Finite mixture models with concomitant information: assessing diagnostic criteria for diabetes

The World Health Organization (WHO) diagnostic criteria for diabetes mellitus were determined in part by evidence that in some populations the plasma glucose level 2 h after an oral glucose load is a mixture of two distinct distributions. We present a finite mixture model that allows the two component densities to be generalized linear models and the mixture probability to be a logistic regression model. The model allows us to estimate the prevalence of diabetes and sensitivity and specificity of the diagnostic criteria as a function of covariates and to estimate them in the absence of an external standard. Sensitivity is the probability that a test indicates disease conditionally on disease being present. Specificity is the probability that a test indicates no disease conditionally on no disease being present. We obtained maximum likelihood estimates via the EM algorithm and derived the standard errors from the information matrix and by the bootstrap. In the application to data from the diabetes in Egypt project a two-component mixture model fits well and the two components are interpreted as normal and diabetes. The means and variances are similar to results found in other populations. The minimum misclassification cutpoints decrease with age, are lower in urban areas and are higher in rural areas than the 200 mg dl^-1 cutpoint recommended by the WHO. These differences are modest and our results generally support the WHO criterion. Our methods allow the direct inclusion of concomitant data whereas past analyses were based on partitioning the data.     

Circuit optimization via sequential computer experiments: design of an output buffer

In electrical engineering, circuit designs are now often optimized via circuit simulation computer models. Typically, many response variables characterize the circuit's performance. Each response is a function of many input variables, including factors that can be set in the engineering design and noise factors representing manufacturing conditions. We describe a modelling approach which is appropriate for the simulator's deterministic input–output relationships. Non-linearities and interactions are identified without explicit assumptions about the functional form. These models lead to predictors to guide the reduction of the ranges of the designable factors in a sequence of experiments. Ultimately, the predictors are used to optimize the engineering design. We also show how a visualization of the fitted relationships facilitates an understanding of the engineering trade-offs between responses. The example used to demonstrate these methods, the design of a buffer circuit, has multiple targets for the responses, representing different trade-offs between the key performance measures.     

Retirement patterns in Hong Kong: a censored regression analysis

This paper provides an overview of retirement patterns in Hong Kong on the basis of limited data. A censored regression model is used to infer the retirement age from people‘s current retirement status and their current age. This model is equivalent to a restricted probit model, and the interpretation of parameters is straightforward. The results clearly show a negative income effect on the retirement decision. The retirement age seems to be positively related to lifetime earnings but negatively related to the rate of decline of earnings with age. JEL classification: C24, J14, J26 Received May 6, 1996 / Accepted February 5, 1997     

Estimation of critical points in the mixture inverse Gaussian model

The maximum likelihood estimation for the critical points of the failure rate and the mean residual life function are presented in the case of mixture inverse Gaussian model. Several important data sets are analyzed from this point of view. For each of the data sets, Bootstrapping is used to construct confidence intervals of the critical points.     

FDI对我国三次产业影响作用的实证分析

利用自回归分布滞后(ADL)模型,结合我国1983到2006年的有关数据,对我国FDI对三次产业的影响作用作出实证分析,得出结论,FDI在短期内对我国经济增长起了较大的促进作用,但长期作用不显著.并结合我国经济现状分析了原因,提出要充分发挥其在经济增长中的作用,需调整FDI在三次产业之间以及产业内部的投资结构.     

数字金融对农户气候适应性行为的影响

气候适应性技术采用率低下已成为制约农业可持续发展的重要因素,数字金融可能影响农户的气候适应性行为。基于河南、陕西、山西三省1 384份农户微观调查数据,运用内生转换回归模型构建反事实分析框架,实证分析数字金融使用对农户气候适应性行为的影响效应及其作用机制。研究发现:使用数字金融能显著促进农户气候适应性技术采纳行为,具体表现为在反事实假设下,使用数字金融的农户若未使用其气候适应性技术采纳程度将下降;未使用数字金融的农户如果使用了,其气候适应性技术采纳程度将上升。机制分析表明,数字金融能够提高借贷易得性与信息易得性,进而促进农户采纳气候适应性行为,农户对于金融包容性的认知能够正向增强数字金融对农户气候适应性行为的影响效应。异质性分析表明,数字金融使用对于资本型适应性行为影响的边际效应最大,在反事实假设下也表现为数字金融使用对农户资本型适应性行为提升效果最强;数字金融对农业收入占家庭收入比率较高、接受过培训的农户采纳气候适应性技术促进效应更高。     

员工满意指数测评方法及其管理策略研究——以某石油机械制造企业为例

员工满意度是保证用户满意的基础,员工对企业的满意指数越高,越有利于提高员工对产品或服务的质量。阐述影响员工满意的因素和产生的效应,提出员工满意指数结构模型,给出模型中隐变量相对应的观测变量以及描述模型中各相关变量之间关系的数学表达式;区分员工满意度的内在报酬因素和外在报酬因素,采用薪酬组合/员工满意度状态管理方格衡量员工的不同满意度状态,实现员工满意状态的动态管理;制定提高员工满意度的P-D-C-A-R五步循环措施,在管理实践中指导企业提高员工满意度。通过对某石油机械制造企业的实际调查,运用偏最小二乘法进行员工满意分析,得到隐变量相关关系曲线和影响员工满意的主要因素,并对企业进一步持续改进员工满意管理策略提出相应建议。     

基于资源观的企业IT能力与企业绩效研究

在企业广泛应用信息技术提高竞争能力之际,信息化如何给企业带来效益一直是国内外研究的热点问题.在回顾相关文献的基础上,利用基于资源观的概念框架分析企业IT资源,建立企业IT能力模型;采用配对样本比较和统计检验方法,对企业IT能力与企业绩效之间的关系进行实证分析.研究结果表明,具有卓越IT能力的企业会表现出更好的绩效,这些企业具有显著较高的营业净利率、总资产收益率、净资产收益率和人均净利润.企业不仅需要进行IT投资,更重要的是把IT投资内化为整个企业的IT能力,只有这样IT才会更好地给企业带来效益,这有助于指导中国企业信息化工程的建设和论证.     

Practical Maximum Pseudolikelihood for Spatial Point Patterns(with Discussion)

This paper describes a technique for computing approximate maximum pseudolikelihood estimates of the parameters of a spatial point process. The method is an extension of Berman & Turner's (1992) device for maximizing the likelihoods of inhomogeneous spatial Poisson processes. For a very wide class of spatial point process models the likelihood is intractable, while the pseudolikelihood is known explicitly, except for the computation of an integral over the sampling region. Approximation of this integral by a finite sum in a special way yields an approximate pseudolikelihood which is formally equivalent to the (weighted) likelihood of a loglinear model with Poisson responses. This can be maximized using standard statistical software for generalized linear or additive models, provided the conditional intensity of the process takes an 'exponential family' form. Using this approach a wide variety of spatial point process models of Gibbs type can be fitted rapidly, incorporating spatial trends, interaction between points, dependence on spatial covariates, and mark information.     

Seasonal forcing on the dynamics ofClethrionomys rufocanus: Modeling geographic gradients in population dynamics

We interpret gradients in population dynamics of the gray-sided vole from the southwestern part of the island of Hokkaido to its northeastern part within the framework of a phenomenological model involving the relative length of summer and winter. In Hokkaido, as in other northern regions, both spring and fall is considered as short transition periods between the two main seasons — summer (the primary breeding season) and winter (the non-reproductive or secondary breeding season). We show that the geographic transition in dynamics may be understood as the combined consequence of different patterns of density-dependence during summer and winter, and geographically varying season lengths. Differences are shown to exist between summer and winter with respect to strength of density-dependence. Direct density-dependence, in particular, is stronger during winter than during summer. A model is presented to show how relative lengths of seasons can induce both stable and periodically fluctuating population dynamics. The results are compared and contrasted with what is otherwise known about the gradient in rodent dynamics in Fennoscandia.