Peter Goos - Academia.edu (original) (raw)

Papers by Peter Goos

In this paper we describe a problem that we define as the Budget-Constrained Minimum Cost Flow (B... more In this paper we describe a problem that we define as the Budget-Constrained Minimum Cost Flow (BCMCF) problem. The BCMCF problem is a natural extension of the well-known Minimum Cost Flow (MCF)[2] problem, with a fixed cost related to the use of arcs and a budget constraint. Network flow problems often become hard when extra constraints are added. Ahuja and Orlin [1] discuss the constrained maximum flow problem with a budget constraint related to the cost of flow. Beasley and Christofides [3] study the resource ...

Computers & Operations Research, 2012

Recently, Kessels et al. (2006) developed a way to produce Bayesian G- and V-optimal designs for ... more Recently, Kessels et al. (2006) developed a way to produce Bayesian G- and V-optimal designs for the multinomial logitmodel. These designs allow for precise response predictions which is the goal of conjoint choice experiments. The authors showed that the G- and V- optimality criteria outperform the D- and A-optimality criteria for prediction. However, their G- and V-optimal design algorithm is

Applied Stochastic Models in Business and Industry

Recently, the use of Bayesian optimal designs for discrete choice experiments, also called stated... more Recently, the use of Bayesian optimal designs for discrete choice experiments, also called stated choice experiments or conjoint choice experiments, has gained much attention, stimulating the development of Bayesian choice design algorithms. Characteristic for the Bayesian design strategy is that it incorporates the available information about people's preferences for various product attributes in the choice design. This is in contrast with the linear design methodology, which is also used in discrete choice design and which depends for any claims of optimality on the unrealistic assumption that people have no preference for any of the attribute levels. Although linear design principles have often been used to construct discrete choice experiments, we show using an extensive case study that the resulting utility-neutral optimal designs are not competitive with Bayesian optimal designs for estimation purposes. Copyright © 2011 John Wiley & Sons, Ltd.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research, 2015

Setting fair health care priorities counts among the most difficult ethical challenges our societ... more Setting fair health care priorities counts among the most difficult ethical challenges our societies are facing. To elicit through a discrete choice experiment the Belgian adult population's (18-75 years; N = 750) preferences for prioritizing health care and investigate whether these preferences are different for prevention versus cure. We used a Bayesian D-efficient design with partial profiles, which enables considering a large number of attributes and interaction effects. We included the following attributes: 1) type of intervention (cure vs. prevention), 2) effectiveness, 3) risk of adverse effects, 4) severity of illness, 5) link between the illness and patient's health-related lifestyle, 6) time span between intervention and effect, and 7) patient's age group. All attributes were statistically significant contributors to the social value of a health care program, with patient's lifestyle and age being the most influential ones. Interaction effects were found, s...

In optimum design theory designs are constructed that maximize the information on the unknown par... more In optimum design theory designs are constructed that maximize the information on the unknown parameters of the response function. The major part deals with designs optimal for response function estimation under the assumption of homoscedasticity. In this paper, optimal designs are derived in case of multiplicative heteroscedasticity for either response function estimation or response and variance function estimation by using

ABSTRACT We propose an efficient individually adapted sequential Bayesian approach for constructi... more ABSTRACT We propose an efficient individually adapted sequential Bayesian approach for constructing conjoint-choice experiments, which uses Bayesian updating, a Bayesian analysis, and a Bayesian design criterion to generate a conjoint-choice design for each individual respondent based on the previous answers of that particular respondent. The proposed design approach is compared with three non-adaptive design approaches, two aggregate-customization approaches (based on the conditional logit model and on a mixed logit model), and the (nearly) orthogonal design approach, under various degrees of response accuracy and consumer heterogeneity. A simulation study shows that the individually adapted sequential Bayesian conjoint-choice designs perform better than the benchmark approaches in all scenarios we investigated in terms of the efficient estimation of individual-level part-worths and the prediction of individual choices. In the presence of high consumer heterogeneity, the improvements are impressive. The new method also performs well when the response accuracy is low, in contrast with the recently proposed adaptive polyhedral approach. Furthermore, the new methodology yields precise population-level parameter estimates, even though the design criterion focuses on the individual-level parameters.

SSRN Electronic Journal, 2000

The authors propose a fast and efficient algorithm for constructing D-optimal conjoint choice des... more The authors propose a fast and efficient algorithm for constructing D-optimal conjoint choice designs for mixed logit models in the presence of respondent heterogeneity. With this new algorithm, the construction of semi-Bayesian D-optimal mixed logit designs with large numbers of attributes and attribute levels becomes practically feasible. The results from the comparison of eight designs (ranging from the simple locally D-optimal design for the multinomial logit model and the nearly orthogonal design generated by Sawtooth (CBC) to the complex semi-Bayesian mixed logit design) across wide ranges of parameter values show that the semi-Bayesian mixed logit approach outperforms the competing designs not only in terms of estimation efficiency but also in terms of prediction accuracy. In particular, it was found that semi-Bayesian mixed logit designs constructed with large heterogeneity parameters are most robust against the misspecification of the values for the mean of the individual l...

In a classical conjoint choice experiment, respondents choose one profile from each choice set th... more In a classical conjoint choice experiment, respondents choose one profile from each choice set that has to be evaluated. However, in real life the respondent does not always make a choice: often he/she does not prefer any of the alternatives offered. Therefore, including a no-choice option in a choice set makes a conjoint choice experiment more realistic. In the literature three different models are used to analyze the results of a conjoint choice experiment with a no-choice option: the no-choice multinomial logit model, the extended no-choice multinomial logit model and the nested no-choice multinomial logit model. We develop optimal designs for each of these models using the D-optimality criterion and the modified Fedorov algorithm. We compare the optimal designs with a reference design that was constructed ignoring the no-choice option and we discuss the impact of the different designs and models on the precision of estimation and the predictive accuracy based on a simulation study.

Journal of the Royal Statistical Society: Series C (Applied Statistics), 2007