Estimating Nested Count Data Models

Count data models have found a wide variety of applications not only in applied economics and finance but also in diverse fields ranging from biometrics to political science. Poisson and negative binomial (NB) models have been extensively used in count data analysis. Two particular NB model specifications, NBI and NBII, have been especially popular. However, these models impose arbitrary restrictions on the relation between the conditional mean and variance of the dependent variable, limiting their generality. This study proposes tests for selection among the Poisson and NB models by formally demonstrating that the log likelihood function (LLF) of a general NB model parametrically nests the LLF of the Poisson, NBI and NBII as testable special cases. It also proposes estimation of the general NB model since it allows greater flexibility in the relationship between the mean and variance of the dependent variable than NBI and NBII. The empirical application, which uses micro-level data on recreational boating, provides support for the paper's main theme. Tests clearly reject not only the Poisson, but also NBI and NBII, in favour of a different NB model, underscoring the importance of the general model specification.     

The Impact of Transaction Costs and the Expected Length of Stay on Homeownership

Economic theory suggests that an increase in the expected length of stay in a dwelling increases the probability of a household choosing to own a house rather than rent. This hypothesis is derived from recognition that there are substantial transaction costs associated with homeownership and increased expected time in the home reduces the annualized transaction cost. Using a military data set, we confirm that expected length of stay in a dwelling and the transaction costs of selling are very important to the ownership decision. Our best estimate of the transaction costs of selling a home are the sum of 3% of house value and 4% of household earnings.     

Latent Markov Modelling of Recidivism Data

This article discusses the application of latent Markov modelling for the analysis of recidivism data. We briefly examine the relations of Markov modelling with log–linear analysis, pointing out pertinent differences as well. We show how the restrictive Markov model may be more easily applicable by adding latent variables to the model, in which case the latent Markov model is a dynamic version of the latent class model. As an illustration, we apply latent Markov analysis on an empirical data set of juvenile prosecution careers, showing how the Markov analyses producing well-fitting and interpretable solutions. We end by comparing the possible contributions of Markov modelling in recidivism research, outlining its drawbacks as well. Recommendations and directions for future research conclude the article.     

Mixture Models of Missing Data

This paper proposes a general framework for the analysis of survey data with missing observations. The approach presented here treats missing data as an unavoidable feature of any survey of the human population and aims at incorporating the unobserved part of the data into the analysis rather than trying to avoid it or make up for it. To handle coverage error and unit non-response, the true distribution is modeled as a mixture of an observable and of an unobservable component. Generally, for the unobserved component, its relative size (the no-observation rate) and its distribution are not known. It is assumed that the goal of the analysis is to assess the fit of a statistical model, and for this purpose the mixture index of fit is used. The mixture index of fit does not postulate that the statistical model of interest is able to account for the entire population rather, that it may only describe a fraction of it. This leads to another mixture representation of the true distribution, with one component from the statistical model of interest and another unrestricted one. Inference with respect to the fit of the model, with missing data taken into account, is obtained by equating these two mixtures and asking, for different no-observation rates, what is the largest fraction of the population where the statistical model may hold. A statistical model is deemed relevant for the population, if it may account for a large enough fraction of the population, assuming the true (if known) or a sufficiently small or a realistic no-observation rate.     

Scaling for Residual Variance Components of Ordered Category Responses in Generalised Linear Mixed Multilevel Models

A method is proposed that enables changes in variance components to be computed from the results of fitting ordered response generalised models with multilevel and random effects. This deals with the rescaling of the response that occurs as we add new features to a developing model.     

电子政务中数据标准化的方法研究

文章从电子政务的实践出发,阐述了电子政务中数据标准化的步骤和方法。     

数据挖掘技术在医院信息系统中的应用研究

本文在数据挖掘技术概述的基础上,分析了把数据挖掘应用于医院信息系统的必要性,并阐述了其在医院信息系统建设中的具体应用。     

变系数动态面板数据模型的半参数估计

本文把一般的常系数的动态面板数据模型拓广到变系数的情形。对于变系数的动态面板数据模型首先推导出模型所隐含的各种矩条件,然后利用广义矩估计的方法得到了模型中未知参数的半参数广义矩估计,最后对于我们所得到的估计的渐进性和一致性进行证明。     

基于运单及POI数据的快递企业客户发现方法

针对快递企业拓展业务困难的情况,提出了基于快递运单发现快递公司网点周边潜在客户的方法。以北京市为例,采用网络爬虫从互联网抓取快递运单,通过对运单数据进行分析获得快递营业网点信息,利用高德开放平台提供的POI数据查询功能,发现快递网点附近潜在客户信息。研究结果表明业务量最大的几个网点占据了地区总业务量的半数以上,挖掘这些网点的业务情况,对于企业的发展有启发意义。研究成果可为快递企业进行竞争性客户分析提供支撑,也可为快递企业进行市场推广提供辅助支撑。     

大数据时代医院档案管理工作的创新及优化

在医疗体制改革日益深化、医疗卫生事业快速发展的背景下,医院档案种类、载体形式和数量有所增加,医院档案的重要性日益凸显。医院档案在医疗服务和医院各项工作开展中发挥着重要作用,医院管理部门要注重医院档案管理工作,为医院改革和发展提供基础保障。在大数据时代,医院需要树立新型档案管理理念,引进大数据技术,创新并优化现有的医院档案管理模式。论文主要对大数据时代医院档案管理工作的创新及优化进行了分析。     

Models of Personnel Management: A Means of Understanding the Diversity of Personnel Practices?

Kathy Monks, who is a Lecturer in Human Resource Management at Dublin City University Business School, discusses the results of a survey of the roles and responsibilities of personnel managers in Eire. She describes four main models of personnel practice which she found and compares them with those developed by Tyson and Fell in 1986 drawing on research in the UK. She goes on to consider why organisations adopt simple or complex personnel management pratices.     

具有异质性非参数时间趋势的面板数据模型的估计

本文为一类具有异质性非参数时间趋势的面板数据模型提出了一种简单估计方法。基于局部多项式回归的思想,首先去除数据中的时间趋势成分,然后由最小二乘法来估计公共系数,同时得到时间趋势函数的非参数估计。在一些正则条件下,研究了这些估计量的渐近性质,即在时间维度T和横截面维度n同时趋向无穷时,建立了各个估计量的渐近相合性和渐近正态性。最后通过蒙特卡洛模拟,考查了这种估计方法的有限样本性质。     

医院卫生统计数据质量影响因素及应对措施研究

医院卫生统计数据质量对医院的建设发展具有重要的作用。从人为方面、人才队伍建设方面、工作条件方面、作用发挥与重视程度方面以及技术等方面分析了影响医院卫生统计数据质量的因素,在此基础上从加强统计法制工作、改革统计制度及方法、建立健全监控评估体系、提高重视程度、加强人才队伍建设、提高信息化水平等六个方面研究了提高医院卫生统计数据质量的应对措施。     

A Comparative Analysis of Different IV and GMM Estimators of Dynamic Panel Data Models

It is well known that the usual procedures for estimating panel data models are inconsistent in the dynamic setting. A large number of consistent estimators however, have been proposed in the literature. This paper provides a survey of the majority of mainstream estimators, which tend to consist of IV and GMM ones. It also considers a newly proposed extension to the promising Wansbeek–Bekker estimator (Harris & Mátyás, 2000). To provide guidance to the applied researcher working on micro-datasets, the small sample performance of these estimators is evaluated using a set of Monte Carlo experiments.     

组合模型在我国火灾损失数据拟合中的应用

文章使用组合模型对我国28个省市1999~2012年的火灾直接经济损失数据进行分布拟合,利用R语言给出各模型参数的最大似然估计及模型检验。结果显示,组合模型的拟合效果明显优于使用单个模型的拟合效果,而且威布尔—广义帕累托组合模型是最优的。     

试论多种支付模式下医院财务内控管理设计

目的:分析多种支付模式下医院财务内控管理的设计与运用效果。方法:以我院财务内控管理工作推行多种支付模式下医院财务内控管理的时间为分组依据,分成对照组、干预组,两组分别采用常规财务内控管理模式、多种支付模式下医院财务内控管理模式,展开各项财务管理工作。评估两组财务内控管理工作质量、门诊缴费与缴纳住院预交金平均时间、患者对医院支付的满意度差异。结果:干预组财务内控管理工作质量优于对照组(P<0.05)。干预组患者门诊缴费、缴纳住院预交金平均时间均短于对照组(P<0.05)。干预组患者对医院支付的满意度高于对照组(P<0.05)。结论:多种支付模式下医院财务内控管理设计的重点包含支持支付方式、单边账管理等。多种支付模式下医院财务内控管理的应用,可缩短患者缴费等待时间,改善财务内控管理工作质量。     

大数据背景下医院人事档案管理的策略

大数据改变了人们的生活、生产方式,也在很大程度上影响了医院管理模式。现阶段,医院人事档案管理过程中,需要基于大数据的特点,合理规范医院人事工作的流程,根据信息技术的特点,创新医院人事档案管理模式,提升医院人事档案管理水平。论文基于已有的相关研究,对于大数据背景下医院人事档案管理现状进行了探讨,进而提出医院人事档案管理的建议,旨在不断完善医院人事档案管理流程,提升医院人事管理水平。     

单因子利率期限结构模型参数估计的数据选择

本文证明了单因子利率模型参数估计数据选择的相关性原则,并提出另一原则：选择交易最频繁、成交量最大的利率品种。依据这两个原则,R007是瞬时利率rt的最佳近似替代,在使用中国货币市场利率估计单因子利率模型的参数时,应该选择R007作为估计数据。     

发挥热电联产的优势推动城市集中供热发展

热电联产集中供热是节约能源、减少环境污染的重要措施之一.目前我国热电联产集中供热的发展机遇和挑战共存,应发挥热电联产的优势,推动城市集中供热的发展.     

加大力度强化食品安全监管

根据国务院<关于进一步加强食品安全工作的决定>,质检部门肩负着整顿食品生产加工企业,加强食品生产领域质量安全和卫生监管的神圣职责.忻州市质量技术监督系统2005年的工作重点之一,就是全面履行食品生产领域安全监管职责,发挥主力军作用,把食品安全工作放在突出位置,加大力度、强化监管,确保本地区食品安全,保护人民群众的安全健康.