Showing papers by "Melvin J. Hinich published in 1994"

Ideology and the theory of political choice

Abstract: There is no unified theory that can explain both voter choice and where choices come from. Hinich and Munger fill that gap with their model of political communication based on ideology.Rather than beginning with voters and diffuse, atomistic preferences, Hinich and Munger explore why large groups of voters share preference profiles, why they consider themselves "liberals" or "conservatives." The reasons, they argue, lie in the twin problems of communication and commitment that politicians face. Voters, overloaded with information, ignore specific platform positions. Parties and candidates therefore communicate through simple statements of goals, analogies, and by invoking political symbols. But politicians must also commit to pursuing the actions implied by these analogies and symbols. Commitment requires that ideologies be used consistently, particularly when it is not in the party's short-run interest.The model Hinich and Munger develop accounts for the choices of voters, the goals of politicians, and the interests of contributors. It is an important addition to political science and essential reading for all in that discipline."Hinich and Munger's study of ideology and the theory of political choice is a pioneering effort to integrate ideology into formal political theory. It is a major step in directing attention toward the way in which ideology influences the nature of political choices." --Douglass C. North." . . represents a significant contribution to the literature on elections, voting behavior, and social choice." --Policy CurrentsMelvin Hinich is Professor of Government, University of Texas. Michael C. Munger is Associate Professor of Political Science, University of North Carolina.

A test of the predictive dimensions model in spatial voting theory

Abstract: We model correlated voter-candidate issue data within the framework of the Enelow-Hinich spatial model of predictive dimensions. The empirical consequences of this model of the issue data are surprising and allow for an indirect test of the Enelow-Hinich spatial model. The central prediction of the correlated data model we construct, which depends critically on the underlying spatial model, is tested with issue data from the 1980 NES pre-election interview. The test results are highly supportive of the model's predictions. We conclude both that the spatial model of predictive dimensions is empirically supported and that candidate spatial locations estimated by the model are not an artifact of correlated voter-candidate issue data.

Development and application of a statistically based feature extraction algorithm for monitoring tool wear in Circuit Board Assembly

Abstract: Vibroacoustic signals of rotating machinery are composed of sums of modulated periodicities, broadband random components, and occasionally a set of transient responses. These signals are not ergodic as the modulated periodicities are partially coherent. Progressive wear of the rotating machine causes the nonlinear structure of the received signal to intensify, and nonlinearity results in transfer of energy between harmonics of the signal's periodic components. Statistics developed from bispectrum and second-order cumulant spectrum estimates of the measured signal are combined with power spectrum amplitudes as feature inputs for standard multivariate classifiers. The higher-order statistics measure, respectively, the extent of nonlinearity and intermodulation of the received signal. Classification results of simulated and actual incipient wear data collected from a controlled experiment drilling circuit boards illustrate the potential of this novel statistical signal processing approach.

Statistical monitoring of rotating machinery by cumulant spectral analysis

Abstract: Background on the rotating drill wear problem, including approaches using a combination of sensors and signal features, are briefly summarized prior to sharing results from a higher- order statistical study of accelerometer data. Vibroacoustic signals of rotating machinery are composed of sums of modulated periodicities, broadband random components, and occasionally a set of transient responses. These signals are not ergodic as the modulated periodicities are partially coherent. Progressive wear of the rotating machine causes the nonlinear structure of the received signal to intensify, and nonlinearity results in transfer of energy between harmonics of the signal's periodic components. Statistics developed from bispectrum and second-order cumulant spectrum estimates of the measured signal are combined with power spectrum amplitudes as feature inputs for standard multivariate classifiers. The higher-order statistics measure, respectively, the extent of nonlinearity and intermodulation of the received signal. Classification results of actual drill wear data collected from a controlled experiment reveal that statistics from estimates of the second order cumulant spectrum have increased discrimination power for detecting incipient drill wear.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.