Issues in the use of ratings-based versus choice-based conjoint analysis in operations management research

Conjoint analysis has played an important role in helping make a number of operations management decisions including product and service design, supplier selection, and service operations capacity. Many recent advances in this area have raised questions about the most appropriate form of conjoint analysis for this research. We review recent developments in the literature and provide new evidence on how the choice between ratings- and choice-based conjoint models might affect the estimates of customer demand used in operations management models.     

Willingness-to-pay estimation with choice-based conjoint analysis: Addressing extreme response behavior with individually adapted designs

The increasing consideration of behavioral aspects in operations management models has prompted greater use of choice-based conjoint (CBC) studies in operations research. Such studies can elicit consumers’ willingness to pay (WTP), a core input for many optimization models. However, optimization models can yield valid results only if consumers’ WTP is estimated accurately. A simulation study and two field studies show that extreme response behavior in CBC studies, such that consumers always or never choose the no-purchase option, harms the validity of WTP estimates. Reporting the share of consumers who always and never select the no-purchase option allows for detecting extreme response behavior. This study suggests an individually adapted design that avoids extreme response behavior and thus significantly improves WTP estimation accuracy.     

Measurement of preferences with self-explicated approaches: A classification and merge of trade-off- and non-trade-off-based evaluation types

Self-explicated approaches are popular preference measurement approaches for products with many attributes. This article classifies previous self-explicated approaches according to their evaluation types, i.e. trade-off- versus non-trade-off-based, and outlines their advantages and disadvantages. In addition, it proposes a new method, the presorted adaptive self-explicated approach that is based on Netzer and Srinivasan’s (2011) adaptive self-explicated approach and that combines trade-off- and non-trade-off-based evaluation types. Two empirical studies compare this new method with the most popular existing self-explicated approaches, including the adaptive self-explicated approach and paired comparison preference measurement. The new method overcomes the insufficient discrimination between importance weights, as usually found in non-trade-off-based evaluation types; discourages respondents’ simplification strategies, as are frequently encountered in trade-off evaluation types; is easy to implement; and yields high predictive validity compared with other popular self-explicated approaches.     

Modelling the independent insurance agent's product evaluation strategy: An application of conjoint analysis

A major property-casualty insurance company had streamlined underwriting procedures and hoped to design a new commercial insurance package which would appeal to its independent agents. They hoped to do this by improving service times, premium and/or commission, but making these improvements would require the agents to fill out a new underwriting form. The research used conjoint analysis to determine for management the optimum levels of each of the factors to be employed in the new programme and the possible negative impact of the new underwriting form. Also discussed are issues relating to the use of conjoint analysis; in particular, the handling of large factorial designs and the aggregation of individual results.     

Improved probabilistic point estimation schemes for uncertainty analysis

The probabilistic point estimation (PPE) methods replace the probability distribution of the random parameters of a model with a finite number of discrete points in sample space selected in such a way to preserve limit probabilistic information of involved random parameters. Most PPE methods developed thus far match the distribution of random parameters up to the third statistical moment and, in general, could provide reasonable accurate estimation only for the first two statistical moments of model output. This study proposes two optimization-based point selection schemes for the PPE methods to enhance the accuracy of higher-order statistical moments estimation for model output. Several test models of varying degrees of complexity and nonlinearity are used to examine the performance of the proposed point selection schemes. The results indicate that the proposed point selection schemes provide significantly more accurate estimation of model output uncertainty features than the existing schemes.     

Fuzzy relations for the analysis of traders’ preferences in an information market game

In this paper we focus on preference and decision data gathered during a computer-supported information market game in which 35 students participated during seven consecutive trading sessions. The participants’ individual preferences on the market shares are collected to calculate a collective preference ranking using the Borda social choice method. Comparing this preference ranking to the shares’ actual market ranking resulting from the participants’ trading, we find a statistically significant difference between both rankings. As the preferences established by market behavior cannot be adequately explained through a social choice rule, we propose an alternative explanation based on the herd behavior phenomenon where traders imitate the most successful trader in the market. Using a decision analysis technique based on fuzzy relations, we study the participants’ rankings of the best share in the market during 7 weeks and compare the most successful trader to the other traders. The results from our analysis show that a substantial number of traders is indeed following the market leader.     

Optimal threshold analysis of segmentation methods for identifying target customers

In CRM (Customer Relationship Management), the importance of a segmentation method for identifying good customers has been increasing. For evaluation of different segmentation methods, Accuracy often plays a key role. This indicator, however, cannot distinguish two types of errors. The purpose of this paper is to overcome this pitfall by introducing two different indicators: Recall and Precision. Assuming that a promotion is addressed exclusively to the selected target customers, the financial effectiveness of the underlying segmentation method is expressed as a function of Recall and Precision. An optimization problem is then formulated so as to maximize the financial measure by finding the optimal threshold level in terms of the severeness for estimating the target set. By introducing a functional form which represents correctness and mistakes about the target set, the unique optimal solution is derived explicitly. The proposed approach is validated by using real customer purchase data.     

Aggregation of utility-based individual preferences for group decision-making

Multi-attribute utility theory (MAUT) elicits an individual decision maker’s preferences for single attributes and develops a utility function by mathematics formulation to add up the preferences of the entire set of attributes when assessing alternatives. A common aggregation method of MAUT for group decisions is the simple additive weighting (SAW) method, which does not consider the different preferential levels and preferential ranks for individual decision makers’ assessments of alternatives in a decision group, and thus seems too intuitive in achieving the consensus and commitment for group decision aggregation. In this paper, the preferential differences denoting the preference degrees among different alternatives and preferential priorities denoting the favorite ranking of the alternatives for each decision maker are both considered and aggregated to construct the utility discriminative values for assessing alternatives in a decision group. A comparative analysis is performed to compare the proposed approach to the SAW model, and a satisfaction index is used to investigate the satisfaction levels of the final two resulting group decisions. In addition, a feedback interview is conducted to understand the subjective perceptions of decision makers while examining the results obtained from these two approaches for the second practical case. Both investigation results show that the proposed approach is able to achieve a more satisfying and agreeable group decision than that of the SAW method.     

A mispricing model of stocks under asymmetric information

We extend the theory of asymmetric information in mispricing models for stocks following geometric Brownian motion to constant relative risk averse investors. Mispricing follows a continuous mean-reverting Ornstein–Uhlenbeck process. Optimal portfolios and maximum expected log-linear utilities from terminal wealth for informed and uninformed investors are derived. We obtain analogous but more general results which nests those of Guasoni (2006) as a special case of the relative risk aversion approaching one.     

Estimation of a change-point in the mean function of functional data

The paper develops a comprehensive asymptotic theory for the estimation of a change-point in the mean function of functional observations. We consider both the case of a constant change size, and the case of a change whose size approaches zero, as the sample size tends to infinity. We show how the limit distribution of a suitably defined change-point estimator depends on the size and location of the change. The theoretical insights are confirmed by a simulation study which illustrates the behavior of the estimator in finite samples.     

关于有界函数导数的估计

主要讨论了有界函数的导数估计问题,得到三阶导数、四阶导数的准确估计式。     

Estimation for incomplete manpower data

Much attention has been paid to both non-parametric and parametric estimation for survival data with right censoring, particularly in the medical literature. In manpower planning the completed length of service until leaving is of great interest, and here also the data are right censored since people are still in service when data collection ends. However, it often occurs that the data are also left truncated since people are already in service at the beginning of data collection. These people have often been neglected both in estimation of the empirical distribution function and also in fitting particular parametric distributions. However, it is important to include them so as to use all the data, particularly when data are only present for a short period. The methods developed were applied to data for the completed length of service of both skilled and unskilled workers where the data were collected over a period of years. Using modified Kaplan-Meier estimation, applied to these data sets, empirical distribution functions were obtained. A number of parametric distributions were also fitted. The goodness of fit of these distributions as predictors of leavers and stayers over a given period was then tested using a chi-squared test.     

Families of relations modelling preferences under incomplete information

Let us consider a preferential information of type preference–indifference–incomparability (P, I, J), with additional information about differences in attractiveness between pairs of alternatives. The present paper offers a theoretical framework for the study of the “level of constraint” of this kind of partial preferential information. It suggests a number of structures as potential models being less demanding than the classical one in which differences in utilities can be used to represent the comparison of differences in attractiveness. The models are characterized in the more general context of families of non-complete preference structures, according to two different perspectives (called “semantico-numerical” and “matrix”). Both perspectives open the door to further practical applications connected with elicitation of the preferences of a decision maker.     

熵损失函数下两参数指数威布尔分布尺度参数的Bayes估计

本文给定一截尾样本,在熵损失函数下,研究了两参数指数威布尔分布尺度参数在先验伽玛分布下的Bayes估计,并给出了该参数的Bayes区间估计。     

Dependence Estimation and Visualization in Multivariate Extremes with Applications to Financial Data

We investigate extreme dependence in a multivariate setting with special emphasis on financial applications. We introduce a new dependence function which allows us to capture the complete extreme dependence structure and present a nonparametric estimation procedure. The new dependence function is compared with existing measures including the spectral measure and other devices measuring extreme dependence. We also apply our method to a financial data set of zero coupon swap rates and estimate the extreme dependence in the data.AMS 2000 Subject Classification. Primary—62G32, 62H12 Secondary—62E20     

状态概率的E-Bayes估计与多层Bayes估计

在文献[1]中提出了参数估计的一种方法--E-Bayes估计并给出了状态概率的E-Bayes估计的定义、E-Bayes估计公式、预测模型及其在证券投资中应用,本文在此基础上将给出状态概率的多层Bayes估计、状态概率的E-Bayes估计的性质--E-Bayes估计,多层Bayes估计的关系.最后,给出模拟算例.     

Copula函数在水文计算中的适用性分析

论述和探讨了Copula函数在水文计算中的应用及适用性,重点分析了常用的Archimedean CopuIa函数在描述水文变量尾部相关性方面的差异,以及相关系数的不确定性对各Copula函数分析结果的影响,讨论了Copula函数在相关性分析中出现的其他问题及注意事项.     

正则的正实部函数的导数估计

讨论了正则的正实部函数的导数估计问题,利用正实部函数的性质,得到三阶导数、四阶导数的准确估计式.