We develop a new class of spatial voting models for binary preference data that can accommodate both monotonic and non-monotonic response functions, and are more flexible than alternative "unfolding" models previously introduced in the literature. We then use these models to estimate revealed preferences for legislators in the U.S. House of Representatives and justices on the U.S. Supreme Court. The results from these applications indicate that the new models provide superior complexity-adjusted performance to various alternatives and also that the additional flexibility leads to preferences' estimates that are closer matches to the perceived ideological positions of legislators and justices.

翻译：我们针对二元偏好数据开发了一类新的空间投票模型，该模型可同时容纳单调与非单调响应函数，且相较于文献中先前引入的其他“展开”模型具有更强的灵活性。随后，我们运用这些模型对美国众议院议员及美国最高法院大法官的显性偏好进行了估计。应用结果表明，新模型在调整复杂度后的性能优于多种替代模型，且其额外灵活性使得偏好估计结果更贴近议员与大法官公认的意识形态立场。