a hybrid model simply combines MNL and conditional logit by adding the two together in the deterministic part of the model. 刘勇，王高畅2009 离散选择模型在零售中的应用. ——— 基于MNL 、MNP 和Mixed Logit 的比较

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  离散选择模型(研究1025)

  
  

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  第八章 离散选择模型

  
  

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  二元离散选择模型

  
  

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  SP调查在离散选择模型中...

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 In an MNL model, a predictor like religion is fixed across the choices, but the effect of the predictor is different for each choice. So religion may be an important consideration of an individual when they evaluate the Republican party, but may be less important when they evaluate the Democrats or Greens. In order to consider both types of independent variables at once, statisticians have 6 developed a hybrid logit model. Under a hybrid model the latent variables take the form Uij = βjxi + γzij + εij . (3) In other words, a hybrid model simply combines MNL and conditional logit by adding the two together in the deterministic part of the model. 

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Submitted to Management Science manuscript MS-MS-12-00426.R2 On Theoretical and Empirical Aspects of Marginal Distribution Choice Models (Authors’ names blinded for peer review) In this paper, we study the properties of a recently proposed class of semiparametric discrete choice models (referred to as the Marginal Distribution Model), by optimizing over a family of joint error distributions with prescribed marginal distributions. Surprisingly, the choice probabilities arising from the family of Generalized Extreme Value models can be obtained from this approach, despite the difference in assumptions on the underlying probability distributions. We use this connection to develop flexible and general choice models to incorporate respondent and product/attribute level heterogeneity in both partworths and scale parameters in the choice model. Furthermore, the extremal distributions obtained from the MDM can be used to approximate the Fisher’s Information Matrix to obtain reliable standard error estimates of the partworth parameters, without having to bootstrap the method. We use various simulated and empirical datasets to test the performance of this approach. We evaluate the performance against the classical Multinomial Logit, Mixed Logit, and a machine learning approach developed by Evgeniou et al. (13) (for partworth heterogeneity). Our numerical results indicate that MDM provides a practical semi-parametric alternative to choice modeling

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 5.7.1 Informal Tests A variety of informal tests can be applied to an estimated model. These tests are designed to assess the reasonableness of the implications of estimated parameters. The most common tests concern: • The sign of parameters (do the associated variables have a positive or negative effect on the alternatives with which they are associated?), • The difference (positive or negative) within sets of alternative specific variables (does the inclusion of this variable have a more or less positive effect on one alternative relative to another?) and • The ratio of pairs of parameters (is the ratio between the parameters of the correct sign and in a reasonable range?). 5.7.1.1 Signs of Parameters The most basic test of the estimation results is to examine the signs of the estimated parameters with theory, intuition and judgment regarding the expected impact of the corresponding variables. The estimated coefficients on the travel time and cost variables in Table 5-2 are negative, as expected, implying that the utility of a mode decreases as the mode becomes slower and/or more expensive. This, in turn, will reduce the choice probability of the corresponding mode. 5.7.1.2 Differences in Alternative Specific Variable Parameters across Alternatives We often have expectations about the impact of decision-maker characteristics on different alternatives. For example, when analyzing mode choice, we expect a number of variables to be more positive for automobile alternatives, especially Drive Alone, than for other alternatives. These include income, automobile ownership, home ownership, single family dwelling unit, etc. Since DA is the reference alternative in these models, we expect negative parameters on all alternative specific income variables, with small values for the shared ride alternatives and larger values for other alternatives, to reflect our intuition that increasing income will be associated with decreased preference for all other alternatives relative to drive alone. All the estimated