Sentence examples for multinomial probit models from inspiring English sources

Empirically, we took double difference from the multinomial probit models to get the marginal effects of the interaction terms and thereby answered the hypotheses [33, 34].

This study utilizes a series of multinomial probit models and the 2007 National Apprenticeship Survey (NAS) to investigate the completion behaviour of individuals enrolled in apprenticeship programs.

Nested or cross-nested models are one approach to overcoming this issue [10], while multinomial probit models also do not suffer from this limitation.

Despite the higher computational costs of obtaining marginal effects from multinomial probit models compared to multinomial logit models, this study uses the former.

This paper proposes using hierarchical Bayes multinomial probit models to construct respondent uncertainty measures based on the utility difference and a concept called utility suppression, which is quantified by the derivative of the inverse Mills ratio.

Then, we generated potential alternative routes using a modified labeling approach, measured the route environment through environment auditing and secondary data, estimated two multinomial probit models, and compared the results between the two neighborhoods and between the alternative choice models.

We used multinomial probit model to test the first hypothesis.

The third column shows the estimated parameters for the multinomial probit model.

The results of the multinomial probit model are in Table 4.

On the basis of a multinomial probit model, we find that preferences are indeed susceptible to starting point bias.

Following Hausman and Wise (1978), a multinomial probit model is employed in order to avoid imposing this IIA assumption.h.h