Prediction of k-records from a general class of distributions under balanced type loss functions

Metrika ◽  
2008 ◽  
Vol 70 (1) ◽  
pp. 19-33 ◽  
Author(s):  
Jafar Ahmadi ◽  
Mohammad Jafari Jozani ◽  
Éric Marchand ◽  
Ahmad Parsian
Keyword(s):  
General Class ◽  
Loss Functions

Bayes estimation based on -record data from a general class of distributions under balanced type loss functions

2009 ◽  
Vol 139 (3) ◽  
pp. 1180-1189 ◽  
Author(s):  
Jafar Ahmadi ◽  
Mohammad Jafari Jozani ◽  
Éric Marchand ◽  
Ahmad Parsian
Keyword(s):  
General Class ◽  
Bayes Estimation ◽  
Loss Functions ◽  
Record Data

Bayesian and Robust Bayesian analysis under a general class of balanced loss functions

2010 ◽  
Vol 53 (1) ◽  
pp. 51-60 ◽  
Author(s):  
Mohammad Jafari Jozani ◽  
Éric Marchand ◽  
Ahmad Parsian
Keyword(s):  
Bayesian Analysis ◽  
General Class ◽  
Loss Functions ◽  
Robust Bayesian Analysis ◽  
Balanced Loss Functions

A Unified Theory for Robust Bayesian Prediction Under a General Class of Regret Loss Functions

2021 ◽  
Author(s):  
Nader Nematollahi ◽  
Mohammad Jafari Jozani ◽  
Razieh Jafaraghaie
Keyword(s):  
General Class ◽  
Bayesian Prediction ◽  
Loss Functions ◽  
Unified Theory

A General Class of Loss Functions with Industrial Applications

1998 ◽  
Vol 30 (2) ◽  
pp. 152-162 ◽  
Author(s):  
Fred A. Spiring ◽  
Anthony S. Yeung
Keyword(s):  
General Class ◽  
Industrial Applications ◽  
Loss Functions ◽  
Class Of Loss Functions

Valence electron spectroscopy of inhomogeneous media

1992 ◽  
Vol 50 (2) ◽  
pp. 1150-1151
Author(s):  
A. Howie ◽  
D.W. McComb
Keyword(s):  
Specific Gravity ◽  
Loss Function ◽  
Electron Spectroscopy ◽  
Valence Electron ◽  
Peak Height ◽  
Loss Functions ◽  
Loss Peak ◽  
High Energies ◽  
Valence Electron Density ◽  
Electron Microscopist

The bulk loss function Im(-l/ε (ω)), a well established tool for the interpretation of valence loss spectra, is being progressively adapted to the wide variety of inhomogeneous samples of interest to the electron microscopist. Proportionality between n, the local valence electron density, and ε-1 (Sellmeyer's equation) has sometimes been assumed but may not be valid even in homogeneous samples. Figs. 1 and 2 show the experimentally measured bulk loss functions for three pure silicates of different specific gravity ρ - quartz (ρ = 2.66), coesite (ρ = 2.93) and a zeolite (ρ = 1.79). Clearly, despite the substantial differences in density, the shift of the prominent loss peak is very small and far less than that predicted by scaling e for quartz with Sellmeyer's equation or even the somewhat smaller shift given by the Clausius-Mossotti (CM) relation which assumes proportionality between n (or ρ in this case) and (ε - 1)/(ε + 2). Both theories overestimate the rise in the peak height for coesite and underestimate the increase at high energies.

A general class of models for stationary two-dimensional random processes

Biometrika ◽  
1985 ◽  
Vol 72 (2) ◽  
pp. 281-291 ◽  
Author(s):  
A. V. VECCHIA
Keyword(s):  
General Class ◽  
Random Processes ◽  
Two Dimensional

A CLASS OF LORENZ CURVES BASED ON LINEAR EXPONENTIAL LOSS FUNCTIONS

2002 ◽  
Vol 31 (6) ◽  
pp. 925-942 ◽  
Author(s):  
José María Sarabia ◽  
Marta Pascual
Keyword(s):  
Loss Functions ◽  
Lorenz Curves

A general class of processor interconnection strategies

1982 ◽  
Vol 10 (3) ◽  
pp. 90-98 ◽  
Author(s):  
Laxmi N. Bhuyan ◽  
Dharma P. Agrawal
Keyword(s):  
General Class

scholarly journals Study on Radar Echo-Filling in an Occlusion Area by a Deep Learning Algorithm

2021 ◽  
Vol 13 (9) ◽  
pp. 1779
Author(s):  
Xiaoyan Yin ◽  
Zhiqun Hu ◽  
Jiafeng Zheng ◽  
Boyong Li ◽  
Yuanyuan Zuo
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Learning Algorithm ◽  
Weather Radar ◽  
Loss Functions ◽  
Training Dataset ◽  
Echo Intensity ◽  
Common Mean ◽  
Deep Learning Algorithm ◽  
Radar Beam

Radar beam blockage is an important error source that affects the quality of weather radar data. An echo-filling network (EFnet) is proposed based on a deep learning algorithm to correct the echo intensity under the occlusion area in the Nanjing S-band new-generation weather radar (CINRAD/SA). The training dataset is constructed by the labels, which are the echo intensity at the 0.5° elevation in the unblocked area, and by the input features, which are the intensity in the cube including multiple elevations and gates corresponding to the location of bottom labels. Two loss functions are applied to compile the network: one is the common mean square error (MSE), and the other is a self-defined loss function that increases the weight of strong echoes. Considering that the radar beam broadens with distance and height, the 0.5° elevation scan is divided into six range bands every 25 km to train different models. The models are evaluated by three indicators: explained variance (EVar), mean absolute error (MAE), and correlation coefficient (CC). Two cases are demonstrated to compare the effect of the echo-filling model by different loss functions. The results suggest that EFnet can effectively correct the echo reflectivity and improve the data quality in the occlusion area, and there are better results for strong echoes when the self-defined loss function is used.

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