Showing papers by "Melvin J. Hinich published in 1989"
TL;DR: In this paper, the authors construct a general probabilistic spatial theory of elections and examine sufficient conditions for equilibrium in two-candidate contests with expected vote-maximizing candidates.
Abstract: In this paper, we construct a general probabilistic spatial theory of elections and examine sufficient conditions for equilibrium in two-candidate contests with expected vote-maximizing candidates. Given strict concavity of the candidate objective function, a unique equilibrium exists and the candidates adopt the same set of policy positions. Prospective uncertainty, reduced policy salience, degree of concavity of voter utility functions, some degree of centrality in the feasible set of policy locations, and restrictions on the dimensionality of the policy space are all stabilizing factors in two-candidate elections.
117 citations
TL;DR: In this article, the bispectrum of a ship's radiated noise was estimated using a towed array being towed by the same ship that served as the noise source, and the results showed that there exist frequency-dependent bispectral components in the ship's radiometric signals.
Abstract: The bispectrum of ship‐radiated noise is estimated. The noise was received on a towed array being towed by the same ship that served as the noise source. The array was beamformed such that the forward endfire beam pointed toward the towing platform and the broadside beam sampled the environment without the radiated noise of the ship. The results show that there exist frequency‐dependent bispectral components in the ship’s radiated noise, whereas the ambient noise does not contain any significant bispectral components. Since the existence of a nonzero frequency‐dependent bispectrum indicates the existence of nonlinear components in the noise‐generating mechanism, it is concluded that the radiated noise of the towing platform contains such nonlinear mechanisms. Therefore, the bispectrum could be used to indicate the existence of such noise sources as would normally be hidden in the background noise when the usual spectral estimation procedures are applied.
53 citations
TL;DR: In this article, a maximum likelihood estimator for passive range and depth estimation of an acoustic point source in a shallow water waveguide is presented, where the data from a vertical array of hydrophones are passed through a modal filter, the output of which is the set of complex modal amplitudes associated with the normal mode model of acoustic propagation.
Abstract: The maximum‐likelihood estimator for passive range and depth estimation of an acoustic point source in a shallow‐water waveguide is presented. The data from a vertical array of hydrophones are passed through a modal filter, the output of which is the set of complex modal amplitudes associated with the normal‐mode model of acoustic propagation. The range and depth estimates are then found by a maximum‐likelihood estimation procedure that uses these modal amplitudes as inputs. This technique is compared to the matched‐field procedure and is shown to have better signal‐to‐noise and sidelobe behavior for a given scenario. Results are given for both synthetic and real data. The results with the real data demonstrate the importance of the mode‐filtering property of the maximum‐likelihood estimator presented in this work.
31 citations
TL;DR: Using a scaling procedure that directly estimates the key parameters of a new spatial model of elections described previously, the locations of voters and candidates in the three waves of the 1980 Major Panel File are recovered.
29 citations
01 Jan 1989
TL;DR: In this paper, the authors examined the Gaussianity of ocean acoustical time series from a time series point of view and showed that even series which previously passed univariate tests for normality are shown to be non-Gaussian time series, so that these time series must be modeled in a nonlinear fashion.
Abstract: Previous research into the Gaussianity of ocean acoustical time series has examined univariate marginal densities. In this paper we present research which examines this issue from a time series point of view. Even series which previously passed univariate tests for normality are shown to be non-Gaussian time series. Additionally, these time series are shown to be nonlinear time series, so that such acoustical series must be modeled in a nonlinear fashion.
9 citations