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Inequality in provider and patient-initiated healthcare cancellations during Covid-19.
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Multinomial Logit Specification Tests
International Economic Review, 1985This paper considers tests of the multinomial logit (MNL) model against unspecified alternative models. The test we propose avoids both the asymptotic bias of the likelihood ratio test originally suggested by \textit{D. McFadden}, \textit{K. Train} and \textit{W. Tye} [An application of diagnostic tests for the independence from irrelevant alternatives
Hsiao, Cheng, Small, K.
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Transportation Research Record: Journal of the Transportation Research Board, 2009
Statisticians along with other scientists have made significant computational advances that enable the estimation of formerly complex statistical models. The Bayesian inference framework combined with Markov chain Monte Carlo estimation methods such as the Gibbs sampler enable the estimation of discrete choice models such as the multinomial logit (MNL)
Washington, Simon +3 more
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Statisticians along with other scientists have made significant computational advances that enable the estimation of formerly complex statistical models. The Bayesian inference framework combined with Markov chain Monte Carlo estimation methods such as the Gibbs sampler enable the estimation of discrete choice models such as the multinomial logit (MNL)
Washington, Simon +3 more
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Optimizing Multinomial Logit Profit Functions
Management Science, 1996The multinomial logit model is a standard approach for determining the probability of purchase in product line problems. When the purchase probabilities are multiplied by product contribution margins, the resulting profit function is generally nonconcave.
Ward Hanson, Kipp Martin
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Two-stage multinomial logit model
Expert Systems with Applications, 2011We suggest a two-stage multinomial logit model (TMLM) for incorporating and interpreting both the interaction and main effects in the model for multi-categorized responses. TMLM combines the robustness of multinomial logit model (MLM) with the good properties of decision tree (DT), which makes it possible to cluster homogeneous subjects and thus to ...
Jin-Hyung Kim, Mijung Kim
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CONDITIONAL AND MULTINOMIAL LOGITS AS BINARY LOGIT REGRESSIONS
Advances in Adaptive Data Analysis, 2011For a categorical variable with several outcomes, its dependence on the predictors is usually considered in the conditional or multinomial logit models. This work considers elasticity features of the binary and categorical logits and introduces the coefficients individual by observations.
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On the multinomial logit model
Physica A: Statistical Mechanics and its Applications, 1999Abstract We show that the Multinomial Logit model of bounded rational choice can be derived in the same way as the Gibbs–Boltzmann distribution in statistical physics. In particular, this model describes the behavior of a thermodynamic agent (which is an agent whose utility function depends on a very large number of variables) with respect to a ...
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