A unified approach to bivariate discrete distributions

Here we introduce a bivariate generalized hypergeometric factorial moment distribution (BGHFMD) through its probability generating function (p.g.f.) whose marginal distributions are the generalized hypergeometric factorial moment distributions introduced by Kemp and Kemp (Bull Int Stat Inst 43:336–338,1969). Well-known bivariate versions of distributions such as binomial, negative binomial and Poisson are special cases of this distribution. A genesis of the distribution and explicit closed form expressions for the probability mass function of the BGHFMD, its factorial moments and the p.g.f.’s of its conditional distributions are derived here. Certain recurrence relations for probabilities, moments and factorial moments of the bivariate distribution are also established.     

Influence analysis for Poisson inverse Gaussian regression models based on the EM algorithm

For Poisson inverse Gaussian regression models, it is very complicated to obtain the influence measures based on the traditional method, because the associated likelihood function involves intractable expressions, such as the modified Bessel function. In this paper, the EM algorithm is employed as a basis to derive diagnostic measures for the models by treating them as a mixed Poisson regression with the weights from the inverse Gaussian distributions. Several diagnostic measures are obtained in both case-deletion model and local influence analysis, based on the conditional expectation of the complete-data log-likelihood function in the EM algorithm. Two numerical examples are given to illustrate the results.     

政府管制、企业创新与IPO时机选择

基于美式期权的思想,研究了在中国政府加快注册制改革的背景下政府行为和企业创新对IPO时机选择的影响,用泊松跳随机过程描述技术创新对企业现金流的冲击,即通过影响企业价值进而影响企业上市时机选择。利用2001—2015年的数据进行实证分析,从定性和定量的角度深入探讨了市场变量、政府变量和企业创新与IPO数量之间的动态关系。     

Local influence analysis for Poisson autoregression with an application to stock transaction data

In statistical diagnostics and sensitivity analysis, the local influence method plays an important role and has certain advantages over other methods in several situations. In this paper, we use this method to study time series of count data when employing a Poisson autoregressive model. We consider case‐weights, scale, data, and additive perturbation schemes to obtain their corresponding vectors and matrices of derivatives for the measures of slope and normal curvatures. Based on the curvature diagnostics, we take a stepwise local influence approach to deal with data with possible masking effects. Finally, our established results are illustrated to be effective by analyzing a stock transactions data set.     

An application of the generalized Poisson difference distribution to the Bayesian modelling of football scores

The analysis of sports data, in particular football match outcomes, has always produced an immense interest among the statisticians. In this paper, we adopt the generalized Poisson difference distribution (GPDD) to model the goal difference of football matches. We discuss the advantages of the proposed model over the Poisson difference (PD) model, which was also used for the same purpose. The GPDD model, like the PD model, is based on the goal difference in each game that allows us to account for the correlation without explicitly modelling it. The main advantage of the GPDD model is its flexibility in the tails by considering shorter as well as longer tails than the PD distribution. We carry out the analysis in a Bayesian framework in order to incorporate external information, such as historical knowledge or data, through the prior distributions. We model both the mean and the variance of the goal difference and show that such a model performs considerably better than a model with a fixed variance. Finally, the proposed model is fitted to the 2012–2013 Italian Serie A football data, and various model diagnostics are carried out to evaluate the performance of the model.     

Evaluation of the MEMM, Parameter Estimation and Option Pricing for Geometric Lévy Processes

In this paper, we shall propose a useful approach to evaluate concretely the MEMM (minimal entropy martingale measure) for the typical geometric Lévy processes such as compound Poisson, stable, VG (Variance Gamma), CGMY (Carr-Geman-Madan-Yor), NIG (Normal Inverse Gaussian), etc. In addition, we shall estimate the parameters of geometric Lévy processes and value the European call option and Asian call option using the Nikkei financial data.     

保费收入随机化条件下的保险公司破产概率模型研究

保费收入是保险公司破产概率的重要影响因素。传统的保险公司破产概率模型常将保费收入过程看作连续的确定性过程,然而在现实中,保费收入过程却是一个离散的随机过程。本文用复合泊松过程描述保费收入,从而将确定性保费收入条件下的破产概率模型拓展到随机化保费收入条件下的破产概率模型,在此基础上模拟计算了保险公司破产概率,并比较分析了不同的保险资金投资模式对破产概率的影响。     

分布式嵌套阵列及其DOA估计

提出了一种分布式嵌套阵列天线结构,由两个相互独立的四级嵌套子阵构成。两个子阵间存在一个基线长度,且满足一定条件。对该阵列天线接收到的信号进行高阶累积量和Khatri-Rao积运算可以得到三个完全相同的均匀直线阵列天线结构。针对新得到的阵列天线结构,使用基于空间平滑技术的双尺度酉旋转不变子空间（ESPRIT）波达方向（DOA）估计算法对信号进行DOA估计。该方法可以有效地提高阵列天线的自由度,进而达到提高估计精度的目地。仿真结果证明了基于所提出阵列天线结构的DOA估计方法的有效性。     

基于排队论的医院急诊模型

本文用排队论的方法对医院急诊室排队系统进行分析,确定了该系统的的排队模型。给出了统计平衡条件的排队系统的主要指标。讨论了该排队系统的最优化,并进行了实例分析。     

要素价格扭曲与异质性企业区位选择——基于泊松面板回归的实证研究

采用生产函数法,论文详细测算了1998-2007年中国大陆除西藏以外267个城市的要素价格扭曲。接下来,运用中国工业企业数据库中的数据,构建泊松面板回归模型考察了要素价格扭曲对于不同生产效率企业选址产生的影响。按行业、区域分别划分样本以及稳健性检验均表明:(1)较高的要素价格扭曲水平对于该地区新进入的低效率企业的期望值具有显著为正的影响,而对于该地区新进入的高效率企业的期望值则具有显著为负的影响;(2)地区运输成本和环境管制强度对于不同效率企业选址的影响存在异质性,而其他区域特征解释变量对于选址企业数量的影响均显著,且方向与理论预期一致。