Results 21 to 30 of about 17,358,302 (310)
On Robustness for Spatio-Temporal Data
Mathematics, 2022 The spatio-temporal variogram is an important factor in spatio-temporal prediction through kriging, especially in fields such as environmental sustainability or climate change, where spatio-temporal data analysis is based on this concept.
Alfonso García-Pérez
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The density of multivariate $M$-estimates [PDF]
The Annals of Statistics, 2000 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Almudevar, Anthony +2 more
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M-Estimators of Scatter with Eigenvalue Shrinkage [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 A popular regularized (shrinkage) covariance estimator is the shrinkage sample covariance matrix (SCM) which shares the same set of eigenvectors as the SCM but shrinks its eigenvalues toward its grand mean. In this paper, a more general approach is considered in which the SCM is replaced by an M-estimator of scatter matrix and a fully automatic data ...
Palomar, Daniel P. +3 more
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The performance of some new estimated ridge parameter regression model [PDF]
مجلة جامعة الانبار للعلوم الصرفةIn the presence of high correlation between the independent variables in the linear regression model, which is known as the multicollinearity problem, the ordinary least squares estimator produces large variations in the sample. To overcome this problem,
Fatima ALfahdawe, Mustafa Alheety
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Sparsity and M-Estimators in RFI Mitigation for Typical Radio Astrophysical Signals
Universe, 2023 In this paper, radio frequency interference (RFI) mitigation by robust maximum likelihood estimators (M-estimators) for typical radio astrophysical signals of, e.g., pulsars and fast radio bursts (FRBs), will be investigated.
Hao Shan +6 more
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Robust and sparse M-estimation of DOA
Signal Processing, 2023 A robust and sparse Direction of Arrival (DOA) estimator is derived for array data that follows a Complex Elliptically Symmetric (CES) distribution with zero-mean and finite second-order moments. The derivation allows to choose the loss function and four loss functions are discussed in detail: the Gauss loss which is the Maximum-Likelihood (ML) loss ...
Christoph F. Mecklenbräuker +3 more
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A Novel Influence Function M-Estimator-Based for Active Noise Control
Archives of Acoustics, 2021 M-estimators are widely used in active noise control (ANC) systems in order to update the adaptive FIR filter taps. ANC systems reduce the noise level by generating anti-noise signals. Up to now, the evaluation of M-estimators capabilities has shown that
Seyed Amir HOSEINI SABZEVARI +1 more
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Axioms, 2023 Under classical statistics, research typically relies on precise data to estimate the population mean when auxiliary information is available. Outliers can pose a significant challenge in this process.
Abdullah Mohammed Alomair, Usman Shahzad
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Journal of Agricultural and Resource Economics, 1992 The role of price risk in sow farrowings is investigated by using bivariate ARCH-M and GARCH-M models and a nonparametric kernel estimator. To account for the relevant time horizon of irreversible supply decisions, predictions for mean price and ...
Matthew T. Holt, GianCarlo Moschini
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On Maximum Likelihood Estimation of the Markov Process [PDF]
The Egyptian Statistical Journal, 1981 The maximum likelihood estimators of the unknown parameters of the stationary Gaussian Markov process are obtained. The likelihood function of the observations is derived.
A.A. Abd-Alla
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