Results 1 to 10 of about 1,494,076 (289)
Robust Shadow Estimation [PDF]
PRX Quantum, 2021 Efficiently estimating properties of large and strongly coupled quantum systems is a central focus in many-body physics and quantum information theory. While quantum computers promise speedups for many of these tasks, near-term devices are prone to noise
Senrui Chen +3 more
doaj +4 more sources
Robust Relative Error Estimation [PDF]
Entropy, 2018 Relative error estimation has been recently used in regression analysis. A crucial issue of the existing relative error estimation procedures is that they are sensitive to outliers.
Kei Hirose, Hiroki Masuda
doaj +4 more sources
Robust Bayes-Like Estimation: Rho-Bayes estimation [PDF]
The Annals of Statistics, 2016 We consider the problem of estimating the joint distribution $P$ of $n$ independent random variables within the Bayes paradigm from a non-asymptotic point of view.
Andrei N. Parvulescu (2122654) +7 more
core +12 more sources
Robust Optical Flow Estimation [PDF]
Image Processing On Line, 2013 n this work, we describe an implementation of the variational method proposed by Brox etal. in 2004, which yields accurate optical flows with low running times.
Javier Sánchez Pérez +2 more
doaj +3 more sources
Multi-layer CNN-LSTM network with self-attention mechanism for robust estimation of nonlinear uncertain systems [PDF]
Frontiers in NeuroscienceIntroductionWith the help of robot technology, intelligent rehabilitation of patients with lower limb motor dysfunction caused by stroke can be realized.
Lin Liu +11 more
doaj +2 more sources
Absolute M split estimation as an alternative for robust M-estimation [PDF]
Advances in Geodesy and Geoinformation, 2022 The problem of outlying observations is very well-known in the surveying data processing. Outliers might have several sources, different magnitudes, and shares within the whole observation set.
Robert Duchnowski, Patrycja Wyszkowska
doaj +1 more source
Robust Estimation via Robust Gradient Estimation [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2020 SummaryWe provide a new computationally efficient class of estimators for risk minimization. We show that these estimators are robust for general statistical models, under varied robustness settings, including in the classical Huber ε-contamination model, and in heavy-tailed settings.
Prasad, Adarsh +3 more
openaire +2 more sources
A COMPARISON OF M-ESTIMATION AND S-ESTIMATION ON THE FACTORS AFFECTING IR DHF IN EAST JAVA IN 2017
The Indonesian Journal of Public Health, 2021 Robust regression on M estimation and S estimation is the Ordinary Least Square (OLS) regression on the data outlier. East Java is one of the provinces in Indonesia with a high case fatalitiy rate (1.34%). The raising of Dengue Haemoragic Fever (DHF) in
Mardiana Mardiana +3 more
doaj +1 more source
Robust pooling through the data mode
Intelligent Systems with Applications, 2023 The task of learning from point cloud data is always challenging due to the often occurrence of noise and outliers in the data. Such data inaccuracies can significantly influence the performance of state-of-the-art deep learning networks and their ...
Ayman Mukhaimar +4 more
doaj +1 more source
COMPARISON OF ROBUST ESTIMATION ON MULTIPLE REGRESSION MODEL
Barekeng, 2023 This study aimed to compare the robustness of the OLS method with a robust regression model on data that had outliers. The methods used on the robust regression model were M-estimation, MM-estimation, and S-estimation.
Padrul Jana +2 more
doaj +1 more source