In the past, research in traffic assignment modeling has been directed primarily towards the deterministic model. Alternative, more behavioral principles were thought to be too demanding computationally. This paper presents two mathematical contributions that enable one to solve a logit assignment model with flow-dependent travel times at a reduced cost. First, a convergence test for Fisk's min...

Regularization methods consist of a category of commonly used techniques to obtain robust solutions to ill-posed problems such as nonparametric regression and classification. In recent years, methods of regularization have also been successfully introduced to address some other classical problems in statistics, e.g. model/variable selection and dimension reduction. This thesis is composed of tw...

The rise of online threshold fundraising offers a novel opportunity for the private provision of public and information goods. However, the underlying coordination problem may lead to inefficient allocations. Online mechanisms have two unique features that are not well-understood: (i) uncommonly large groups and (ii) little information regarding others’ incentives. Intuitively, increasing group...

‎This paper proposes a new form of optimization problem which is a two-level programming problem with infinitely many lower level constraints‎. ‎Firstly‎, ‎we consider some lower level constraint qualifications (CQs) for this problem‎. ‎Then‎, ‎under these CQs‎, ‎we derive formula for estimating the subdifferential of its valued function‎. ‎Finally‎, ‎we present some necessary optimality condit...

-This paper presents a financial distress prediction model that combines the approaches of neural network learning and logit analysis. This combination can retain the advantages and avoid the disadvantages of the two kinds of approaches in solving such a problem. The radial basis function network (RBFN) is adopted to construct the prediction model. The architecture of RBFN allows the grouping o...

To treat with social phenomena, statistical and mathematical physics provides a powerful and rigorous method [1], and several papers have studied the models of social phenomena based on stochastic processes. Many researchers in econometrics or biometrics have proposed that the discrete choice including binary analysis may be formulated as the AR (autoregressive), logit, and probit models [2]. I...

Currently, there is an important debate about the relative merits of models with discrete and continuous representations of consumer heterogeneity. A recent JMR study (Andrews, Ansari, and Currim 2002, AAC hereafter) compared metric conjoint analysis models with discrete and continuous representations of heterogeneity, finding no differences between the two models with respect to parameter reco...

As shown by Guimaraes, Figueiredo and Woodward (2003), a particular class of conditional logit models yield identical parameter estimates to a Poisson count data model. In Schmidheiny and Brülhart (2011), we have pointed out that the conditional logit model and the Poisson model can be seen as polar cases of a continuum of intermediate cases which emerge from a random utility nested logit model...