Showing papers by "Melvin J. Hinich published in 1981"

A New Approach to Voter Uncertainty in the Downsian Spatial Model

Abstract: A new model of voter uncertainty about candidate positions is presented in which voters simplify the issue positions of the candidate by representing them as a random variable on an underlying evaluative dimension. It is further assumed that the degree of voter uncertainty depends upon the mean location of this random variable. It is demonstrated that this type of spatially dependent uncertainty results in a shift of each voter's ideal point on the underlying dimension. We discuss two types of shifts, one in which voter ideal points are shifted toward the extremes and the other in which they are shifted toward the center and comment on the consequences of these shifts for two-candidate electoral competition. Finally, we relate our model to earlier work on the subject by Downs (1957) and Shepsle (1972).

A New Approach to the Spatial Theory of Electoral Competition

Abstract: In his unidimensional model of electoral competition, Downs argues that voters use party ideology as an informational short cut for forecasting the policies that a party will pursue if elected. Parties are perceived by voters as points on an ideological axis. In the Davis-Hinich multidimensional model, on the other hand, the axes are real issues, and the principal actors are politicians who are modeled as points in the multi-issue space. This paper reformulates spatial voting theory in terms of a model that connects what we call predictive dimensions with political issues that are salient during a given election campaign. This model is both a synthesis and an extension of the Downs and Davis-Hinich spatial models. We obtain a median voter result for one predictive dimension that is similar to the Downs result but with important differences. We also obtain results showing the electoral advantage of incumbency and the tendency for incremental change when there is a great deal of heterogeneity in voter perceptions about the candidates.

Voting as an act of contribution

Abstract: This paper presents a theory which rationalizes voting in terms of the marginal utility a citizen derives from contributing a small amount of effort in the political process when the cost of voting is small. Citizens abstain when the marginal cost of voting exceeds the marginal perceived benefit. A simple choice rule for voting in a two candidate race is derived from the theory. This rule depends on the voter's subjective belief about the election outcome as well as his preferences for the candidates. The key assumption is that the voter's utility increases if he votes for a winner, or decreases if he votes for a loser. This assumption is no less plausible than the assumption that voters believe they can be pivotal.

Frequency-wavenumber array processing

Abstract: Most array signal processing systems use delay‐and‐sum beamforming to estimate source bearings. This paper demonstrates the close relationship between beamforming and frequency–wavenumber spectrum analysis. The latter approach has computational advantages over beamforming when the noise is spatially correlated. The wavenumber approach is used to derive the array response of a general linear or planar array to plane wave signals. The statistical properties of the maximum‐likelihood estimators of source bearing and amplitude are presented for an array with many elements. Optimal array design is also discussed.

Estimating the earth's impedance function when there is noise in the electric and magnetic signals

Abstract: Publisher Summary This chapter describes a technique for making asymptotically unbiased estimates of the transfer or impedance function of an electric circuit, a black box, or the earth when both the input and output signals are contaminated by additive noises. The chapter explores the consistency of an estimator of a constant multiple of a minimum-phase frequency response function for the situation in which both the input series and the output series are measured with error. The chapter also explains how the constant multiplier can be determined and how an estimate of the phase of the frequency response function can be obtained from the cross spectrum of the observed series. It then presents an estimate of a multiple of the amplitude function by way of approximations to the Hilbert transform formulas connecting these quantities. The effectiveness of this procedure is demonstrated by adding noise to a known set of magnetic and electric field data from magnetotellurics and then recovering the estimate of the impedance function of the earth gotten from the noiseless data.

Statistical approach to passive target tracking

Abstract: The paper presents a method for passively tracking a moving target using a sequence of bearings from a surveillance platform. The key assumption for the method is that the target is moving at a constant speed on a fixed heading during the data acquisition period. This is the same assumption that is made for Ekelund ranging. Parameter estimates are computed after bearings are taken as the tracking platforms maneuvers. No specific maneuver by the tracking platform is required. The estimators presented in this paper are approximately maximum likelihood when the target is distant from the platform.

Estimating signal and noise using a random array

Abstract: This paper presents approximations for the rms error of the maximum likelihood estimator of the direction of a plane wave incident on a random array. The sensor locations are assumed to be realizations of independent, identically distributed random vectors. The second part of the paper presents an asymptotically unbiased estimator of the noise wavenumber spectrum from random array data.

A method for estimating distributed lags when observations are randomly missing

Abstract: This paper presents a frequency-domain technique for estimating distributed lag coefficients (the impulse-response function) when observations are randomly missed. The technique treats stationary processes with randomly missed observations as amplitude-modulated processes and estimates the transfer function accordingly. Estimates of the lag coefficients are obtained by taking the inverse transform of the estimated transfer function. Results with artificially created data show that the technique performs well even when the probability of an observation being missed is one-half and in some cases when the probability is as low as one-fifth. The approximate asymptotic variance of the estimator is also calculated in the paper.

A method for estimating distributed lags when observations are randomly missing

Abstract: This paper presents a frequency-domain technique for estimating distributed lag coefficients (the impulse-response function) when observations are randomly missed. The technique treats stationary processes with randomly missed observations as amplitude-modulated processes and estimates the transfer function accordingly. Estimates of the lag coefficients are obtained by taking the inverse transform of the estimated transfer function. Results with artificially created data show that the technique performs well even when the probability of an observation being missed is one-half and in some cases when the probability is as low as one-fifth. The approximate asymptotic variance of the estimator is also calculated in the paper.