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  • 1.
    Brännäs, Kurt
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Economics.
    Rosenqvist, G.
    Semiparametric estimation of heterogeneous count data models1994In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860, Vol. 76, no 2, p. 247-258Article in journal (Refereed)
    Abstract [en]

    Unobserved heterogeneity in a stochastic model is usually represented by a mixing distribution. In this paper a semiparametric estimator is adapted to over-dispersed Poisson regression models. No assumptions are needed about the estimated mixing distribution. The parameters of included explanatory variables are estimated at the same time. The applicability and promising properties of the method are illustrated. Empirically the estimator is applied to a coffee purchase model and to a business travel frequency model subject to zero truncation. The approach is useful, e.g., in marketing research where socio-demographic variables as well as marketing instruments can be included as explanatory variables.

  • 2. Carlsson, Christer
    et al.
    Ehrenberg, Dieter
    Eklund, Patrik
    Department of Computer Science, Åbo Akademi University, Åbo, Finland.
    Fedrizzi, Mario
    Gustafsson, Patrik
    Lindholm, Paul
    Merkuryeva, Galina
    Riissanen, Tony
    Ventre, Aldo
    Consensus in distributed soft environments1992In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860, Vol. 61, no 1-2, p. 165-185Article in journal (Refereed)
    Abstract [en]

    This paper studies the problem of formalizing consensus reaching within a set of decision makers trying to find and agree upon a mutual decision. Decision makers produce their individual rankings, using their own pet decision schemas. Thus consensus reaching relies only on the aggregation of individual decisions rather than on individual decision procedures. The aggregation of the individual rankings is supported by an advising monitor which tries to contract the decision makers into a mutual decision through soft enforcement. Convergence to consensus then depends upon the decision makers' willingness to compromise. We use a topological approach to consensus where we can measure distances between decision makers. Within the approach we can also model the trade-off between a degree of consensus and a strength of majority.

  • 3.
    Eklund, Patrik
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Rusinowska, Agnieszka
    Radboud University, Faculty of Management Science, Nijmegen, The Netherlands and Warsaw School of Economics, Department of Mathematical Economics, Warsaw, Poland.
    De Swart, Harrie
    Tilburg University, Faculty of Philosophy, Tilburg, The Netherlands.
    Consensus reaching in committees2007In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860, Vol. 178, no 1, p. 185-193Article in journal (Refereed)
    Abstract [en]

    In this paper, we apply a consensus model to decision-making in committees that have to choose one or more alternatives from a set of alternatives. The model does not use a voting rule nor a set of winning coalitions. Every decision maker evaluates each alternative with respect to given criteria. The criteria may be of unequal importance to a decision maker. Decision makers may be advised by a chairman to adjust their preferences, i.e., to change their evaluation of some alternative(s) or/and the importance of the criteria, in order to obtain a better consensus. The consensus result should satisfy constraints concerning the consensus degree and the majority degree. A simple example is presented. (c) 2005 Elsevier B.V. All rights reserved.

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