Test of Normality Against Generalized Exponential Power Alternatives

The family of symmetric generalized exponential power (GEP) densities offers a wide range of tail behaviors, which may be exponential, polynomial, and/or logarithmic. In this article, a test of normality based on Rao's score statistic and this family of GEP alternatives is proposed. This test is tailored to detect departures from normality in the tails of the distribution. The main interest of this approach is that it provides a test with a large family of symmetric alternatives having non-normal tails. In addition, the test's statistic consists of a combination of three quantities that can be interpreted as new measures of tail thickness. In a Monte-Carlo simulation study, the proposed test is shown to perform well in terms of power when compared to its competitors.     

A Measure of Discrimination Between two Double Truncated Distributions

On the basis of Kullback-Leibler discrimination information, and of discrimination measures introduced by Ebrahimi and Kirmani (1996a) and by Di Crescenzo and Longobardi (2004), we propose a measure of discrepancy between double truncated distributions. Some properties of this measure are studied and some mistakes in the preceding literature are corrected.     

Joint modeling of multivariate longitudinal mixed measurements and time to event data using a Bayesian approach

In many longitudinal studies multiple characteristics of each individual, along with time to occurrence of an event of interest, are often collected. In such data set, some of the correlated characteristics may be discrete and some of them may be continuous. In this paper, a joint model for analysing multivariate longitudinal data comprising mixed continuous and ordinal responses and a time to event variable is proposed. We model the association structure between longitudinal mixed data and time to event data using a multivariate zero-mean Gaussian process. For modeling discrete ordinal data we assume a continuous latent variable follows the logistic distribution and for continuous data a Gaussian mixed effects model is used. For the event time variable, an accelerated failure time model is considered under different distributional assumptions. For parameter estimation, a Bayesian approach using Markov Chain Monte Carlo is adopted. The performance of the proposed methods is illustrated using some simulation studies. A real data set is also analyzed, where different model structures are used. Model comparison is performed using a variety of statistical criteria.     

A Polynomial Logistic Distribution and its Applications in Finance

The importance of Logistic distribution has been widely recognized in many applied areas such as, demography, population studies, finance, agriculture, etc. Since its introduction as a model, much attention has been paid to the study of several generalizations of it, which would offer additional flexibility when data fitting is chased. In the present paper we introduce and develop a natural generalization of the Logistic distribution by considering a probability model whose logit cumulative distribution function transformation is of polynomial type. The performance of the model's fitting to financial data, using different parameter estimation methods, is also investigated.     

Costing Mixed Coxian Phase-type Systems with Poisson Arrivals

Previously, we developed a modeling framework which classifies individuals with respect to their length of stay (LOS) in the transient states of a continuous-time Markov model with a single absorbing state; phase-type models are used for each class of the Markov model. We here add costs and obtain results for moments of total costs in (0, t], for an individual, a cohort arriving at time zero and when arrivals are Poisson. Based on stroke patient data from the Belfast City Hospital we use the overall modelling framework to obtain results for total cost in a given time interval.     

考虑共同跳跃的波动建模:基于高频数据视角

针对共同跳跃研究的不足,文章沿袭已有理论框架,采用常用的日内跳跃检验方法,构建了共同跳跃(协)方差和连续样本路径(协)方差,并扩展HAR-RV-CJ模型,将(协)方差、共同跳跃置于统一波动模型框架内。通过对上证综指和深圳成指高频数据的实证分析,结果显示两指数共同跳跃占其各自的跳跃比例较大,且基本上都是同方向的跳跃;共同跳跃(协)方差和连续样本路径(协)方差对已实现(协)方差的影响都是显著的,考虑共同跳跃影响有助于提高(协)方差建模的准确性。此研究有助于投资者优化投资策略和为监管部门提供监管基础。     

A Bayesian analysis for the multivariate point null testing problem

A Bayesian test for the point null testing problem in the multivariate case is developed. A procedure to get the mixed distribution using the prior density is suggested. For comparisons between the Bayesian and classical approaches, lower bounds on posterior probabilities of the null hypothesis, over some reasonable classes of prior distributions, are computed and compared with the p-value of the classical test. With our procedure, a better approximation is obtained because the p-value is in the range of the Bayesian measures of evidence.     

Zur bestimmung von bayes-lösungen bei messender prüfung

A model of sampling inspection by variables is presented, where the loss function and the a priori distribution of the unknown defective fraction are assumed to be known. The existence of a BAYES rule in the class of relevant decision functions (sampling plans) is proved. Furthermore, it is shown that under certain conditions the decision functions "accept without sampling" or "reject without sampling" are BAYESian decision rules. Some numerical examples are presented to illustrate the theory developed in the paper.     

The inverse problem of multivariate and matrix-variate skew normal distributions

In this paper, we prove that the joint distribution of random vectors Z ₁ and Z ₂ and the distribution of Z ₂ are skew normal provided that Z ₁ is skew normally distributed and Z ₂ conditioning on Z ₁ is distributed as closed skew normal. Also, we extend the main results to the matrix variate case.     

Asymptotic normality of numbers of observations in random regions determined by order statistics

In this paper, we study the joint limiting behaviour of numbers of observations that fall into regions determined by order statistics and Borel sets. We show that suitably centred and normed versions of these numbers are asymptotically multivariate normal under some conditions. We consider two cases: one where the population distribution function is discontinuous and the other where it is continuous and the order statistics are extreme. Finally, we compare results obtained for the two cases with their analogues for absolutely continuous distribution function and central-order statistics.