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.     

基于Simpson公式改进的FAGM(1,1)模型及其应用研究

本文分析了传统FAGM（1,1）模型建模过程中存在的误差,提出了一种基于Simpson公式改进的FAGM（1,1）模型。首先,基于分数阶累加生成算子和分数阶累减生成算子建立分数阶FAGM（1,1）模型。其次,利用Simpson积分公式对FAGM（1,1）模型的背景值进行改进,建立SFAGM（1,1）模型。进一步,应用遗传算法确定SFAGM（1,1）模型的最优阶数以提高模型的预测精度。最后,以中国人均GDP为例,对比分析GM（1,1）模型、Simpson改进的GM（1,1）模型（SGM（1,1））、FAGM（1,1）模型、SFAGM（1,1）模型的模拟结果,并对"十三五"时期的人均GDP进行预测,其结果表明SFAGM（1,1）模型比GM（1,1）、SGM（1,1）、FAGM（1,1）在人均GDP的预测方面有更高的精度,"十三五"期间人均GDP年平均增长率为10.64%,到2020年达到83146.97元,是2010年人均GDP的2.69倍,以2010年的人均GDP为基准,到2020年将能够实现翻一番的目标。     

在线评论的行为影响与价值应用研究综述

随着社交网络的发展,大量用户生成的在线评论影响着消费者的行为,同时被应用于企业的经营管理活动,引起市场营销、信息系统、产品开发等领域学者们的广泛关注,取得很大的研究进展。但是在线评论的研究范围广泛,目前并没有系统性的概念框架,缺乏对在线评论的全局认识。因此,本文尝试从行为影响和价值应用两个层面对在线评论研究文献进行全面总结,并结合当前的技术发展和市场环境,探讨在线评论研究的未来趋势。     

零售企业数字化转型对经营效率的影响研究——基于上市企业年报的文本挖掘分析

在构建以国内大循环为主体、国内国际双循环相互促进的新发展格局下,零售企业数字化转型具有重要的现实意义。基于2010—2019年中国实体上市零售企业的年报信息,采用文本挖掘法测算了零售企业的数字化转型水平,并实证检验了零售企业数字化转型对经营效率的影响机制与效应。实证结果表明,数字化转型显著促进了零售企业经营效率的提升。从影响机制来看,数字化转型通过扩大市场规模和改善管理效率对经营效率产生正向影响;从数字化转型的分维度来看,数字化技术的应用对经营效率的提升有显著促进作用,而数字化商业模式和数字化渠道的作用不显著。从异质性分析来看,数字化转型对零售企业经营效率的提升效应主要体现在国有企业当中;规模过大会抑制零售企业数字化转型对经营效率的提升作用;东中部地区零售企业数字化转型对提升经营效率的影响显著,但是这一影响在西部地区并不显著。因此,加快推进数字化转型升级,加大数字化技术应用力度,发挥国有企业示范作用,以及克服“大企业病”,均是促进零售企业经营效率提升的关键。     

A Population Approach to Analysis of Variance Models

Several authors have contributed to what can now be considered a rather complete theory for analysis of variance in cases with orthogonal factors. By using this theory on an assumed basic reference population, the orthogonality concept gives a natural definition of independence between factors in the population. By looking upon the treated units in designed experiments as a formal sample from a future population about which we want to make inference, a natural parametrization of expectations and variances connected to such experiments arises. This approach seems to throw light upon several controversial questions in the theory of mixed models. Also, it gives a framework for discussing the choice of conditioning in models     

Generalized Leverage and its Applications

The generalized leverage of an estimator is defined in regression models as a measure of the importance of individual observations. We derive a simple but powerful result, developing an explicit expression for leverage in a general M -estimation problem, of which the maximum likelihood problems are special cases. A variety of applications are considered, most notably to the exponential family non-linear models. The relationship between leverage and local influence is also discussed. Numerical examples are given to illustrate our results     

A Multivariate Model for Repeated Failure Time Measurements

A parametric multivariate failure time distribution is derived from a frailty-type model with a particular frailty distribution. It covers as special cases certain distributions which have been used for multivariate survival data in recent years. Some properties of the distribution are derived: its marginal and conditional distributions lie within the parametric family, and association between the component variates can be positive or, to a limited extent, negative. The simple closed form of the survivor function is useful for right-censored data, as occur commonly in survival analysis, and for calculating uniform residuals. Also featured is the distribution of ratios of paired failure times. The model is applied to data from the literature     

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.     

Managing Uncertainty through Profit Sharing Contracts from Medieval Italy to Silicon Valley

Organizational innovation is essential to economic development. But, the way successful societies have organized new ventures has been remarkably similar in both past and present. The commenda organizations of medieval Italy shared many characteristics with modern startups that are financed by venture capital. Profit share contracts; limited liability and periodic reevaluations are cases in point. Agency contracts in both types of ventures are designed to absorb the high uncertainty inherent to these enterprises through risk sharing. Uncertainty prohibits a unique ex ante ranking order of investment projects and prompts investors to look for hidden human capital. Equity finance is better equipped to even out unexpected losses and gains that are inherent to uncertainty than debt finance.