7 research outputs found
Applicability of sustainable urban drainage systems: an evaluation by multi-criteria analysis
Spatial Estimation Techniques for Precipitation Analysis — Application to a Region in India
Progressive stress accelerated life tests under finite mixture models
Finite mixtures, Accelerated life tests, Progressive stress, Cumulative exposure model, Type-I censoring, Maximum likelihood estimation, Local Fisher information matrix, Simulation,
Generalized processing tree models: Jointly modeling discrete and continuous variables
Multinomial processing tree models assume that discrete cognitive states determine observed response frequencies. Generalized processing tree (GPT) models extend this conceptual framework to continuous variables such as response times, process-tracing measures, or neurophysiological variables. GPT models assume finite-mixture distributions, with weights determined by a processing tree structure, and continuous components modeled by parameterized distributions such as Gaussians with separate or shared parameters across states. We discuss identifiability, parameter estimation, model testing, a modeling syntax, and the improved precision of GPT estimates. Finally, a GPT version of the feature comparison model of semantic categorization is applied to computer-mouse trajectories