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Ideal point estimation in political science usually aims to reduce a matrix of\udvotes to a small number of preference dimensions. We argue that taking a non-parametric\udperspective can yield measures that are more useful for some subsequent\udanalyses. We propose a conditional autoregressive preference measurement\udmodel, which we use to generate case-specific preference estimates for US\udSupreme Court justices from 1946 to 2005. We show that the varying relative\udlegal positions taken by justices across areas of law condition the opinion assignment\udstrategy of the Chief Justice and the decisions of all justices as to whether\udto join the majority opinion. Unlike previous analyses that have made similar\udclaims, using case-specific preference estimates enables us to hold constant the\udjustices involved, providing stronger evidence that justices are strategically responsive\udto each others' relative positions on a case-by-case basis rather than\udsimply their identities or average relative preferences

Estimating Vote-Specific Preferences from Roll-Call Data Using Conditional Autoregressive Priors