Loading...
Please wait, while we are loading the content...
Similar Documents
Estimation of the mixed logit likelihood function by randomized quasi-monte carlo (2011).
| Content Provider | CiteSeerX |
|---|---|
| Author | Munger, D. L.’ecuyer, P. Bastin, F. Cirillo, C. Tuffin, B. |
| Abstract | We examine the effectiveness of randomized quasi-Monte Carlo (RQMC) techniques to estimate the integrals that express the discrete choice probabilities in a mixed logit model, for which no closed form formula is available. These models are used extensively in travel behavior research. We consider popular RQMC constructions such as randomized Sobol’, Faure, and Halton points, but our main emphasis is on randomly-shifted lattice rules, for which we study how to select the parameters as a function of the considered class of integrands. We compare the effectiveness of all these methods and of standard Monte Carlo (MC) to reduce both the variance and the bias when estimating the log-likelihood function at a given parameter value. In our numerical experiments, randomized lattice rules (with carefully selected parameters) and digital nets are the best performers and they reduce the bias as much as the variance. With panel data, in our examples, the performance of all RQMC methods degrades rapidly when we simultaneously increase the dimension and the number of observations per individual. |
| File Format | |
| Publisher Date | 2011-01-01 |
| Access Restriction | Open |
| Subject Keyword | Randomized Quasi-monte Carlo Mixed Logit Likelihood Function Popular Rqmc Construction Mixed Logit Model Numerical Experiment Travel Behavior Research Form Formula Lattice Rule Digital Net Main Emphasis Rqmc Method Degrades Log-likelihood Function Discrete Choice Probability Randomly-shifted Lattice Rule Panel Data Standard Monte Carlo Halton Point Parameter Value Randomized Sobol |
| Content Type | Text |
