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英文文献：Estimating Mixed Logit Recreation Demand Models With Large Choice Sets-估计有大量选择集的混合Logit娱乐需求模型
英文文献作者：Domanski, Adam
英文文献摘要：
Discrete choice models are widely used in studies of recreation demand. They have proven valuable when modeling situations where decision makers face large choice sets and site substitution is important. However, when the choice set faced by the individual becomes very large (on the order of hundreds or thousands of alternatives), computational limitations make estimation with the full choice set intractable. Sampling of alternatives in a conditional logit framework is an effective method to limit computational burdens while still producing consistent estimates. This method is allowed by the existence of the independence of irrelevant alternatives (IIA) assumption. More advanced mixed logit models account for unobserved preference heterogeneity and overcome the behavioral limitations of the IIA assumption, however in doing so, prohibit sampling of alternatives. A method is developed where a latent class (finite mixture) model is estimated via the expectations-maximization algorithm and in doing so, allows consistent sampling of alternatives in a mixed logit model. The method is tested and applied to a recreational demand Wisconsin fishing survey.
离散选择模型被广泛应用于休闲需求的研究中。当决策者面对大量选择集和站点替代很重要时，它们已经被证明是有价值的。然而，当个体面临的选择集变得非常大时(成百上千个选项的数量级)，计算的局限性使得用全选择集进行估计变得非常棘手。在条件logit框架中抽样选择是一种有效的方法来限制计算负担，同时仍然产生一致的估计。该方法是允许存在的独立性的不相关选项(IIA)假设。更先进的混合logit模型解释了未观察到的偏好异质性，并克服了IIA假设的行为限制，但在这样做时，禁止了选择的抽样。提出了一种方法，通过期望最大化算法估计一个潜在类(有限混合)模型，并允许在混合logit模型中对备选方案进行一致的抽样。该方法已在威斯康星州渔业休闲需求调查中得到验证和应用。
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