A general nonlinear logit model is used to analyze political choice data. The model assumes
probabilistic voting based on a spatial utility function. The parameters of the utility function …

Theory: The spatial model of elections can better be represented by using conditional logit
models which consider the position of the parties in issue spaces than by multinomial logit …

A methodology is presented for empirical estimation of spatial models of voting in mass
elections. The basic choice model posits utility functions that depend on spatial distance and …

This paper presents a software package designed to estimate Poole and Rosenthal W-
NOMINATE scores in R. The package uses a logistic regression model to analyze political …

We develop a Bayesian procedure for estimation and inference for spatial models of roll call
voting. This approach is extremely flexible, applicable to any legislative setting, irrespective …

Logit and probit, the two most common techniques for estimation of models with a
dichotomous dependent variable, impose the assumption that individuals with a probability …

Empirical models of spatial voting allow legislators' locations in a policy or ideological space
to be inferred from their roll‐call votes. These are typically random utility models where the …

This book presents a simple geometric model of voting as a tool to analyze parliamentary
roll call data. Each legislator is represented by one point and each roll call is represented by …

This paper surveys recent developments in analytical models of roll-call voting in the US
Congress. Two controversies are analyzed in the paper: the one over the number of …

This paper shows a general nonparametric unfolding technique for maximizing the correct
classification of binary choice or two-category data. The motivation for and the primary focus …