The Generalized Extreme Value Model was developed by McFadden for the case with discrete choice sets. The present paper extends this model to cases with both discrete and continuous choice sets and choice sets that are unobservable relative to the analyst. We also propose behavioral assumptions that justify random utility functions (processes) that have a max stable structure i.e., utility processes where the finite dimensional distributions are of the multivariate extreme value type. Finally we derive non-parametrically testable implications for the choice probabilities in the continuous case
Multivariate extreme-value analysis is concerned with the extremes in a multivariate random sample, ...
The classical multivariate extreme-value theory concerns the modelling of extremes in a multivariate...
When sufficiently small perturbations of parameters preserve strict preference for one alternative o...
The Generalized Extreme Value Model was developed by McFadden for the case with discrete choice sets...
Abstract. Consider a finite set of alternatives, and an associated collection of random variables re...
Since the pioneering work of McFadden (1974), discrete choice random-utility models have become work...
In this paper, we explore estimable Generalized Extreme Value (GEV) spatial discrete choice models. ...
The Generalized Extreme Value Model (GEV) was developed by McFadden (cf. McFadden, 1981) with the pu...
Random choice theory has traditionally modeled choices over a -nite number of options. This thesis g...
In the field of spatial extremes, stochastic processes with upper semicontinuous (usc) trajectories ...
This paper considers discrete choice, with choice probabilities coming from maximization of preferen...
This paper formally derives the class of multiple discrete-continuous generalized extreme value (MDC...
The paper extends the generalized extreme value random utility model (McFadden, 1981) to the case wh...
An important problem in the analysis of intertemporal choice processes is to separate the effect of ...
Multivariate extreme value theory has proven useful for modeling multivariate data in fields such as...
Multivariate extreme-value analysis is concerned with the extremes in a multivariate random sample, ...
The classical multivariate extreme-value theory concerns the modelling of extremes in a multivariate...
When sufficiently small perturbations of parameters preserve strict preference for one alternative o...
The Generalized Extreme Value Model was developed by McFadden for the case with discrete choice sets...
Abstract. Consider a finite set of alternatives, and an associated collection of random variables re...
Since the pioneering work of McFadden (1974), discrete choice random-utility models have become work...
In this paper, we explore estimable Generalized Extreme Value (GEV) spatial discrete choice models. ...
The Generalized Extreme Value Model (GEV) was developed by McFadden (cf. McFadden, 1981) with the pu...
Random choice theory has traditionally modeled choices over a -nite number of options. This thesis g...
In the field of spatial extremes, stochastic processes with upper semicontinuous (usc) trajectories ...
This paper considers discrete choice, with choice probabilities coming from maximization of preferen...
This paper formally derives the class of multiple discrete-continuous generalized extreme value (MDC...
The paper extends the generalized extreme value random utility model (McFadden, 1981) to the case wh...
An important problem in the analysis of intertemporal choice processes is to separate the effect of ...
Multivariate extreme value theory has proven useful for modeling multivariate data in fields such as...
Multivariate extreme-value analysis is concerned with the extremes in a multivariate random sample, ...
The classical multivariate extreme-value theory concerns the modelling of extremes in a multivariate...
When sufficiently small perturbations of parameters preserve strict preference for one alternative o...