A new methodology that estimates attitudes semiparametrically and estimates actions nonparametrically, as a function of the resulting attitudinal measures, is used to examine the behavioral effects of ѣultural' and ѥconomic' preferences in the Presidential elections of 1984 and 1992. The...

We propose simultaneous mean-variance regression for the linear estimation and approximation of conditional mean functions. In the presence of heteroskedasticity of unknown form, our method accounts for varying dispersion in the regression outcome across the support of conditioning variables by...

We propose dual regression as an alternative to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions. Dual regression provides all the interpretational power of the quantile regression process while avoiding the need for...

Conditional distribution functions are important statistical objects for the analysis of a wide class of problems in econometrics and statistics. We propose flexible Gaussian representations for conditional distribution functions and give a concave likelihood formulation for their global...

We model attitudes as latent variables that induce stochastic dominance relations in (item) responses. Observable characteristics that affect attitudes can be incorporated into the analysis to improve the measurement of the attitudes; the measurements are posterior distributions that condition...

This paper (i) formalizes conditions under which a population distribution of categorical responses to attitudinal questions (ѩtemsҩ has a scale representation; (ii) develops tests for whether a particular sample of item responses is consistent with a scale representation; (iii) develops...

We propose an alternative ('dual regression') to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions. Dual regression provides all the interpretational power of the quantile regression process while largely avoiding the need...