基于自适应相关滤波器的人眼定位改进方法

人眼定位是人脸识别、视觉跟踪系统中一个关键步骤。针对传统人眼定位方法定位精度低、误差稳定性差的问题,提出将自适应相关滤波器与灰度积分投影法相结合,在训练和测试阶段做了两点改进。首先对训练得到的自适应相关滤波器,在\[-0.2,-0.1,0,0.1,0.2\]角度范围内旋转得到5个滤波器,选择灰度最大值对应的位置作为初始定位点,然后在该定位点的5×5邻域内,水平和垂直方向分别做灰度积分投影,最后选择积分最小值的位置作为最终的目标定位点。与平均合成精确滤波器、最小输出平方误差和滤波器、自适应合成相关滤波器算法相比,改进算法的定位精度最大提高2.9%,平均绝对误差和标准差均低于原始算法。实验结果表明,提出的改进算法在定位精度和稳定性上均优于以上算法。     

Conditional risk measures in a bipartite market structure

In this paper, we study the effect of network structure between agents and objects on measures for systemic risk. We model the influence of sharing large exogeneous losses to the financial or (re)insurance market by a bipartite graph. Using Pareto-tailed losses and multivariate regular variation, we obtain asymptotic results for conditional risk measures based on the Value-at-Risk and the Conditional Tail Expectation. These results allow us to assess the influence of an individual institution on the systemic or market risk and vice versa through a collection of conditional risk measures. For large markets, Poisson approximations of the relevant constants are provided. Differences of the conditional risk measures for an underlying homogeneous and inhomogeneous random graph are illustrated by simulations.     

The asymptotic expansion of the regular discretization error of Itô integrals

We study an Edgeworth‐type refinement of the central limit theorem for the discretization error of Itô integrals. Toward this end, we introduce a new approach, based on the anticipating Itô formula. This alternative technique allows us to compute explicitly the terms of the corresponding expansion formula. Two applications to finance are given; the asymptotics of discrete hedging error under the Black–Scholes model and the difference between continuously and discretely monitored variance swap payoffs under stochastic volatility models.     

An empirical analysis of multiple services and choices of consumer in the Swedish telecommunications market

Received analyses state that firms can use a multiple services offering strategy to retain customers and capture new customers. Factors that determine the multiple services strategy include product discount, service provider and customer characteristics. Consequently, this study addresses the fundamental question: what are the key determining factors that explain the probability that a consumer buys multiple services? A generalized Poisson regression model is employed to examine whether the product discount, service provider, socio-economic variables and geographical location impact consumer decisions. Data from a national survey in 2009 commissioned by Post-och Telestyrelsen, the Swedish telecommunications regulator, are analyzed. The results clearly show that the discount, service provider and income of the consumer affect the consumer׳s buying decision. A consumer who receives a discount or has a high income is more likely to buy multiple services or select more services from the current service provider into his basket than a consumer who does not receive discount or has lower income. Service providers, cable TV operators and telecommunications carriers can also lock-in their consumer and expand their market position from one particular service to another using bundling service. Thus, this may be the time for the telecommunications regulator to consider the market definition.     

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.