This study proposes a simple and practical Composite Marginal Likelihood (CML) inference approach to estimate ordered-response discrete choice models with flexible copula-based spatial dependence structures across observational units. The approach is applicable to data sets of any size, provides standard error estimates for all parameters, and does not require any simulation machinery. The combined copula CML approach proposed here should be appealing for general multivariate modeling contexts because it is simple and flexible, and is easy to implement. The ability of the CML approachto recover the parameters of a spatially ordered process is evaluated using a simulation study, which clearly points to the effectiveness of the approach. In addition, the combined copula CML approach is applied to study the daily episode frequency of teenagers physically active and physicallyinactive recreational activity participation, a subject of considerable interest in the transportation, sociology, and adolescence development fields. The data for the analysis are drawn from the 2000 San Francisco Bay Area Survey. The results highlight the value of the copula approach that separates the univariate marginal distribution form from the multivariate dependence structure, as well as underscore the need to consider spatial effects in recreational activity participation. The variable effects indicate that parents physical activity participation constitutes the most important factor influencing teenagers physical activity participation levels. Thus, an effective way to increase active recreation among teenagers may beto direct physical activity benefit-related information and education campaigns toward parents, perhaps at special physical education sessions at the schools of teenagers. (A) Reprinted with permission from Elsevier.
Samenvatting