Improved Algorithms for Estimating Choice Probabilities in the Multinomial Probit Model
Improved Algorithms for Estimating Choice Probabilities in the Multinomial Probit Model
复制标题
多项概率模型中估计选择概率的改进算法
作者：
M. Langdon
在应用多项概率模型的问题之一一直是缺乏一个令人满意的算法来估计选择概率。目前使用的“Clark”方法满足大部分要求，但有时会出现令人无法接受的大误差。本文介绍了一种新的算法，在一些额外的复杂性为代价，产生估计的选择概率，从来没有大的错误。这种“分离的拆分”方法与Clark方法有许多相似之处，但采用了不同类型的递归结构。对中间效用分布的高阶矩的研究，使得有三种选择方案的问题的可能误差大小有一个上限。一个新的“相关logit”选择模型也已经开发出来，它使用相同的递归结构的分离分裂。多项式Probit模型，但假设协方差矩阵不变。它已被发现给一个令人惊讶的好近似的exa.
One of the problems in the application of multinomial probit models has been the lack of a satisfactory algorithm for estimating choice probabilities. The currently used “Clark” method meets most of the requirements, but is subject to errors which are occasionally unacceptably large. This paper describes a new algorithm which, at the expense of some additional complexity, produces estimates of choice probability which never have large errors. This “separated split” method has many similarities to the Clark method, but employs a different type of recursive structure. Examination of higher moments of intermediate utility distributions has enabled an upper limit to be set on the possible size of error for problems with three choice alternatives. A new “correlated logit” choice model has also been developed, which uses the same recursive structure as the separated split. Multinomial Probit model, but assumes an invariant covariance matrix. It has been found to give a surprisingly good approximation to the exa...