期为变量考察三个维度社会责任表现的差异性,为企业的社会责任决策提供新的思路,为政府部门制定提升企业社会责任行为表现的政策提供新的视角和依据.     

农地抵押融资意愿与最优土地规模

运用2014年陕西、宁夏两省2493户农户的调研数据,采用Probit模型,以农地规模对农户参与农地抵押融资意愿的影响分析为基础,实证分析农户参与农地抵押融资意愿最大化条件下的最优土地规模。研究发现：农地规模对农户参与农地抵押融资意愿呈倒U型影响,高陵、平罗地区农户参与农地抵押融资的最优土地规模分别为12.67亩、56.50亩,分别是当地户均农地规模的2.68倍、3.00倍,农业主导产业差异导致最优土地规模有所不同。总体看来,样本农户的家庭规模、经营类型、往返金融机构的便利程度、金融机构数量对农户参与农地抵押融资意愿有较显著的影响,而分区域来看,各控制变量的影响效应存在较大区别。     

关于国民收入随机模型的广义最小方差控制

基于Astrom随机动态模型,讨论了非最小相位经济系统的控制问题.应用广义最小方差控制方法设计出自发投资的调控策略.通过实例表明,国民收入在控制策略作用下波动幅度最小,这是促进经济增长良性循环的重要前提.     

历史文化名城武威旅游资源定量评价

在实地考察和分析数据的基础上 ,运用层次分析法和模糊数学法对武威市旅游资源进行了定量评价 ,得到旅游资源各评价因子的权重、分值及综合评分值 ,并对其进行等级划分 ,为武威旅游业的发展提供了基础资料和依据。     

On distribution-weighted partial least squares with diverging number of highly correlated predictors

Summary.  Because highly correlated data arise from many scientific fields, we investigate parameter estimation in a semiparametric regression model with diverging number of predictors that are highly correlated. For this, we first develop a distribution-weighted least squares estimator that can recover directions in the central subspace, then use the distribution-weighted least squares estimator as a seed vector and project it onto a Krylov space by partial least squares to avoid computing the inverse of the covariance of predictors. Thus, distrbution-weighted partial least squares can handle the cases with high dimensional and highly correlated predictors. Furthermore, we also suggest an iterative algorithm for obtaining a better initial value before implementing partial least squares. For theoretical investigation, we obtain strong consistency and asymptotic normality when the dimension p of predictors is of convergence rate O { n ^1/2/ log ( n )} and o ( n ^1/3) respectively where n is the sample size. When there are no other constraints on the covariance of predictors, the rates n ^1/2 and n ^1/3 are optimal. We also propose a Bayesian information criterion type of criterion to estimate the dimension of the Krylov space in the partial least squares procedure. Illustrative examples with a real data set and comprehensive simulations demonstrate that the method is robust to non-ellipticity and works well even in 'small n –large p ' problems.     

Two-step estimation for inhomogeneous spatial point processes

Summary.  The paper is concerned with parameter estimation for inhomogeneous spatial point processes with a regression model for the intensity function and tractable second-order properties ( K -function). Regression parameters are estimated by using a Poisson likelihood score estimating function and in the second step minimum contrast estimation is applied for the residual clustering parameters. Asymptotic normality of parameter estimates is established under certain mixing conditions and we exemplify how the results may be applied in ecological studies of rainforests.     

ESTIMATING A PARAMETER WHEN IT IS KNOWN THAT THE PARAMETER EXCEEDS A GIVEN VALUE

In some statistical problems a degree of explicit, prior information is available about the value taken by the parameter of interest, θ say, although the information is much less than would be needed to place a prior density on the parameter's distribution. Often the prior information takes the form of a simple bound, ‘θ > θ₁ ’ or ‘θ < θ₁ ’, where θ₁ is determined by physical considerations or mathematical theory, such as positivity of a variance. A conventional approach to accommodating the requirement that θ > θ₁ is to replace an estimator, , of θ by the maximum of and θ₁. However, this technique is generally inadequate. For one thing, it does not respect the strictness of the inequality θ > θ₁ , which can be critical in interpreting results. For another, it produces an estimator that does not respond in a natural way to perturbations of the data. In this paper we suggest an alternative approach, in which bootstrap aggregation, or bagging, is used to overcome these difficulties. Bagging gives estimators that, when subjected to the constraint θ > θ₁ , strictly exceed θ₁ except in extreme settings in which the empirical evidence strongly contradicts the constraint. Bagging also reduces estimator variability in the important case for which is close to θ₁, and more generally produces estimators that respect the constraint in a smooth, realistic fashion.     

Shrinkage inverse regression estimation for model-free variable selection

Summary.  The family of inverse regression estimators that was recently proposed by Cook and Ni has proven effective in dimension reduction by transforming the high dimensional predictor vector to its low dimensional projections. We propose a general shrinkage estimation strategy for the entire inverse regression estimation family that is capable of simultaneous dimension reduction and variable selection. We demonstrate that the new estimators achieve consistency in variable selection without requiring any traditional model, meanwhile retaining the root n estimation consistency of the dimension reduction basis. We also show the effectiveness of the new estimators through both simulation and real data analysis.     

Models for the analysis of C-reactive protein in statin trials

Elevation in C-reactive protein (CRP) is an independent risk factor for cardiovascular disease progression and levels are reduced by treatment with statins. However, on-treatment CRP, given baseline CRP and treatment, is not normally distributed and outliers exist even when transformations are applied. Although classical non-parametric tests address some of these issues, they do not enable straightforward inclusion of covariate information. The aims of this study were to produce a model that improved efficiency and accuracy of analysis of CRP data. Estimation of treatment effects and identification of outliers were addressed using controlled trials of rosuvastatin. The robust statistical technique of MM-estimation was used to fit models to data in the presence of outliers and was compared with least-squares estimation. To develop the model, appropriate transformations of the response and baseline variables were selected. The model was used to investigate how on-treatment CRP related to baseline CRP and estimated treatment effects with rosuvastatin. On comparing least-squares and MM-estimation, MM-estimation was superior to least-squares estimation in that parameter estimates were more efficient and outliers were clearly identified. Relative reductions in CRP were higher at higher baseline CRP levels. There was also evidence of a dose-response relationship between CRP reductions from baseline and rosuvastatin. Several large outliers were identified, although there did not appear to be any relationships between the incidence of outliers and treatments. In conclusion, using robust estimation to model CRP data is superior to least-squares estimation and non-parametric tests in terms of efficiency, outlier identification and the ability to include covariate information.