Browsing by Subject "multivariate count data"

Now showing 1 - 3 of 3

A Multivariate Hurdle Count Data Model with an Endogenous Multiple Discrete- Continuous Selection System
(2013-07-20) Bhat, Chandra R.; Dubey, Subodh K.; Sidharthan, Raghuprasad; Bhat, Prerna C.
This paper proposes a new econometric formulation and an associated estimation method for multivariate count data that are themselves observed conditional on a participation selection system that takes a multiple discrete-continuous model structure. This leads to a joint model system of a multivariate count and a multiple discrete-continuous selection system in a hurdletype model. The model is applied to analyze the participation and time investment of households in out-of-home activities by activity purpose, along with the frequency of participation in each selected activity. The results suggests that the number of episodes of activities as well as the time investment in those activities may be more of a lifestyle- and lifecycle-driven choice than one related to the availability of opportunities for activity participation.
A New Econometric Approach to Multivariate Count Data Modeling
(2013-01) Bhat, Chandra R.; Paleti, Rajesh; Castro, Marisol
In the current paper, we propose a modeling framework to explicitly link a count data model with an event type multinomial choice model. The proposed framework uses a multinomial probit kernel for the event type choice model and introduces unobserved heterogeneity in both the count and discrete choice components. Additionally, this paper establishes several new results regarding the distribution of the maximum of multivariate normally distributed variables, which form the basis to embed the multinomial probit model within a joint modeling system for multivariate count data. The model is applied for analyzing out-of-home non-work episodes pursued by workers, using data from the National Household Travel Survey.
On accommodating spatial dependence in bicycle and pedestrian injury counts by severity level
(Elsevier, 2013) Narayanamoorthy, Sriram; Paleti, Rajesh; Bhat, Chandra R.
This paper proposes a new spatial multivariate count model to jointly analyze the traffic crash- related counts of pedestrians and bicyclists by injury severity. The modeling framework is applied to predict injury counts at a Census tract level, based on crash data from Manhattan, New York. The results highlight the need to use a multivariate modeling system for the analysis of injury counts by road-user type and injury severity level, while also accommodating spatial dependence effects in injury counts.