Results 221 to 230 of about 1,032,480 (246)

Parametric covariance prediction for heteroscedastic noise

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015
The ubiquitous additive Gaussian noise model is favored in statistical modeling applications for its flexibility and ease of use. Often noise is assumed to be well-represented by a constant covariance, while in reality error characteristics may change predictably.
Humphrey Hu, George Kantor
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Twin proximal support vector regression with heteroscedastic Gaussian noise

Expert Systems With Applications
Quan Qian, Chao Liu
exaly   +2 more sources

DOA estimation in heteroscedastic noise

The Journal of the Acoustical Society of America, 2018
The paper considers direction of arrival (DOA) estimation from long-term observations in a noisy environment. In such an environment the noise source might evolve, causing the stationary models to fail. Therefore a heteroscedastic Gaussian noise model is introduced where the variance can vary across observations and sensors.
Peter Gerstoft, Kay L. Gemba, Santosh Nannuru   +2 more
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Density Deconvolution in a Non-standard Case of Heteroscedastic Noises

Journal of Statistical Theory and Practice, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Cao Xuan Phuong, Le Thi Hong Thuy
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Analysis of TOA localization with heteroscedastic noises

Proceedings of the 33rd Chinese Control Conference, 2014
This paper focuses on the problem of source localization using time-of-arrival (TOA) measurements. Differently from the existing studies assuming that TOA measurement noises are independent and identically distributed, we deal with more practical TOA measurements suffering from heteroscedastic noises due to different physical distances between a source
Baoqi Huang, Lihua Xie, Zai Yang
openaire   +1 more source

Improved radial basis function network for the heteroscedasticity noises

2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2016
The paper presents an improved redial basis function network to degrade the influence of the heteroscedasticity noises in the training data. A general purpose learning algorithm is regarded as the statistical nonlinear regression model which is assumed the constant noise level. However, the heteroscedasticity noises always exist in the real data.
Yue-Shiang Liu, Shun-Feng Su, Ming-Chang Chen, Jin-Tsong Jeng   +3 more
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Heteroscedasticity test of high‐frequency data with jumps and market microstructure noise

Applied Stochastic Models in Business and Industry, 2022
AbstractIn this paper, we are interested in testing whether the volatility process is constant or not during a given time span by using high‐frequency data with the presence of jumps and market microstructure noise. Based on estimators of integrated volatility and spot volatility, we propose a nonparametric procedure to depict the discrepancy between ...
Liu, Qiang, Liu, Zhi, Zhang, Chuanhai
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Component Fusion for Face Detection in the Presence of Heteroscedastic Noise

2003
Face detection using components has been proved to produce superior results due to its robustness to occlusions and pose and illumination changes. A first level of processing is devoted to the detection of individual components, while a second level deals with the fusion of the component detectors.
Binglong Xie, Dorin Comaniciu, Visvanathan Ramesh, Markus Simon, Terrance E. Boult   +4 more
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Detection and Estimation of Heteroscedastic Noise by Means of the Wavelet Transform

Analytical Communications, 1997
The ability of the wavelet transform (WT) to detect and estimate heteroscedastic noise was determined. The WT provides information in both the time and the frequency domains which is necessary for the detection of heteroscedastic noise. By a simple F-test applied to non-overlapping intervals of wavelet coefficients differences in the variance can ...
Christian R. Mittermayr, Erwin Rosenberg, Manfred Grasserbauer   +2 more
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Preprocessing of analytical profiles in the presence of homoscedastic or heteroscedastic noise

Analytical Chemistry, 1994
Analytical profiles are commonly normalized to the most intense peak or to constant sum prior to library searches or multivariate analysis. This work examines normalization procedures from a theoretical point of view and their effects on simulated and real data.
Olav M. Kvalheim, Frode. Brakstad, Yizeng. Liang   +2 more
openaire   +1 more source