Approximate Maximum Likelihood Mobile Localization using Scatterer Information

This letter deals with a hybrid time-of-arrival/angle-of-arrival (TOA/AOA) approximate maximum likelihood (AML) wireless location algorithm. Thanks to the use of both TOA/AOA measurements, the proposed technique can rely on two base stations (BS) only and achieves better performance compared to the original approximate maximum likelihood (AML) method. The use of two BSs is an important advantage in wireless cellular communication systems because it avoids hearability problems and reduces network signaling burden. Simulation results show that, for certain scenarios, the proposed hybrid TOA/AOA AML with two BSs can outperform the AML with up to six BSs.

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Approximate Maximum Likelihood Estimation Using a 3D GNSS Simulator for Positioning in MP/NLOS Conditions

Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017) ◽

10.33012/2017.15213 ◽

2017 ◽

Cited By ~ 2

Author(s):

Nabil Kbayer ◽

Mohamed Sahmoudi ◽

H�ctor Ortega ◽

C�dric Rouch

Keyword(s):

Maximum Likelihood ◽

Maximum Likelihood Estimation ◽

Likelihood Estimation ◽

Approximate Maximum Likelihood

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Selecting between causal and noncausal models with quantile autoregressions

Studies in Nonlinear Dynamics & Econometrics ◽

10.1515/snde-2019-0044 ◽

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.

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WAVELET ESTIMATORS FOR LONG MEMORY IN STOCK MARKETS

International Journal of Theoretical and Applied Finance ◽

10.1142/s0219024909005233 ◽

2009 ◽

Vol 12 (03) ◽

pp. 297-317 ◽

Cited By ~ 4

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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