基于Markov过程的市场预测

本文根据产品在市场流通领域中所处地位，把它分为四个状态，利用Ｍａｒｋｏｖ过程建立其状态分布的数学模型，给企业提供市场信息，以便组织货源与作出科学决策，从而减少参与市场竞争的盲目性．     

城市文化建设中商品性文化生产的授益诱导型政府监管模式

对我国城市文化建设中商品性文化生产的＂授益诱导型＂政府监管模式推行的必要性及其内涵、特征、类别、功能以及制度设计应注意的问题展开了论述,以期有助于提升我国城市政府对商品性文化生产监管的有效度,推进城市文化建设的良性运行。     

Structural equation models for area health outcomes with model selection

Recent analyses seeking to explain variation in area health outcomes often consider the impact on them of latent measures (i.e. unobserved constructs) of population health risk. The latter are typically obtained by forms of multivariate analysis, with a small set of latent constructs derived from a collection of observed indicators, and a few recent area studies take such constructs to be spatially structured rather than independent over areas. A confirmatory approach is often applicable to the model linking indicators to constructs, based on substantive knowledge of relevant risks for particular diseases or outcomes. In this paper, population constructs relevant to a particular set of health outcomes are derived using an integrated model containing all the manifest variables, namely health outcome variables, as well as indicator variables underlying the latent constructs. A further feature of the approach is the use of variable selection techniques to select significant loadings and factors (especially in terms of effects of constructs on health outcomes), so ensuring parsimonious models are selected. A case study considers suicide mortality and self-harm contrasts in the East of England in relation to three latent constructs: deprivation, fragmentation and urbanicity.     

A Weighted Composite Dose – Response Model For Human Salmonellosis

This article describes the development of a weighted composite dose – response model for human salmonellosis. Data from previously reported human challenge studies were categorized into two different groups representing low and moderately virulent/pathogenic Salmonella strains based on a disease end point. Because epidemiological data indicate that some Salmonella strains are particularly pathogenic, and in the absence of human feeding study data for such strains, Shigella dysenteriae was used as a proxy for highly virulent strains. Three single-hit dose – response models were applied to the human feeding study data and evaluated for best fit using maximum likelihood estimation: (1) the exponential (E-1pop), (2) the two-subpopulation exponential (E-2pop), and (3) the Beta-Poisson (BP). Based on the goodness-of-fit test, the E-1pop and BP were the best-fit models for low and moderately virulent/pathogenic Salmonella strains, and the E-2pop and BP models were better for highly virulent/pathogenic strains. Epistemic analysis was conducted by determining the degree of confidence associated with the selected models, which was found to be 50%/50% (E-1pop/BP) for low and moderately pathogenic Salmonella strains, and 9.8%/90.2% (E-2pop/BP) for highly virulent strains. The degree of confidence for each component model and variations in the proportion of strains within each virulence/pathogenicity category were incorporated into the overall composite model. This study describes the influence of variation in strain virulence and host susceptibility on the shape of the population dose – response relationship.     

蒙古语多方言语音识别及共享识别模型探索

通过语音输入方式准确识别蒙古语主要方言 ,并为其建立共享识别模型 ,对实现方言间的信息自动交换及语音识别技术的实用化具有实际意义。针对蒙古语多方言的语音特点 ,可以采取独立的或者共享识别模型进行识别。本文介绍的抽出初步模型和建立共享模型的方法 ,简便易行 ,但识别精度有待提高。文中探索了通过借用数据量和质较完备的语言的语音数据提高识别率的可能性。     

Identifiable finite mixtures of location models for clustering mixed-mode data

For clustering mixed categorical and continuous data, Lawrence and Krzanowski (1996) proposed a finite mixture model in which component densities conform to the location model. In the graphical models literature the location model is known as the homogeneous Conditional Gaussian model. In this paper it is shown that their model is not identifiable without imposing additional restrictions. Specifically, for g groups and m locations, (g!)m–1 distinct sets of parameter values (not including permutations of the group mixing parameters) produce the same likelihood function. Excessive shrinkage of parameter estimates in a simulation experiment reported by Lawrence and Krzanowski (1996) is shown to be an artifact of the model's non-identifiability. Identifiable finite mixture models can be obtained by imposing restrictions on the conditional means of the continuous variables. These new identified models are assessed in simulation experiments. The conditional mean structure of the continuous variables in the restricted location mixture models is similar to that in the underlying variable mixture models proposed by Everitt (1988), but the restricted location mixture models are more computationally tractable.     

Constructing optimal designs for fitting pharmacokinetic models

We consider some computational issues that arise when searching for optimal designs for pharmacokinetic (PK) studies. Special factors that distinguish these are (i) repeated observations are taken from each subject and the observations are usually described by a nonlinear mixed model (NLMM), (ii) design criteria depend on the model fitting procedure, (iii) in addition to providing efficient parameter estimates, the design must also permit model checking, (iv) in practice there are several design constraints, (v) the design criteria are computationally expensive to evaluate and often numerical integration is needed and finally (vi) local optimisation procedures may fail to converge or get trapped at local optima.We review current optimal design algorithms and explore the possibility of using global optimisation procedures. We use these latter procedures to find some optimal designs.For multi-purpose designs we suggest two surrogate design criteria for model checking and illustrate their use.     

A New Model of Trend Inflation

This article introduces a new model of trend inflation. In contrast to many earlier approaches, which allow for trend inflation to evolve according to a random walk, ours is a bounded model which ensures that trend inflation is constrained to lie in an interval. The bounds of this interval can either be fixed or estimated from the data. Our model also allows for a time-varying degree of persistence in the transitory component of inflation. In an empirical exercise with CPI inflation, we find the model to work well, yielding more sensible measures of trend inflation and forecasting better than popular alternatives such as the unobserved components stochastic volatility model. This article has supplementary materials online.     

Inference Under Right Censoring in a Discrete Setup

In this article, we consider the right random censoring scheme in a discrete setup when the lifetime and censoring variables are independent and have geometric distributions with means 1/θ₁ and 1/θ₂, respectively. We first obtain the Maximum Likelihood and Method of Moment estimators of the unknown parameters. We also find the Bayes and Posterior Regret Gamma Minimax estimators of the parameters for the two cases when the prior distributions are dependent and independent, assuming a squared error loss function. We then discuss the Proportional Hazard model, and obtain Maximum Likelihood estimators of the unknown parameters and derive the Bayes estimators assuming squared error loss using Markov Chain Monte Carlo methods.     

Additive Transformation Models for Recurrent Events

In this article, we propose a class of additive transformation models for recurrent event data, which includes the additive rates model as a special case. The new models offer great flexibility in formulating the effects of covariates on the mean function of recurrent events. Estimating equation approaches are developed for the model parameters, and asymptotic properties of the resulting estimators are established. In addition, a model checking procedure is presented to assess the adequacy of the model. The finite sample performance of the proposed estimators is examined through simulation studies, and an application to a bladder cancer study is presented.