Approximate composite marginal likelihood inference in spatial generalized linear mixed models

Non-Gaussian spatial responses are usually modeled using spatial generalized linear mixed model with spatial random effects. The likelihood function of this model cannot usually be given in a closed form, thus the maximum likelihood approach is very challenging. There are numerical ways to maximize the likelihood function, such as Monte Carlo Expectation Maximization and Quadrature Pairwise Expectation Maximization algorithms. They can be applied but may in such cases be computationally very slow or even prohibitive. Gauss–Hermite quadrature approximation only suitable for low-dimensional latent variables and its accuracy depends on the number of quadrature points. Here, we propose a new approximate pairwise maximum likelihood method to the inference of the spatial generalized linear mixed model. This approximate method is fast and deterministic, using no sampling-based strategies. The performance of the proposed method is illustrated through two simulation examples and practical aspects are investigated through a case study on a rainfall data set.     

Hard thresholding regression

In this paper, we propose the hard thresholding regression (HTR) for estimating high‐dimensional sparse linear regression models. HTR uses a two‐stage convex algorithm to approximate the ?₀‐penalized regression: The first stage calculates a coarse initial estimator, and the second stage identifies the oracle estimator by borrowing information from the first one. Theoretically, the HTR estimator achieves the strong oracle property over a wide range of regularization parameters. Numerical examples and a real data example lend further support to our proposed methodology.     

Generalized estimator of population mean by using conventional and non-conventional measures in the presence of measurement errors

In survey research, it is assumed that reported response by the individual is correct. However, given the issues of prestige bias, self-respect, respondent's reported data often produces estimated values which are highly deviated from the true values. This causes measurement error (ME) to be present in the sample estimates. In this article, the estimation of population mean in the presence of measurement error using information on a single auxiliary variable is studied. A generalized estimator of population mean is proposed. The class of estimators is obtained by using some conventional and non-conventional measures. Simulation and numerical study is also conducted to assess the performance of estimators in the presence and absence of measurement error.     

Instrumental variable based variable selection for generalized linear models with endogenous covariates

We study the variable selection problem for a class of generalized linear models with endogenous covariates. Based on the instrumental variable adjustment technology and the smooth-threshold estimating equation (SEE) method, we propose an instrumental variable based variable selection procedure. The proposed variable selection method can attenuate the effect of endogeneity in covariates, and is easy for application in practice. Some theoretical results are also derived such as the consistency of the proposed variable selection procedure and the convergence rate of the resulting estimator. Further, some simulation studies and a real data analysis are conducted to evaluate the performance of the proposed method, and simulation results show that the proposed method is workable.     

Fast and approximate exhaustive variable selection for generalised linear models with APES

We present APproximated Exhaustive Search (APES), which enables fast and approximated exhaustive variable selection in Generalised Linear Models (GLMs). While exhaustive variable selection remains as the gold standard in many model selection contexts, traditional exhaustive variable selection suffers from computational feasibility issues. More precisely, there is often a high cost associated with computing maximum likelihood estimates (MLE) for all subsets of GLMs. Efficient algorithms for exhaustive searches exist for linear models, most notably the leaps‐and‐bound algorithm and, more recently, the mixed integer optimisation (MIO) algorithm. The APES method learns from observational weights in a generalised linear regression super‐model and reformulates the GLM problem as a linear regression problem. In this way, APES can approximate a true exhaustive search in the original GLM space. Where exhaustive variable selection is not computationally feasible, we propose a best‐subset search, which also closely approximates a true exhaustive search. APES is made available in both as a standalone R package as well as part of the already existing mplot package.     

西部乡村—城镇转型多元评价指标体系的构建及应用

以乡村综合发展度、乡村—城镇变迁度、乡村—城镇协调度为测度标准,构建一套综合评价指标体系。以中国西部地区为研究对象,对2001—2010年十年间乡村—城镇转型发展态势进行分析和评价。研究结果表明:(1)从整体层面来看,西部地区乡村综合发展度不断提高,乡村—城镇变迁度不断加快,乡村—城镇协调度不断优化;(2)从省际层面来看,不同省区的乡村—城镇转型呈现出不同的发展面貌,具有较大的差距。     

欧元：世界经济的新变量

欧元是世界经济的一种新的影响因素,它将对中国经济和世界经济产生深远的影响。我国不仅要利用欧元运行之机与欧盟各国谈判,促使其降低对我国企业出口配额限制和反倾销税,改变其对我国的贸易歧视政策,而且我国的出口企业应建立欧元统一大市场观念,尽量利用各种金融工具进行保值、规避风险,尤其是实力强大的出口企业应考虑直接去单一化区内投资,以投资带动贸易,分享欧盟货币统一化的好处,绕过欧盟对我国的贸易壁垒,扩大我国在欧元区的市场份额。     

农机服务发展与粮食生产效率研究:2004—2016——基于变系数随机前沿分析

采用“两步法”研究了农机服务发展与中国粮食生产效率的关系.基于2004-2016年全国31省份粮食生产投入产出的面板数据,利用变系数随机前沿分析方法测算了中国粮食生产的技术变化、技术效率以及全要素生产率,然后构建农机服务发展影响粮食生产效率的OLS回归模型.结果表明:(1)31省份粮食生产的投入要素产出弹性和技术效率存在明显差异,2008年粮食技术变化最为明显,粮食全要素生产率的增长在2008年高达5%.(2)农机服务在2008年以后对粮食全要素生产率的增长具有显著的促进作用,农机服务对粮食TFP的作用存在一定的滞后效应.(3)农机服务发展与劳动产出弹性具有替代关系,与化肥和机械产出弹性具有互补关系.在三大粮食作物中,小麦机械化程度最高,水稻和玉米的机械化程度还有待提升.     

民生工程建设综合评价研究

民生问题是构建和谐社会的关键,也是政府工作的基本着力点。在前期研究构建的指标体系的基础上,运用拉开档次评价法和逐层序关系分析法对我国31个省市自2006－2012年的民生工程建设状况进行了动态综合评价,并用时序几何平均算子（TOWGA）进行了二次加权得到综合评价的总排名,最后对各地区的排序变化进行归纳、分析。     

中西部农村居民健康状况Logistic模型分析

运用有序Logistic模型分析了我国中西部农村居民的健康状况及其决定因素。研究发现,近年来我国中西部农村居民健康状况总体有所好转。中部地区的状况要好于西部地区。中老年人的健康水平下降,妇女的健康水平较差。中学教育对改善健康水平有积极影响。收入的增加和家庭环境卫生的改善有助于提高健康水平。建议政府继续加大对中西部地区农村合作医疗制度改革的支持力度,重视对农村基础教育和环境卫生设施的投入,采取措施维护中老年人和妇女群体的健康水平,改善落后地区的卫生设施和医疗保健条件,促进健康公平。