Noise benefits to robust M-estimation of location in dependent observations

2018 ◽  
Vol 505 ◽  
pp. 144-152
Author(s):  
Yan Pan ◽  
Yuhao Ren ◽  
Fabing Duan
Keyword(s):  
Dependent Observations ◽  
M Estimation ◽  
Noise Benefits

Byzantine-robust distributed sparse learning for M-estimation

2021 ◽  
Author(s):  
Jiyuan Tu ◽  
Weidong Liu ◽  
Xiaojun Mao
Keyword(s):  
Sparse Learning ◽  
M Estimation

scholarly journals Aerosol classification from airborne HSRL and comparisons with the CALIPSO vertical feature mask

2013 ◽  
Vol 6 (1) ◽  
pp. 1815-1858 ◽  
Author(s):  
S. P. Burton ◽  
R. A. Ferrare ◽  
M. A. Vaughan ◽  
A. H. Omar ◽  
R. R. Rogers ◽  
...  
Keyword(s):  
High Spectral Resolution ◽  
Dependent Observations ◽  
Aerosol Retrieval ◽  
Direct Measurements ◽  
Aerosol Classification ◽  
Lidar Ratio ◽  
Langley Research Center ◽  
High Spectral Resolution Lidar ◽  
Aerosol Types ◽  
Aerosol Type

Abstract. Aerosol classification products from the NASA Langley Research Center (LaRC) airborne High Spectral Resolution Lidar (HSRL-1) on the NASA B200 aircraft are compared with coincident V3.01 aerosol classification products from the CALIOP instrument on the CALIPSO satellite. For CALIOP, aerosol classification is a key input to the aerosol retrieval, and must be inferred using aerosol loading-dependent observations and location information. In contrast, HSRL-1 makes direct measurements of aerosol intensive properties, including the lidar ratio, that provide information on aerosol type. In this study, comparisons are made for 109 underflights of the CALIOP orbit track. We find that 62% of the CALIOP marine layers and 54% of the polluted continental layers agree with HSRL-1 classification results. In addition, 80% of the CALIOP desert dust layers are classified as either dust or dusty mix by HSRL-1. However, agreement is less for CALIOP smoke (13%) and polluted dust (35%) layers. Specific case studies are examined, giving insight into the performance of the CALIOP aerosol type algorithm. In particular, we find that the CALIOP polluted dust type is overused due to an attenuation-related depolarization bias. Furthermore, the polluted dust type frequently includes mixtures of dust plus marine aerosol. Finally, we find that CALIOP's identification of internal boundaries between different aerosol types in contact with each other frequently do not reflect the actual transitions between aerosol types accurately. Based on these findings, we give recommendations which may help to improve the CALIOP aerosol type algorithms.

scholarly journals Subsampling the mean of heavy-tailed dependent observations

2004 ◽  
Vol 25 (2) ◽  
pp. 217-234 ◽  
Author(s):  
Piotr Kokoszka ◽  
Michael Wolf
Keyword(s):  
Dependent Observations ◽  
The Mean ◽  
Heavy Tailed

scholarly journals Non-Gaussian noise benefits for coherent detection of narrowband weak signal

2014 ◽  
Vol 378 (26-27) ◽  
pp. 1820-1824 ◽  
Author(s):  
Fabing Duan ◽  
François Chapeau-Blondeau ◽  
Derek Abbott
Keyword(s):  
Gaussian Noise ◽  
Coherent Detection ◽  
Weak Signal ◽  
Noise Benefits ◽  
Non Gaussian
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