In economics, discrete choice models, or qualitative choice models, describe, explain, and predict choices between two or more discrete alternatives,...

Dynamic discrete choice (DDC) models, also known as discrete choice models of dynamic programming, model an agent's choices over discrete options that...

in a particular context or contexts. Typically, it attempts to use discrete choices (A over B; B over A, B & C) in order to infer positions of the items...

variables are quantitative, economic models are classified as discrete or continuous choice model; according to the model's intended purpose/function, it...

pioneered an approach that used only a choice task which became the basis of choice-based conjoint analysis and discrete choice analysis. This stated preference...

higher education, structural estimation of dynamic discrete choice models, and college major choice, having written survey papers on each topic. He has...

between continuous variables and discrete variables. (Discrete variables referring to more than two possible choices are typically coded using dummy variables...

Discrete mathematics is the study of mathematical structures that can be considered "discrete" (in a way analogous to discrete variables, having a bijection...

observation i to category k. In discrete choice theory, where observations represent people and outcomes represent choices, the score is considered the utility...

regression models are essentially the same as binary choice models, one type of discrete choice model: the primary difference is in the theoretical motivation...

project that performs the maximum likelihood estimation of parametric discrete choice models. It is working within the framework of Pandas, a Python data...

one of the founding fathers of the structural estimation of dynamic discrete choice models and the developer of the nested fixed point (NFXP) maximum likelihood...

linear model Generalized linear model Vector generalized linear model Discrete choice Binomial regression Binary regression Logistic regression Multinomial...

prize was "for his development of theory and methods for analyzing discrete choice". He is the Presidential Professor of Health Economics at the University...

Léopold; Zelenyuk, Valentin (2017). "Nonparametric estimation of dynamic discrete choice models for time series data" (PDF). Computational Statistics & Data...

Nathan (2005). Loss Functions for Preference Levels: Regression with Discrete Ordered Labels (PDF). Proc. IJCAI Multidisciplinary Workshop on Advances...

latent variable formulation of the multinomial logit model — common in discrete choice theory — the errors of the latent variables follow a Gumbel distribution...

useful in economics (and other social sciences) because the choice probabilities in discrete choice models generally have this form. The GHK algorithm is now...

{T}}A+\Gamma ^{\mathsf {T}}\Gamma .} Typically discrete linear ill-conditioned problems result from discretization of integral equations, and one can formulate...

In mathematics, the discrete Fourier transform (DFT) converts a finite sequence of equally-spaced samples of a function into a same-length sequence of...

implementation is easier. Sigmoid function, inverse of the logit function Discrete choice on binary logit, multinomial logit, conditional logit, nested logit...

linear model Generalized linear model Vector generalized linear model Discrete choice Binomial regression Binary regression Logistic regression Multinomial...

analyzing discrete choice" University of Minnesota University of California, Berkeley Massachusetts Institute of Technology Discrete choice models 2001...

trip making, while a discrete choice approach brings those variables inside the utility or impedance function. Discrete choice models require more information...

linear model Generalized linear model Vector generalized linear model Discrete choice Binomial regression Binary regression Logistic regression Multinomial...

linear model Generalized linear model Vector generalized linear model Discrete choice Binomial regression Binary regression Logistic regression Multinomial...

Mixed logit is a fully general statistical model for examining discrete choices. It overcomes three important limitations of the standard logit model by...

McCullagh. For example, if one question on a survey is to be answered by a choice among "poor", "fair", "good", "very good" and "excellent", and the purpose...

transformation may distribute the errors in a Gaussian fashion, so the choice to perform a nonlinear transformation must be informed by modeling considerations...

Nando de Freitas, Abhijeet Ghosh: Active Preference Learning with Discrete Choice Data. Advances in Neural Information Processing Systems: 409-416 (2007)...