Semiparametric estimation of (constrained) ultrametric trees

This paper introduces a general, formal treatment of dynamic constraints, i.e., constraints on the state changes that are allowed in a given state space. Such dynamic constraints can be seen as representations of "real world" constraints in a managerial context. The notions of transition, reversible and irreversible transition, and transition relation will be introduced. The link with Kripke models (for modal logics) is also made explicit. Several (subtle) examples of dynamic constraints will be given. Some important classes of dynamic constraints in a database context will be identified, e.g. various forms of cumulativity, non-decreasing values, constraints on initial and final values, life cycles, changing life cycles, and transition and constant dependencies. Several properties of these dependencies will be treated. For instance, it turns out that functional dependencies can be considered as "degenerated" transition dependencies. Also, the distinction between primary keys and alternate keys is reexamined, from a dynamic point of view.

A new stochastic path-length tree methodology for constructing communication networks

Network analysis has become a popular method for identifying the communication structure in a system where positional and relational aspects are important. In this paper, a maximum likelihood based methodology is presented that allows for the analysis of binary sociometric data. This methodology provides a network representation via estimated path-length or additive trees that indicate the distance between all pairs of members. The methodology is distinguished from traditional hierarchical clustering based procedures by its direct consideration of the asymmetry in a typical communication process, the simultaneous representation of structural characteristics (e.g., clique membership, clique cohesiveness), and the identification of the specialized communication roles of each member (e.g., opinion leader, liaison). A penalty function algorithm is developed and its performance is investigated via a Monte Carlo analysis with synthetic data. An application examining information flows among managers is presented. Finally, directions for future research are suggested.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/29291/1/0000352.pd

Accommodating the effects of brand unfamiliarity in the multidimensional scaling of preference data

This paper presents a multidimensional scaling (MDS) methodology (vector model) for the spatial analysis of preference data that explicitly models the effects of unfamiliarity on evoked preferences. Our objective is to derive a joint space map of brand locations and consumer preference vectors that is free from potential distortion resulting from the analysis of preference data confounded with the effects of consumer-specific brand unfamiliarity. An application based on preference and familiarity ratings for ten luxury car models collected from 240 consumers who intended to buy a luxury car within a designated time frame is presented. The results are compared with those obtained from MDPREF, a popular metric vector MDS model used for the scaling of preference data. In particular, we find that the consumer preference vectors obtained from the proposed methodology are substantially different in orientation from those estimated by the MDPREF model. The implications of the methodology are discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47094/1/11002_2004_Article_BF00994083.pd

Selecting Competitive Tactics: Try a Strategy Map

When developing strategy, a manager considers how various tactics will affect short-term performance and broad strategic direction. The skilled manager keeps those factors in mind and, simultaneously, gauges what the competition is up to. The authors describe a mapping technique that will help managers to do just that. Not only does the technique provide an accessible measure of relative competitive standing, but it also allows managers to simulate tactical changes and analyze their probably impact on business performance

Market Segment Derivation and Profiling Via a Finite Mixture Model Framework

The Marketing literature has shown how difficult it is to profile market segments derived with finite mixture models, especially using traditional descriptor variables (e.g., demographics). Such profiling is critical for the proper implementation of segmentation strategy. We propose a new finite mixture modelling approach that provides a variety of model specifications to address this segmentation dilemma. Our proposed approach allows for a large number of nested models (special cases) and associated tests of (local) independence to distinguish amongst them. A commercial application to customer satisfaction is provided where a variety of different model specifications are tested and compared.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46979/1/11002_2004_Article_399784.pd

A parametric procedure for ultrametric tree estimation from conditional rank order proximity data

The psychometric and classification literatures have illustrated the fact that a wide class of discrete or network models (e.g., hierarchical or ultrametric trees) for the analysis of ordinal proximity data are plagued by potential degenerate solutions if estimated using traditional nonmetric procedures (i.e., procedures which optimize a STRESS-based criteria of fit and whose solutions are invariant under a monotone transformation of the input data). This paper proposes a new parametric, maximum likelihood based procedure for estimating ultrametric trees for the analysis of conditional rank order proximity data. We present the technical aspects of the model and the estimation algorithm. Some preliminary Monte Carlo results are discussed. A consumer psychology application is provided examining the similarity of fifteen types of snack/breakfast items. Finally, some directions for future research are provided.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45756/1/11336_2005_Article_BF02294429.pd

CATSCALE: A stochastic multidimensional scaling methodology for the spatial analysis of sorting data and the study of stimulus categorization

Sorting tasks have provided researchers with valuable alternatives to traditional paired-comparison similarity judgments. They are particularly well-suited to studies involving large stimulus sets where exhaustive paired-comparison judgments become infeasible, especially in psychological studies investigating stimulus categorization. This paper presents a new stochastic multidimensional scaling procedure called CATSCALE for the analysis of three-way sorting data (as typically collected in categorization studies). We briefly present a review of the role of sorting tasks, especially in categorization studies, as well as a description of several traditional modes of analysis. The CATSCALE model and maximum likelihood based estimation procedure are described. An application of CATSCALE is presented with respect to a behavioral accounting study examining auditor's categorization processes with respect to various types of errors found in typical financial statements.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/31400/1/0000315.pd