基于情境线索的属性趋同效应研究

情境效应是研究消费行为的重要视角和研究热点。情境线索是消费者形成推论并作出选择的重要依据,在属性信息不完全的情形下,产品属性的同异会成为重要的外部线索并影响消费者的风险感知和购买行为。不同于由选择集内属性量差结构所导致的传统情境效应形式和知觉聚焦效应,本文从情境线索角度提出了属性趋同效应,揭示了在信息不完全情形下属性趋同的正面效用,验证了不同风险感知强度对属性趋同效应的影响,并研究了趋同属性对消费者风险感知的影响,发现和验证了新的情境效应形式。     

A Closed-Form Formula for an Option with Discrete and Continuous Barriers

This article presents a methodology to derive analytical formulas for a class of complicated financial derivatives with a continuously monitored barrier and a few discretely monitored ones. Numerical results based on concrete numbers for the parameters are presented and analyzed.     

A bivariate noncentral T-disttrtoution with applications

The particular bivariate noncentral t-distribution associated with two univariate noncentral t variates having a correlation coefficient of one is considered. Some applications and properties are presented together with tables in the same form as Johnson and Welch's tables for a univariate noncentral t-distribution.     

Telecommunications Network Design—Comparison of Alternative Approaches

The design and development of the network infrastructure to support mission‐critical applications has become a critical and‐complex activity. This study explores the use of genetic algorithms (GA) for network design in the context of degree‐constrained minimal spanning tree (DCMST) problem; compares for small networks the performance of GA with a mathematical model that provides optimal solutions; and for larger networks, compares GA's performance with two heuristic methods—edge exchange and primal algorithm. Two performance measures, solution quality and computation time, are used for evaluation. The algorithms are evaluated on a wide variety of network sizes with both static and dynamic degree constraints on the network nodes. The results indicate that GA provides optimal solutions for small networks. For larger networks it provides better solution quality compared to edge exchange and primal method, but is worse than the two methods in computation time.     

Burn-in Time and Estimation of Change-Point with Weibull-Exponential Mixture Distribution*

Many industrial products have three phases in their product lives: infant-mortality, normal, and wear-out phases. In the infant-mortality phase, the failure rate is high, but decreasing; in the normal phase, the failure rate remains constant; and in the wear-out phase, the failure rate is increasing. A burn-in procedure may be used to reduce early failures before shipping a product to consumers. A cost model is formulated to find the optimal burn-in time, which minimizes the expected sum of manufacturing cost, burn-in cost, and warranty cost incurred by failed items found during the warranty period. A mixture of Weibull hyperexponential distribution with shape parameter less than one and exponential distribution is used to describe the infant-mortality and the normal phases of the product life. The product under consideration can be either repairable or non-repairable. When the change-point of the product life distribution is unknown, it is estimated by using the maximum-likelihood estimation method. The effects of sample size on estimation error and the performance of the model are studied, and a sensitivity analysis is performed to study the effects of several parameters of the W-E distribution and costs on the optimal burn-in time.