Spatial variation in risk of disease: a nonparametric binary regression approach

A common problem in environmental epidemiology is the estimation and mapping of spatial variation in disease risk. In this paper we analyse data from the Walsall District Health Authority, UK, concerning the spatial distributions of cancer cases compared with controls sampled from the population register. We formulate the risk estimation problem as a nonparametric binary regression problem and consider two different methods of estimation. The first uses a standard kernel method with a cross-validation criterion for choosing the associated bandwidth parameter. The second uses the framework of the generalized additive model (GAM) which has the advantage that it can allow for additional explanatory variables, but is computationally more demanding. For the Walsall data, we obtain similar results using either the kernel method with controls stratified by age and sex to match the age–sex distribution of the cases or the GAM method with random controls but incorporating age and sex as additional explanatory variables. For cancers of the lung or stomach, the analysis shows highly statistically significant spatial variation in risk. For the less common cancers of the pancreas, the spatial variation in risk is not statistically significant.     

Application of Markov chain Monte Carlo methods to modelling birth prevalence of Down syndrome

Data collected before the routine application of prenatal screening are of unique value in estimating the natural live-birth prevalence of Down syndrome. However, much of these data are from births from over 20 years ago and they are of uncertain quality. In particular, they are subject to varying degrees of underascertainment. Published approaches have used ad hoc corrections to deal with this problem or have been restricted to data sets in which ascertainment is assumed to be complete. In this paper we adopt a Bayesian approach to modelling ascertainment and live-birth prevalence. We consider three prior specifications concerning ascertainment and compare predicted maternal-age-specific prevalence under these three different prior specifications. The computations are carried out by using Markov chain Monte Carlo methods in which model parameters and missing data are sampled.     

上海股票市场股票收益率因素研究

根据上海股票市场从1995 年7 月到2000 年6 月所有A 股股票的月收益率、价格、市值和 公司财务数据,利用Fama-Macbeth 回归分析方法及构造动态组合方法,分析总市值、流通市值、 价格、账面市值比、市盈率、账面资产负债比等因素对股票回报率的影响. 发现上海股票市场具 有显著的市值效应、账面市值比效应、市盈率效应和价格效应. 这些效应不能用股票的beta 值 来解释. 同时发现Fama- French 的三因子模型不能完全解释这些效应,但在三因子模型的基础 上再加上一个市盈率因子可以很好地解     

基于GRPLS 回归的 中国经济增长影响因素分析

构造一种新的方法———岭- 偏最小二乘回归方法(它既有效消除了因素变量之间的多 重共线性,又克服了传统方法的不足,且使模型更加稳健,具有更强的预测和分析能力) ;并运 用广义岭- 偏最小二乘回归方法分析了我国经济增长的影响因素,为我国制订持续、快速增长 的经济政策提供了有益的参考.     

动力减振器最优参数的非线性回归和若干问题

本文采用最优化方法求得考虑生系统阻尼的动力减振器最优参数集，并对此参数集进行非线性回归，得到了便于动力减振器最优设计的回归曲线族。本文还对现有的主系统无阻尼时动力减振器最优参数公式进行了讨论，指出了这些公式的局限性，并根据本文的结果给出了应用范围更广泛的改进公式。     

不同模型在财务预警实证中的比较研究

研究当前国内外公司财务困境预测问题的现状,寻找一套适合于我国企业的财务状况识别指标体系;然后依据该指标体系采用不同方法建立财务状况预测模型;最后,利用样本公司实际指标数据对各个模型的短期及中期预测效果进行了比较分析与实证研究。     

交易额、A 股比例、势效应和三因子模型

利用回归和构造动态投资组合方法,对中国股票市场1995 年7 月到2000 年12 月股票 月收益率、交易额、总市值及其财务数据进行分析,发现中国股票市场具有显著的交易额效应、 A 股比例效应、市值效应、账面市值比效应等. 这些效应有密切的相互关系,但不能用市场beta 值来解释. 市场beta 值、市值因子、账面市值比因子一起,也就是Fama2French 三因子模型,可以 很好地解释这些效应     

资本下乡：进入模式选择及其影响因素

进入模式是经济组织配置资源的重要手段。以工商资本对农户经营活动的控制权程度为依据,将工商资本投资农业的进入模式划分为“松散型”、“半紧密型”、“紧密型”三种类型。利用对广东、湖北、江苏以及云南四省151个投资主体的调查数据,采用有序逻辑回归模型分析生产因素、市场因素、制度因素以及生产技术传递能力对工商资本投资农业的进入模式选择的影响。结果表明：耕地资源优势越弱,工商资本投资农业选择紧密型进入模式的可能性越高;市场潜力越大,越倾向于选择紧密型进入模式;非正式制度越不利,选择紧密型进入模式的可能性越低;生产技术的传递能力越弱,越倾向于选择紧密型进入模式。     

农村老年人日常生活家庭照料与社会照料关系研究——基于多层回归模型的分析

文章利用中国健康与养老追踪调查 2011—2012 年的基线调查数据,运用多层回归模型,通过分析社会照料对农村老年人家庭照料获得的影响,探讨家庭照料与社会照料的关系。 回归估计结果显示,个人层次上的年龄、居住状况、失能情况和社区层次上的老龄补贴、经济状况对农村老年人获得家庭照料有不同程度的影响,而代表社会照料的变量在统计上均不显著。 回归估计结果说明,社会照料并不能对缺位的家庭照料起到补充作用,更不会起到替代作用,两者呈现一种非此即彼的“无关系”。 文章的政策启示是,打破家庭照料与社会照料的壁垒,构建一个“流动”的老年照料体系。     

Testing Inference in Inflated Beta Regressions under Model Misspecification

We consider testing inference in inflated beta regressions subject to model misspecification. In particular, quasi-z tests based on sandwich covariance matrix estimators are described and their finite sample behavior is investigated via Monte Carlo simulations. The numerical evidence shows that quasi-z testing inference can be considerably more accurate than inference made through the usual z tests, especially when there is model misspecification. Interval estimation is also considered. We also present an empirical application that uses real (not simulated) data.