This paper introduces a variant of random utility choice models based on mixed probability density functions, hence the adopted moniker "k-Mix models." Mixed pdf's contain components with the usual continuous density function specifications that underlie common choice models (e.g. MNL, GEV,...

This paper introduces a new member of the generalized extreme value (GEV) family of discrete choice models that directly incorporates choice set generation modeling into the specification via the GEV generating function. Though still a two-stage model of choice set generation and choice, the...

This paper proposes a behavioral interpretation of the choice set generation process that is useful for structuring and specifying discrete models that incorporate this stage of choice. The key concept is the notion of random constraints, which is operationalized in the form of probabilistic...

This research proposes an extension to the traditional compensatory utility maximization framework which has guided most theoretical and statistical work in choice modeling applications, including those in transportation demand estimation work. Attribute cutoffs are incorporated into the...

This paper examines the properties and empirically tests a model of discrete choice which incorporates probabilistic choice set generation. Denominated the Parametrized Logit Captivity (PLC) model, it is a generalization of the well-known "dogit" specification. The PLC model is shown to be...

We propose as a practice that researchers investigate other sources of heterogeneity besides taste variation in the search for parsimonious recommendations about new product development and marketing program design. Some evidence exists that regularities in choice processes may be more common...