Semiparametric linear transformation models for current status data

Current status data arise when the death of every subject in a study cannot be determined precisely, but is known only to have occurred before or after a random monitoring time. The authors discuss the analysis of such data under semiparametric linear transformation models for which they propose a general inference procedure based on estimating functions. They determine the properties of the estimates they propose for the regression parameters of the model and illustrate their technique using tumorigenicity data.     

Joint impact of multiple observations on a subset of variables in multiple linear regression

In multiple linear regression analysis, each observation affects the fitted regression equation differently and has varying influences on the regression coefficients of the different variables. Chatterjee & Hadi (1988) have proposed some measures such as DSSE_ij (Impact on Residual Sum of Squares of simultaneously omitting the ith observation and the jth variable), F_j (Partial F-test for the jth variable) and F_j(i) (Partial F-test for the jth variable omitting the ith observation) to show the joint impact and the interrelationship that exists among a variable and an observation. In this paper we have proposed more extended form of those measures DSSE_IJ, F_J and F_J(I) to deal with the interrelationships that exist among the multiple observations and a subset of variables by monitoring the effects of the simultaneous omission of multiple variables and multiple observations.     

Implementing partial least squares

Partial least squares (PLS) regression has been proposed as an alternative regression technique to more traditional approaches such as principal components regression and ridge regression. A number of algorithms have appeared in the literature which have been shown to be equivalent. Someone wishing to implement PLS regression in a programming language or within a statistical package must choose which algorithm to use. We investigate the implementation of univariate PLS algorithms within FORTRAN and the Matlab (1993) and Splus (1992) environments, comparing theoretical measures of execution speed based on flop counts with their observed execution times. We also comment on the ease with which the algorithms may be implemented in the different environments. Finally, we investigate the merits of using the orthogonal invariance of PLS regression to improve the algorithms.     

小麦新品种“绵阳26”主要性状与产量的关系

对45个试验点的资料分析,多抗、丰产、优质、广适小麦新品种绵阳26的主要性状有效穗x1、穗粒数x2和千粒x3、单穗重x4与产量y的回归方程为：y＝－614．8425＋9.8683x1＋7.3223x2＋10．5315x3＋22．5748x4。经偏回归和偏相关分析,产量构成因素中,千粒重、有效穗对绵阳26产量的贡献和影响最大（b'3＝1．7435,b'1＝1．5372,R3y.＝0．7983,R1y＝0．6991）。在现有产量的基础上稳定千粒重,主攻穗数和穗粒数,是发挥绵阳26高产潜力的决定因素。     

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.     

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。     

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。     

数字素养与农户收入差距:加剧还是缓解?——基于中西部地区的微观证据

数字经济时代,数字贫困问题对农户收入差距的影响引发新的关注。基于2021年中国农村经济与农村金融数据,采用再中心化响应函数(RIF)回归方法探究数字素养对农户收入差距的影响。研究发现:(1)数字素养显著扩大了农户收入差距,并具备明显的马太效应,高数字素养能够攫取更多数字红利,而低数字素养更容易陷入“数字贫困”状态,无法发挥出数字经济的增收效应。(2)应用数字素养会显著加剧农户收入差距,而通用数字素养对农户收入差距的影响并不显著。这种情况也得以印证,现阶段数字“接入鸿沟”已经得以解决,数字“应用鸿沟”差距逐渐凸显。(3)数字素养能够缩小农户财产性收入差距,但会显著扩大工资性收入差距与非农经营性收入差距。脱贫地区受到数字素养的马太效应更强,农户收入差距明显加剧。研究结论证实了我国农村内部存在明显的数字鸿沟,同时数字贫困问题加剧了农户收入差距。新发展阶段我国数字乡村战略的推进不仅要注重农村低收入群体以及弱势群体数字素养的培育,更要加快构建农村数字经济的包容性发展路径,助力农民农村共同富裕的实现。