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Estimating a Destination-Choice Model from a Choice-based Sample with Limited Information

机译：从信息有限的基于选择的样本中估计目的地选择模型

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In most applications of multinomial logit and other probabilistic discrete-choice models, the estimation data set is either a simple random sample of the population of interest or an exogenously stratified sample. Often, however, it is cheaper and easier to sample individuals while they are carrying out the chosen activity of concern. This produces a choice-based sample, which presents important problems of estimation and inference. This paper is concerned with estimation of destination-choice models from choice-based samples when neither the aggregate market shares of alternatives nor the probability distribution of explanatory variables in the population is known. The method of Cosslett (1981) for estimating multinomial logit models from such data is summarized, and the limitations on information about choice behavior that can be recovered from the sample are explained. An empirical model of pharmacy choice in the Namur, Belgium, area is presented. It is shown that useful and important information about destination-choice behavior can be obtained from a choice-based sample, even without knowledge of aggregate market shares and the probability distribution of explanatory variables.
机译：在多项式logit和其他概率离散选择模型的大多数应用中，估计数据集要么是目标群体的简单随机样本，要么是外生分层样本。但是，通常在进行个人所关注的活动时对个人进行采样会更便宜，更容易。这产生了一个基于选择的样本，它带来了估计和推断的重要问题。当既不知道替代方案的总市场份额，也不总体中解释变量的概率分布是已知的时，本文涉及从基于选择的样本中估计目的地选择模型。总结了Cosslett（1981）从此类数据估计多项式logit模型的方法，并解释了可以从样本中恢复的关于选择行为的信息的局限性。介绍了比利时那慕尔地区药房选择的经验模型。结果表明，即使不了解总体市场份额和解释变量的概率分布，也可以从基于选择的样本中获得有关目的地选择行为的有用而重要的信息。

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