When estimating random coefficients models from choice data, decisions relating to the multivariate density function assumed to describe preference heterogeneity across the population raise questions about stochastic (in)dependence between preference dimensions, uni-vs. multi-modality, potential point masses, bounds and/or constraints on support regions, among other concerns. Parametric representations of population distributions have generally implied uncomfortable compromises to achieve estimation tractability. It would seem preferable to sidestep such issues by estimating individual preferences in a distribution-free manner, but this freedom of form implies a large number of parameters since we lose the parsimony enabled by parametric densities and must deal directly with estimation of individual decision maker preferences. I propose a hybrid distribution-free estimator for individual parameter logit models that uses a genetic algorithm as first stage, the solution from which becomes a starting point for a gradient-based search to obtain the final posterior maximum likelihood estimates of individual preferences. This estimator is described in detail, its parameter recovery capability is tested with Monte Carlo data generation simulations, and a case study is developed in some detail to illustrate its use in policy analysis. The estimator can be applied to both stated and revealed preference data, requiring only that sufficient choice replications be available for individual observation units consistent with extant estimation methods. Computational experience shows the estimator to require CPU times comparable to extant simulation-based estimation methods, meaning that its use is practical for the exploration of the parameter space through multiple trials.

当从选择数据估计随机系数模型时，与假定描述总体偏好异质性的多元密度函数相关的决策提出了关于偏好维度之间的随机（不）依赖性的问题，uni-vs。多模态、潜在的点质量、支持区域的边界和/或约束，以及其他问题。人口分布的参数表示通常意味着为了实现估计的易处理性而做出令人不安的妥协。通过以无分布的方式估计个人偏好来回避这些问题似乎更可取，但这种形式自由意味着大量参数，因为我们失去了参数密度带来的简约性，必须直接处理个人决策者偏好的估计. 我提出了一种用于个体参数 logit 模型的混合无分布估计器，该模型使用遗传算法作为第一阶段，解决方案成为基于梯度的搜索的起点，以获得个体偏好的最终后验最大似然估计。详细描述了该估计器，其参数恢复能力通过蒙特卡罗数据生成模拟进行了测试，并详细开发了一个案例研究来说明其在政策分析中的用途。估计器可以应用于陈述的和显示的偏好数据，只需要与现存估计方法一致的单个观察单元有足够的选择复制。计算经验表明，估算器需要的 CPU 时间与现有的基于模拟的估算方法相当，