Assessing Type I error and power of multistate Markov models for panel data—A simulation study

Ordinal outcomes collected at multiple follow-up visits are common in clinical trials. Sometimes, one visit is chosen for the primary analysis and the scale is dichotomized amounting to loss of information. Multistate Markov models describe how a process moves between states over time. Here, simulation studies are performed to investigate the Type I error and power characteristics of multistate Markov models for panel data with limited non-adjacent state transitions. The results suggest that the multistate Markov models preserve the Type I error and adequate power is achieved with modest sample sizes for panel data with limited non-adjacent state transitions.     

Approximate confidence intervals for a linear combination of binomial proportions: A new variant

We propose a new adjustment for constructing an improved version of the Wald interval for linear combinations of binomial proportions, which addresses the presence of extremal samples. A comparative simulation study was carried out to investigate the performance of this new variant with respect to the exact coverage probability, expected interval length, and mesial and distal noncoverage probabilities. Additionally, we discuss the application of a criterion for interpreting interval location in the case of small samples and/or in situations in which extremal observations exist. The confidence intervals obtained from the new variant performed better for some evaluation measures.     

On the skewness of extreme order statistics from heterogeneous samples

The effect of heterogeneity on order statistics has attracted much attention in recent decades. In this paper, we study the skewness of extreme order statistics from heterogeneous samples in the scale models according to star ordering. It is shown that, without any restriction on the scale parameters, the skewness of extreme order statistics from heterogeneous samples is larger than that from homogeneous samples. We further extend this study to the multiple-outlier scale models. Some examples and applications are highlighted as well.     

Third and fourth moments of vector autoregressions with regime switching

We derive matrix formulae in closed form for the unconditional third and fourth moments of a broad class of vector autoregressive time series with regime switching. First and second moments are well known. New measures of multivariate skewness and kurtosis are introduced and basic properties are investigated. The knowledge of series level, variation, co-movements, skewness, and kurtosis is useful to support model interpretation in real data application. Numerical examples complete the paper.     

The asymptotic behaviour of the residual sum of squares in models with multiple break points

ABSTRACT

Models with multiple discrete breaks in parameters are usually estimated via least squares. This paper, first, derives the asymptotic expectation of the residual sum of squares and shows that the number of estimated break points and the number of regression parameters affect the expectation differently. Second, we propose a statistic for testing the joint hypothesis that the breaks occur at specified points in the sample. Our analytical results cover models estimated by the ordinary, nonlinear, and two-stage least squares. An application to U.S. monetary policy rejects the assumption that breaks are associated with changes in the chair of the Fed.     

Decision process in one-of-a-kind production

One-of-a-kind production is project business. In any project, the decision process is of importance for a successful outcome. However, there is a need for a more formal approach to decision-making in projects. We have therefore, developed a model of the decision process. It uses three types of decisions: selection, authorisation and plan decisions. Enterprise models together with the project life cycle form a platform for developing a decision model. The life cycle enables a decision chain, whereas the enterprise models allow decisions at different levels. The horizontal decision chain is linked to physical flow of documents and materials. Vertically, decision-making is hierarchically decomposed into levels of different planning horizons and time periods.     

Fauconnier的空间映射论及其在话语分析中的应用

认知语言学为研究和解释语言现象提供了新的研究方法,属于广义认知科学的一个分支。文章在以Lakoff为代表的认知语义学的基础上,运用Fauconn ier模式理论中的心理空间理论和空间映射论,采用认知科学的理念分析解释了隐喻转喻映射和ICM的图式映射等问题,并指出作为一种重要的认知模式,其空间理论可以帮助解读许多日常言语交际中“非常规”的语言事实,为许多语言现象的研究提供了一种新的认知模式。     

结构型一般EV线性模型参数估计的相合性研究

本文对结构型一般EV线性模型参数的研究,利用矩方法,得到了未知参数的强相合性。     

A UNIFIED FRAMEWORK FOR MODELLING WILDLIFE POPULATION DYNAMICS†

This paper proposes a unified framework for defining and fitting stochastic, discrete‐time, discrete‐stage population dynamics models. The biological system is described by a state‐space model, where the true but unknown state of the population is modelled by a state process, and this is linked to survey data by an observation process. All sources of uncertainty in the inputs, including uncertainty about model specification, are readily incorporated. The paper shows how the state process can be represented as a generalization of the standard Leslie or Lefkovitch matrix. By dividing the state process into subprocesses, complex models can be constructed from manageable building blocks. The paper illustrates the approach with a model of the British grey seal metapopulation, using sequential importance sampling with kernel smoothing to fit the model.