We investigate the use of naive Bayesian classifiers for multinomial feature spaces and derive error estimates for these classifiers. The error analysis is done by developing a mathematical model to estimate the probability density functions for all multinomial likelihood functions describing different classes. We also develop a simplified method to account for the correlation between multinomi...

Transportation is a necessity if the day-to-day economic activities of the society must move on. There are different modes of informal transport available in Nigeria. This study evaluated the determinants of the modal choice of informal transport among commuters in North central city of Ilorin, Nigeria. The study used primary data generated through a structured questionnaire administered to 100...

In survey sampling the randomized response (RR) technique can be used to obtain truthful answers to sensitive questions. Although the individual answers are masked due to the RR technique, individual (sensitive) response rates can be estimated when observing multivariate response data. The beta-binomial model for binary RR data will be generalized to handle multivariate categorical RR data. The...

This paper proposes a new multinomial choice model which explicitly takes into account variation in choice sets across observations. The proposed varying choice set logit (VCL) model relaxes the independence of irrelevant alternatives assumption by allowing the individual random utility function to directly depend on choice set types, and can be applied to a variety of data in which some indivi...

mlogit is a package for R which enables the estimation the multinomial logit models with individual and/or alternative specific variables. The main extensions of the basic multinomial model (heteroscedastic, nested and random parameter models) are implemented.

mlogit is a package for R which enables the estimation of the multinomial logit models with individual and/or alternative specific variables. The main extensions of the basic multinomial model (heteroscedastic, nested and random parameter models) are implemented.