Results 261 to 270 of about 2,181,691 (306)

Optimized parametric bispectrum estimation

ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing, 2003
When analyzing various signals produced by some nonlinear process, higher-order spectra are necessary to characterize the nonlinearity. For example, the bispectrum is very useful in analyzing three-wave nonlinear interaction data. An optimized parametric bispectrum estimation method is presented.
Chong Koo An, Sung Bae Kim, Edward J. Powers   +2 more
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Robust estimation with parametric score function estimation

IEEE International Conference on Acoustics Speech and Signal Processing, 2002
Robust estimation of signal parameters in the additive noise model has become an important problem. Its relevance can be attributed to the realisation that impulsive noise is present in communications channels. The approach to robust estimation taken here follows the M-estimation concept of robust statistics, except the score function is modeled as a ...
Ramon F. Brcich, Abdelhak M. Zoubir
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On the Estimation of Parametric Density Functions

Biometrika, 1980
SUMMARY The best invariant estimate of the parametric density function in statistical models invariant under a transformation group is derived. The estimate is best with respect to a goodness-of-fit criterion based on an informa,tion measure. We are concerned with the estimation of a parametric density function p(y I 0) using data x.
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On Information Inequalities in the Parametric Estimation

Theory of Probability & Its Applications, 1993
Let \(X_ 1,X_ 2,\dots\) be independent, identically distributed random variables, \(X_ i \in ({\mathcal X},\mu)\). Assume that \(X_ 1\) belongs to a distribution \(P_ \theta\) from a parametric family \(\{P_ t\); \(t \in \Theta\}\), where \(\Theta\) is an open subset of \(\mathbb{R}^ d\) and, for any \(t \in \Theta\), \(P_ t\) is absolutely continuous ...
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Non‐parametric Estimation of the Residual Distribution

Scandinavian Journal of Statistics, 2001
Consider a heteroscedastic regression model Y=m(X) +σ(X)ε, where the functions m and σ are “smooth”, and ε is independent of X. An estimator of the distribution of ε based on non‐parametric regression residuals is proposed and its weak convergence is obtained. Applications to prediction intervals and goodness‐of‐fit tests are discussed.
Akritas, Michael G., Van Keilegom, Ingrid   +1 more
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A parametric technique for time delay estimation

ICASSP '82. IEEE International Conference on Acoustics, Speech, and Signal Processing, 1984
Estimating the time-delay between two received signals is formulated as a parameter estimation problem for a certain spectral model. The model parameters are computed by a two step procedure: The modified Yule-Walker equations are used to estimate the autoregressive coefficients of the source signal, and a frequency domain squared error criterion is ...
Benjamin Friedlander, Boaz Porat
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On Coherence in Parametric Density Estimation

Biometrika, 1990
SUMMARY In parametric density estimation the existence of a prior distribution on the parameter dictates the use of the corresponding predictive density function as estimate. This paper argues that in a decision theory approach to parameter density estimation any loss function introduced should lead to this predictive result for situations where prior ...
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Parametric and Nonparametric FDR Estimation Revisited

Biometrics, 2006
Summary Nonparametric and parametric approaches have been proposed to estimate false discovery rate under the independent hypothesis testing assumption. The parametric approach has been shown to have better performance than the nonparametric approaches. In this article, we study the nonparametric approaches and quantify the underlying relations between
Wu, Baolin, Guan, Zhong, Zhao, Hongyu
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Parametric Estimation

Technometrics, 1970
G. S. Rogers, M. T. Wasan
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On multinomial parametric estimation

Communications in Statistics - Theory and Methods, 1983
When the multinomial probabilities are functions of a vector 8 it is shown that when bias correction is applied, the maximum likelihood estimator of 0 is least deficient in a class of estimators which includes the minimum chi-square and the minimum modified chi-square estimators.
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