Entropy-based tests of uniformity: A Monte Carlo power comparison

In this article, we consider different entropy estimators and propose some entropy-based tests of uniformity. Critical values of the proposed test statistics are obtained by Monte Carlo simulation. Then the power values of the tests for various alternatives and sample sizes are compared. Finally, some recommendations for the application of the proposed tests in practice are presented.     

The odd log-logistic Lindley Poisson model for lifetime data

We define two new lifetime models called the odd log-logistic Lindley (OLL-L) and odd log-logistic Lindley Poisson (OLL-LP) distributions with various hazard rate shapes such as increasing, decreasing, upside-down bathtub, and bathtub. Various structural properties are derived. Certain characterizations of OLL-L distribution are presented. The maximum likelihood estimators of the unknown parameters are obtained. We propose a flexible cure rate survival model by assuming that the number of competing causes of the event of interest has a Poisson distribution and the time to event has an OLL-L distribution. The applicability of the new models is illustrated by means real datasets.     

A new exponentiated class of distributions: Properties and applications

Motivated by series-parallel-series systems, we introduce a new generator of continuous distributions with three extra parameters called the generalized exponentiated class of distributions. We study its mathematical properties and introduce a bivariate extension of the class. We discuss the estimation of its parameters by maximum likelihood and illustrate the potentiality of the class by applications to two real datasets.     

本地社区的角色:对知识区域发展理论的修正

到了21世纪初,知识作为城乡区域发展的关键因素的重要意义已经完全确立,然而,作为知识型发展赖以确立的具体机制和体制关系的研究却只是在近些年才开始而且还有些欠缺.本文主要基于"三重螺旋模式"(Etzkowitz & Klofsten,2005),这种模式提出,大学、企业和政府是所有知识型发展的关键因素.本文是澳大利亚昆士兰州知识型社区发展的个案研究,考察了"三重螺旋模式"在地方和区域之间相互关系中的作用.表明了作为创新基地的社区在各关键因素的相互作用中的核心作用,并提出了"四重螺旋模式"的设想.在"四重螺旋模式"中,社区在新经济构建过程中与企业、大学、政府具有同等重要的作用.结论:只有社区,企业,大学,政府四个因素有机融合,共同作用,知识型发展才会得到提高.     

Robust weighted generalized estimating equations based on statistical depth

Most of the longitudinal data contain influential points and for analyzing them generalized and weighted generalized estimating equations (GEEs and WGEEs) are highly influenced by these points. An approach for dealing with outliers is having weight functions. In this article, we propose some new weights based on the statistical depth. These weights express centrality of points with respect to the whole sample with a smaller depth (larger depth) for the point far from the center (for the point near the center). The proposed approach leads to robust WGEE. These approaches are applied on two real datasets and some simulation studies.     

The exponentiated transmuted-G family of distributions: Theory and applications

In this paper, a new family of continuous distributions called the exponentiated transmuted-G family is proposed which extends the transmuted-G family defined by Shaw and Buckley (2007). Some of its mathematical properties including explicit expressions for the ordinary and incomplete moments, generating function, Rényi and Shannon entropies, and order statistics are derived. Some special models of the new family are provided. The maximum likelihood is used for estimating the model parameters. We provide the simulation results to assess the performance of the proposed model. The usefulness and flexibility of the new family is illustrated using real data.     

Markov switching component GARCH model: Stability and forecasting

ABSTRACT

This paper introduces an extension of the Markov switching GARCH model where the volatility in each state is a convex combination of two different GARCH components with time varying weights. This model has the dynamic behavior to capture the variants of shocks. The asymptotic behavior of the second moment is investigated and an appropriate upper bound for it is evaluated. Using the Bayesian method via Gibbs sampling algorithm, a dynamic method for the estimation of the parameters is proposed. Finally, we illustrate the efficiency of the model by simulation and also by considering two different set of empirical financial data. We show that this model provides much better forecasts of the volatility than the Markov switching GARCH model.     

A new generalized odd log-logistic family of distributions

We introduce and study general mathematical properties of a new generator of continuous distributions with three extra parameters called the new generalized odd log-logistic family of distributions. The proposed family contains several important classes discussed in the literature as submodels such as the proportional reversed hazard rate and odd log-logistic classes. Its density function can be expressed as a mixture of exponentiated densities based on the same baseline distribution. Some of its mathematical properties including ordinary moments, quantile and generating functions, entropy measures, and order statistics, which hold for any baseline model, are presented. We also present certain characterization of the proposed distribution and derive a power series for the quantile function. We discuss the method of maximum likelihood to estimate the model parameters. We study the behavior of the maximum likelihood estimator via simulation. The importance of the new family is illustrated by means of two real data sets. These applications indicate that the new family can provide better fits than other well-known classes of distributions. The beauty and importance of the new family lies in its ability to model real data.