Proportional Rate Model with Incomplete Covariate and Complete Auxiliary Information

This paper deals with the analysis of proportional rate model for recurrent event data when covariates are subject to missing. The true covariate is measured only on a randomly chosen validation set, whereas auxiliary information is available for all cohort subjects. To further utilize the auxiliary information to improve study efficiency, we propose an estimated estimating equation for the regression parameters. The resulting estimators are shown to be consistent and asymptotically normal. Both graphical and numerical techniques for checking the adequacy of the model are presented. Simulations are conducted to evaluate the finite sample performance of the proposed estimators. Illustration with a real medical study is provided.     

Enterprise systems and performance of future city logistics

Abstract

Future cities are driven by the developments in Information and Communication Technology to support the triple bottom line requirements of sustainability. Logistics will play a critical role in future cities due to the increasingly micro-dynamic nature of socio-economics of these cities and globalised production and consumption patterns. Enterprise systems (ES), the founding applications to automate and integrate different business processes, will be the key enablers for providing the necessary support required by the future city logistics. However, the implementation of ES and realisation of proposed benefits within the estimated time frame is challenging due to the huge resource requirements in terms of manpower, budget and time. This study takes the antecedent (critical success factors) approach for the implementation process to establish a success predictive model for the realisation of ES benefits. The partial least square regression has been used to test the model. Moreover, this study explores the impact of ES benefits on the logistics performance indicators to explore the synergies between them. This is fundamentally important because future city logistics will rely heavily on appropriately designed and implemented (enterprise) information systems.     

Auxiliary covariate in additive hazards regression for survival data

We consider the additive hazards regression analysis by utilising auxiliary covariate information to improve the efficiency of the statistical inference when the primary covariate is ascertained only for a randomly selected subsample. We construct a martingale-based estimating equation for the regression parameter and establish the asymptotic consistency and normality of the resultant estimator. Simulation study shows that our proposed method can improve the efficiency compared with the estimator which discards the auxiliary covariate information. A real example is also analysed as an illustration.     

On multivariate confidence regions and simultaneous confidence limits for ratios

Convenient general linear model computational procedures are presented for constructing multivariate confidence regions and simultaneous confidence limits for ratios of linear combinations of the parameters. The practical consequence is that a single general linear model computer program, capable of validating the underlying model and estimating the parameters, can (after slight modification) also construct the confidence regions, and even determine their precise analytic form (ellipsoid, hyperboloid, etc.). The text is deliberately factual while the appendices extend and help clarify earlier work by Henry Scheffe. As an example, a confidence ellipse and simultaneous confidence limits are constructed for several relative potencies in a classical multiple parallel line bioassay.     

A novel approach to estimate the Cox model with temporal covariates and application to medical cost data

Abstract

We propose a novel approach to estimate the Cox model with temporal covariates. Our new approach treats the temporal covariates as arising from a longitudinal process which is modeled jointly with the event time. Different from the literature, the longitudinal process in our model is specified as a bounded variational process and determined by a family of Initial Value Problems associated with an Ordinary Differential Equation. Our specification has the advantage that only the observation of the temporal covariates at the event-time and the event-time itself are needed to fit the model, while it is fine but not necessary to have more longitudinal observations. This fact makes our approach very useful for many medical outcome datasets, such as the SPARCS and NIS, where it is important to find the hazard rate of being discharged given the accumulative cost but only the total cost at the discharge time is available due to the protection of private information. Our estimation procedure is based on maximizing the full information likelihood function. The resulting estimators are shown to be consistent and asymptotically normally distributed. Simulations and a real example illustrate the utility of the proposed model. Finally, a couple of extensions are discussed.     

Optimal investment and premium control for insurers with ambiguity

Abstract

In this paper, we consider the optimal investment and premium control problem for insurers who worry about model ambiguity. Different from previous works, we assume that the insurer’s surplus process is described by a non-homogeneous compound Poisson model and the insurer has ambiguity on both the financial market and the insurance market. Our purpose is to find the impacts of model ambiguity on optimal policies. With the objective of maximizing the expected utility of terminal wealth, the closed-form solutions of the optimal investment and premium policies are obtained by solving HJB equations. Finally, numerical examples are also given to illustrate the results.     

Identification in Nonparametric Simultaneous Equations Models

This paper provides conditions for identification of functionals in nonparametric simultaneous equations models with nonadditive unobservable random terms. The conditions are derived from a characterization of observational equivalence between models. We show that, in the models considered, observational equivalence can be characterized by a restriction on the rank of a matrix. The use of the new results is exemplified by deriving previously known results about identification in parametric and nonparametric models as well as new results. A stylized method for analyzing identification, which is useful in some situations, is also presented.     

双寡头市场中品牌广告竞争和大类广告合作策略研究

利用微分对策理论研究了一强一弱双寡头垄断市场中的品牌广告竞争和大类广告合作策略,提出了一个Stackelberg博弈模型,采用汉密尔顿-雅可比-贝尔曼方程求得了模型的均衡品牌广告、大类广告和价值函数,给出了市场份额计算公式。研究发现:与非合作策略相比,大类广告合作策略下,无论强者还是弱者,他们的销售量都增加了,同时两个企业的品牌广告投入也增加了;通过大类广告合作策略,无论强者(领导者)还是弱者(追随者),他们的利润都得到了提高;强者、弱者和行业的大类广告投入分别高于非合作下强者、弱者和行业的大类广告投入。     

Diophantine方程（a~n-1）（（a＋1）~n-1）=x~2有解的条件

设a是大于1的正整数;a≡λ（mod 2）,其中λ∈{0,1};又设f（a）=ord2（a-λ）表示素数2在正整数a-λ的标准分解式中的次数.该文运用初等数论方法证明了：如果方程（an-1）（（a＋1）n-1）=x2有正整数解（n,x）,则必有（i）f（a）=2r,其中r是大于1的正整数;（ii）a＋1的奇素因数p都适合p≡±1（mod 8）.     

我国宏观经济政策效应的模拟分析——基于季度宏观经济联立方程模型

将宏观经济中各主要因素归入商品市场、金融市场、劳动市场、价格和国外影响五个子模块,通过各模块及要素间的相互联系勾勒出我国经济运行的理论架构,并利用1999年一季度至2009年一季度的季度数据,采用动态建模方法建立了刻画我国宏观经济运行特征的联立方程模型;在此基础上运用情景模拟对各宏观经济政策方案的效应进行分析,结果表明,积极的财政政策和适度宽松的货币政策可有效地缓解金融危机冲击、保持经济的平稳增长,且财政政策的效果明显优于货币政策;进而指出我国下一步宏观经济政策的选择和提高货币政策有效性的必要性和切入点。