EM algorithms for nonparametric estimation of mixing distributions - Kenneth E. Train (Journal of Choice Modelling, 2008)
This paper describes and implements three computationally attractive procedures for nonparametric estimation of mixing distributions in discrete choice models. The procedures are specic types of the well known EM (Expectation-Maximization) algorithm based on three dierent ways of approximating the mixing distribution nonparametrically: (1) a discrete distribution with mass points and frequencies treated as parameters, (2) a discrete mixture of continuous distributions, with the moments and weight for each distribution treated as parameters, and (3) a discrete distribution with fixed mass points whose frequencies are treated as parameters. The methods are illustrated with a mixed logit model of households' choices among alternative-fueled vehicles.
Keywords: 330; mixed logit; probit; random coecients; EM algorithm; nonparametric estimation