Bayesian estimation and influence diagnostics of generalized partially linear mixed-effects models for longitudinal data

This paper develops a Bayesian approach to obtain the joint estimates of unknown parameters, nonparametric functions and random effects in generalized partially linear mixed models (GPLMMs), and presents three case deletion influence measures to identify influential observations based on the φ-divergence, Cook's posterior mean distance and Cook's posterior mode distance of parameters. Fisher's iterative scoring algorithm is developed to evaluate the posterior modes of parameters in GPLMMs. The first-order approximation to Cook's posterior mode distance is presented. The computationally feasible formulae for the φ-divergence diagnostic and Cook's posterior mean distance are given. Several simulation studies and an example are presented to illustrate our proposed methodologies.     

Diagnostics in elliptical regression models with stochastic restrictions applied to econometrics

We propose an influence diagnostic methodology for linear regression models with stochastic restrictions and errors following elliptically contoured distributions. We study how a perturbation may impact on the mixed estimation procedure of parameters in the model. Normal curvatures and slopes for assessing influence under usual schemes are derived, including perturbations of case-weight, response variable, and explanatory variable. Simulations are conducted to evaluate the performance of the proposed methodology. An example with real-world economy data is presented as an illustration.     

Goodness-of-fit tests of generalized linear mixed models for repeated ordinal responses

Categorical longitudinal data are frequently applied in a variety of fields, and are commonly fitted by generalized linear mixed models (GLMMs) and generalized estimating equations models. The cumulative logit is one of the useful link functions to deal with the problem involving repeated ordinal responses. To check the adequacy of the GLMMs with cumulative logit link function, two goodness-of-fit tests constructed by the unweighted sum of squared model residuals using numerical integration and bootstrap resampling technique are proposed. The empirical type I error rates and powers of the proposed tests are examined by simulation studies. The ordinal longitudinal studies are utilized to illustrate the application of the two proposed tests.     

中国宏观经济波动源的识别与原因分析

为了识别驱动中国宏观经济周期波动性的影响因素,依据中国经济的特殊性,基于1978-2014年42个宏观经济变量的样本数据集构建动态因子模型进行实证分析。研究发现,驱动中国宏观经济波动主要因素有5个潜在宏观因子,其中前四个主要因子分别揭示了驱动中国经济周期波动的主要波动源,它们分别为工业产出因子、外商直接投资(FDI)因子、设备利用率因子和全要素生产率因子。另外,讨论了熨平经济周期性波动的经济政策选择。     

An F-type test for detecting departure from monotonicity in a functional linear model

When studying associations between a functional covariate and scalar response using a functional linear model (FLM), scientific knowledge may indicate possible monotonicity of the unknown parameter curve. In this context, we propose an F-type test of monotonicity, based on a full versus reduced nested model structure, where the reduced model with monotonically constrained parameter curve is nested within an unconstrained FLM. For estimation under the unconstrained FLM, we consider two approaches: penalised least-squares and linear mixed model effects estimation. We use a smooth then monotonise approach to estimate the reduced model, within the null space of monotone parameter curves. A bootstrap procedure is used to simulate the null distribution of the test statistic. We present a simulation study of the power of the proposed test, and illustrate the test using data from a head and neck cancer study.     

Predictive Inference for Big,Spatial, Non‐Gaussian Data: MODIS Cloud Data and its Change‐of‐Support

Remote sensing of the earth with satellites yields datasets that can be massive in size, nonstationary in space, and non‐Gaussian in distribution. To overcome computational challenges, we use the reduced‐rank spatial random effects (SRE) model in a statistical analysis of cloud‐mask data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board NASA's Terra satellite. Parameterisations of cloud processes are the biggest source of uncertainty and sensitivity in different climate models’ future projections of Earth's climate. An accurate quantification of the spatial distribution of clouds, as well as a rigorously estimated pixel‐scale clear‐sky‐probability process, is needed to establish reliable estimates of cloud‐distributional changes and trends caused by climate change. Here we give a hierarchical spatial‐statistical modelling approach for a very large spatial dataset of 2.75 million pixels, corresponding to a granule of MODIS cloud‐mask data, and we use spatial change‐of‐Support relationships to estimate cloud fraction at coarser resolutions. Our model is non‐Gaussian; it postulates a hidden process for the clear‐sky probability that makes use of the SRE model, EM‐estimation, and optimal (empirical Bayes) spatial prediction of the clear‐sky‐probability process. Measures of prediction uncertainty are also given.     

理论观照与实践反思:乡村振兴道路背景下产业融合探究

为从全球性现代化的视野观察中国现代化道路,通过在湖北西部（鄂西南、鄂西北）、渝东南、贵州（六盘水、遵义、黔东北等）等地烟草行业综合体建设进行调查,对城乡融合发展中的产业融合进行综合考察和研究。研究认为,中国的乡村振兴从“战略”提升为“道路”,表明中国式“两条腿走路”的三农现代化道路认知正式成形,乡村振兴道路在本质上讲是“农村就地现代化道路”,以农业为基础的农村就地现代化道路的关键是农村产业的融合发展;走乡村振兴道路,必须坚持政府是“主导”、乡村是“主场”、就地现代化是“主道”、产业融合发展是“主业”、内生发展是“主流”、三农工作队伍是“主体”、城乡等值发展是“主向”,再从土地流转关系、政策法规、主导产业培育、共享经济平台建设等方面,培育和推进乡村振兴中的产业融合工程。     

回流农民农业经营职业化的影响因素

基于在甘肃、湖南和贵州三省的调查数据,运用二元Logistic回归模型,分析了回流农民农业经营决策和新型农民职业化的影响因素。结果表明:回流农民是否从事农业经营和能否成为新型职业农民同时受到个人禀赋、农业信息感知和乡土情结的影响。个人禀赋的影响表现为:教育程度较高和非常健康的回流农民从事农业经营的可能性低;有培训经历和非常健康的回流农民成为新型职业农民的可能性高;农业信息感知的影响表现为:惠农政策感知水平越高,回流农民从事农业经营的可能性越大,成为新型职业农民的可能性也越大;农业风险感知水平越高,回流农民从事农业经营的可能性越低;支农举措感知水平越高,回流农民成为新型职业农民的可能性越大;乡土情结的影响表现为:有土地流入经历的回流农民从事农业经营的可能性更大,爱农情结越深的回流农民越容易成为新型职业农民。建议从改善农业经营金融扶持与农业保险体系、强化农业技术培训、提升农业信息传播效果和激发回流农民乡土情结四个方面入手,吸引更多回流农民从事农业经营,培育更多回流农民成为新型职业农民。     

城投公司债券信用利差影响因素与对策研究

为防范政府债务风险,减少城投债信用违约事件的发生,采用多元线性回归模型,选取2014～2016年新发城投债中7年期公司债为样本,将政府财政及债券等个体因素相关指标作为自变量,以宏观因素指标作为控制变量,分析中国不同行政级别城投债信用利差的影响因素。研究认为,债券个体因素对县、市和省级城投债信用利差均有显著影响,宏观因素的影响随城投公司所属行政级别升高而升高;地方政府财政因素对各行政级别的影响方向相同,由于城投公司资质存在差异,政府财政因素对低行政级别信用利差的影响显著;不同行政级别下,各类因素对信用利差的影响略有不同,城投公司债券发行信用利差与评级情况和发行规模都呈负向关系,系数绝对值随行政级别的升高而降低,企业自身财务因素影响并不显著。地方政府应大力发展经济使城投公司融资成本降低,投资者在投资前利用多种指标估算债券发行信用利差,城投公司也要注意合理设计发债规模,使自身融资成本达到最低,监管机构加强对低行政级别城投公司债券发行的监管和宏观经济方面的政策引导,以此促进解决城投债问题和推动城投债公司合理发行债券。