Abstract
This paper reports on the operational characteristics of maximum score estimation of a linear model from binary response data. A series of previous articles have shown that in theory the maximum score method makes possible binary response analysis under very weak distributional assumptions. Here, we present evidence on the properties of maximum score estimation in practice. After reviewing the known asymptotic theory of maximum score estimation, the paper describes an algorithm for maximum score estimation and characterizes its performance. Then findings from a Monte Carlo study comparing maximum score and logit maximum likelihood estimation are reported. Finally, the accuracy of bootstrap estimation of maximum score root mean square errors is evaluated.
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