Results 261 to 270 of about 5,238,545 (299)
Some of the next articles are maybe not open access.
An Adaptive and Robust Edge Detection Method Based on Edge Proportion Statistics
IEEE Transactions on Image Processing, 2020Edge detection is one of the most fundamental operations in the field of image analysis and computer vision as a critical preprocessing step for high-level tasks.
Yang Liu, Zongwu Xie, Hong Liu
semanticscholar +1 more source
Robust Statistics for Signal Processing
, 2018K-distribution, 103, 121–123 M-estimator, 1, 5, 10–12, 19, 21, 23, 27, 46, 48, 53, 54, 61–63, 67, 100, 110–113, 117, 118, 120, 121, 140, 141, 157, 162, 167, 183, 197, 206, 214, 229, 249, 260 ARMA model, 196 Huber’s, 11, 12, 14, 16, 17, 22, 23, 26, 27, 50,
A. Zoubir +3 more
semanticscholar +1 more source
Robust statistical arbitrage strategies
Quantitative Finance, 2019We investigate statistical arbitrage strategies when there is ambiguity about the underlying time-discrete financial model. Pricing measures are assumed to be martingale measures calibrated to prices of liquidly traded options, whereas the set of admissible physical measures is not necessarily implied from market data.
Eva Lütkebohmert, Julian Sester
openaire +2 more sources
Robust statistical inference for matched win statistics
Statistical Methods in Medical Research, 2022As alternatives to the time-to-first-event analysis of composite endpoints, the win statistics, that is, the net benefit, the win ratio, and the win odds have been proposed to assess treatment effects, using a hierarchy of prioritized component outcomes based on clinical relevance or severity. Whether we are using paired organs of a human body or pair-
openaire +2 more sources
Robust Statistics—The Approach Based on Influence Functions
, 1986J. Law
semanticscholar +2 more sources
Proceedings of the 19th International Conference on Evaluation and Assessment in Software Engineering, 2015
This keynote discusses the need for more robust statistical methods. For visualizing data I suggest using Kernel density plots rather than box plots. For parametric analysis, I propose more robust measures of central location such as trimmed means, which can support reliable tests of the differences between the central location of two or more samples ...
openaire +1 more source
This keynote discusses the need for more robust statistical methods. For visualizing data I suggest using Kernel density plots rather than box plots. For parametric analysis, I propose more robust measures of central location such as trimmed means, which can support reliable tests of the differences between the central location of two or more samples ...
openaire +1 more source
A robust statistics approach for plane detection in unorganized point clouds
Pattern Recognition, 2020Abner M. C. Araújo, M. M. O. Neto
semanticscholar +1 more source
Journal of the American Statistical Association, 1983
Leone Y. Low, Peter J. Huber
openaire +1 more source
Leone Y. Low, Peter J. Huber
openaire +1 more source
2013
“Robust” estimators are resistant to outliers in data, contrary to the usual classical least-squares estimator such as NLSE. We will describe two robust estimators in this chapter and give an example of the application of them to pre-processing some experimental data.
Harold A. Sabbagh +4 more
openaire +1 more source
“Robust” estimators are resistant to outliers in data, contrary to the usual classical least-squares estimator such as NLSE. We will describe two robust estimators in this chapter and give an example of the application of them to pre-processing some experimental data.
Harold A. Sabbagh +4 more
openaire +1 more source