A classical-like combinatorial interpretation of the Fibonomial coefficients is proposed following [1,2]. It is considered to be in the spirit classicalcombinatorial interpretation like binomial Newton and Gauss q-binomial coefficients or Stirling number of both kinds are. (See ref. [3,4] and refs. given therein). It also concerns choices. Choices of specific sub-posets from a non-tree poset sp...

Random utility models have been widely used in many diverse fields. Considering utility as a random variable opened many new analytical doors to researchers in explaining behavioral phenomena. Introducing and incorporating the random error term into the utility function had several reasons, including accounting for unobserved variables. This paper incorporates fuzziness into random utility mode...

DCM (Discrete Choice Models) is a package for estimating a class of discrete choice models. DCM is a class written in Ox, that implements a wide range of discrete choice models including standard binary response models, with notable extensions including conditional mixed logit, mixed probit, multinomial probit, and random coefficient ordered choice models. The current version can handle both cr...

We develop new methods for conducting a finite sample, likelihood-based analysis of the multinomial probit model. Using a variant of the Gibbs sampler, an algorithm is developed to draw from the exact posterior of the multinomial probit model with correlated errors. This approach avoids direct evaluation of the likelihood and, thus, avoids the problems associated with calculating choice probabi...

We present a structural model of political advertising in equilibrium. Candidates choose advertising across media markets in order to maximize the probability of winning the national election. The voter model takes the form of an aggregate random coefficients discrete choice model in which advertising affects a voter's incentive to vote for either candidate or not to vote at all. We estimate th...

Covariate-adjusted regression was recently proposed for situations where both predictors and response in a regression model are not directly observed, but are observed after being contaminated by unknown functions of a common observable covariate. The method has been appealing because of its flexibility in targeting the regression coefficients under different forms of distortion. We extend this...