The beta skew-generalized normal distribution

In this article, we develop the skew-generalized normal distribution introduced by Arellano-Valle et al. (2004 Arellano-Valle, R.B., Gomez, H.W., Quintana, F.A. (2004). A new class of skew-normal distribution. Commun. Stat. - Theory Methods. 33(7):1465–1480.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) to a new family of the Beta skew-generalized normal (BSGN) distribution . Here, we present some theorems and properties of BSGN distribution and obtain its moment-generating function.     

A mixed-effects least square support vector regression model for three-level count data

Hierarchical study design often occurs in many areas such as epidemiology, psychology, sociology, public health, engineering, and agriculture. This imposes correlation in data structure that needs to be account for in modelling process. In this study, a three-level mixed-effects least squares support vector regression (MLS-SVR) model is proposed to extend the standard least squares support vector regression (LS-SVR) model for handling cluster correlated data. The MLS-SVR model incorporates multiple random effects which allow handling unequal number of observations for each case at non-fixed time points (a very unbalanced situation) and correlation between subjects simultaneously. The methodology consists of a regression modelling step that is performed straightforwardly by solving a linear system. The proposed model is illustrated through numerical studies on simulated data sets and a real data example on human Brucellosis frequency. The generalization performance of the proposed MLS-SVR is evaluated by comparing to ordinary LS-SVR and some other parametric models.     

内生态型政府的内涵及其构建

目前,生态型政府这种新的政府范式理论的研究大都集中在外生态型政府层面,其内涵被界定为致力于实现人与自然之自然性和谐的政府。事实上,外生态型政府就是所谓的"绿色政府""环保型政府"或"环境友好型政府"。但是,随着现实社会的发展和理论研究的深入,内生态型政府比外生态型政府更能适应现实的需要。内生态型政府不仅可以实现人与自然的和谐发展,更可以促进政府自身的重塑和再造,进而实现政府善治的终极目标。而要顺利实现这种由"外"向"内"思维路径的转变,必须制定政府内生态化的根本目标,确立政府可持续发展的价值取向,坚持政府职能统筹的原则,选择政府生态化的网络治理模式,完善生态型政府的制度保障。     

Random effect exponentiated-exponential geometric model for clustered/longitudinal zero-inflated count data

For count responses, there are situations in biomedical and sociological applications in which extra zeroes occur. Modeling correlated (e.g. repeated measures and clustered) zero-inflated count data includes special challenges because the correlation between measurements for a subject or a cluster needs to be taken into account. Moreover, zero-inflated count data are often faced with over/under dispersion problem. In this paper, we propose a random effect model for repeated measurements or clustered data with over/under dispersed response called random effect zero-inflated exponentiated-exponential geometric regression model. The proposed method was illustrated through real examples. The performance of the model and asymptotical properties of the estimations were investigated using simulation studies.KEYWORDS: Count model, under- and over-dispersion, zero-inflation, mixture model, zero-inflated poisson model     

Using a grey relational analysis in an improved Grunow–Finke assessment tool to detect unnatural epidemics

The Grunow–Finke epidemiological assessment tool (GFT) has several limitations in its ability to differentiate between natural and man-made epidemics. Our study aimed to improve the GFT and analyze historical epidemics to validate the model. Using a gray relational analysis (GRA), we improved the GFT by revising the existing standards and adding five new standards. We then removed the artificial weights and final decision threshold. Finally, by using typically unnatural epidemic events as references, we used the GRA to calculate the unnatural probability and obtain assessment results. Using the advanced tool, we conducted retrospective and case analyses to test its performance. In the validation set of 13 historical epidemics, unnatural and natural epidemics were divided into two categories near the unnatural probability of 45%, showing evident differences (p < 0.01) and an assessment accuracy close to 100%. The unnatural probabilities of the Ebola virus disease of 2013 and Middle East Respiratory Syndrome of 2012 were 30.6% and 36.1%, respectively. Our advanced epidemic assessment tool improved the accuracy of the original GFT from approximately 55% to approximately 100% and reduced the impact of human factors on these outcomes effectively.