Approximate maximum likelihood DOA estimation in multiplicative noise environments

2002 ◽  
Author(s):  
O. Besson ◽  
F. Vincent ◽  
P. Stoica ◽  
A.B. Gershman
Keyword(s):  
Maximum Likelihood ◽  
Multiplicative Noise ◽  
Doa Estimation ◽  
Approximate Maximum Likelihood

Fast 3D AML-Based Bird Song Estimation

2014 ◽  
Vol 02 (03) ◽  
pp. 249-259 ◽  
Author(s):  
Jiawei Zhang ◽  
George Kossan ◽  
Richard W. Hedley ◽  
Ralph E. Hudson ◽  
Charles E. Taylor ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Real Time ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Bird Song ◽  
Computational Burden ◽  
Approximate Maximum Likelihood ◽  
Acoustic Array ◽  
Real Time Applications

In this paper, we present simulations and experimentally collected bird song data collected using a modified Voxnet acoustic array node (with four microphones) to perform 3D direction-of-arrival (DOA) estimation of various bird sources. We used the Approximate Maximum-Likelihood (AML) algorithm to construct the steering matrix in the beamforming process for the estimation of the DOA of the bird signals. While the computational burden is high in the 3D scenario, various strategies have been developed to reduce the computational burden of the algorithm for potential real-time applications. Extensive simulations and experimentally collected data are used to validate the effectiveness of the AML algorithm for 3D estimations and the usefulness of the modified Voxnet node. Both the estimated azimuths and elevations have approximately plus and minus 10 degrees of errors.

scholarly journals Selecting between causal and noncausal models with quantile autoregressions

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Alain Hecq ◽  
Li Sun
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Latin American ◽  
Financial Time Series ◽  
Selection Criterion ◽  
Likelihood Methods ◽  
Approximate Maximum Likelihood ◽  
Financial Time ◽  
Maximum Likelihood Methods ◽  
Latin American Countries

AbstractWe propose a model selection criterion to detect purely causal from purely noncausal models in the framework of quantile autoregressions (QAR). We also present asymptotics for the i.i.d. case with regularly varying distributed innovations in QAR. This new modelling perspective is appealing for investigating the presence of bubbles in economic and financial time series, and is an alternative to approximate maximum likelihood methods. We illustrate our analysis using hyperinflation episodes of Latin American countries.

WAVELET ESTIMATORS FOR LONG MEMORY IN STOCK MARKETS

2009 ◽  
Vol 12 (03) ◽  
pp. 297-317 ◽  
Author(s):  
ANOUAR BEN MABROUK ◽  
HEDI KORTAS ◽  
SAMIR BEN AMMOU
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Stock Returns ◽  
Long Memory ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Estimation Accuracy ◽  
Approximate Maximum Likelihood ◽  
Approximate Maximum Likelihood Estimator ◽  
Wavelet Estimators

In this paper, fractional integrating dynamics in the return and the volatility series of stock market indices are investigated. The investigation is conducted using wavelet ordinary least squares, wavelet weighted least squares and the approximate Maximum Likelihood estimator. It is shown that the long memory property in stock returns is approximately associated with emerging markets rather than developed ones while strong evidence of long range dependence is found for all volatility series. The relevance of the wavelet-based estimators, especially, the approximate Maximum Likelihood and the weighted least squares techniques is proved in terms of stability and estimation accuracy.

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