An overview of statistical‐model‐based techniques for audio restoration

2004 ◽  
Vol 115 (5) ◽  
pp. 2493-2493
Author(s):  
Patrick J. Wolfe
Keyword(s):  
Statistical Model ◽  
Model Based ◽  
Audio Restoration

Statistical Model-Based Approaches to Audio Restoration and Analysis

2001 ◽  
Vol 30 (4) ◽  
pp. 323-338 ◽  
Author(s):  
Simon J. Godsill ◽  
Patrick J. Wolfe ◽  
William N.W. Fong
Keyword(s):  
Statistical Model ◽  
Model Based ◽  
Audio Restoration

scholarly journals On the Accuracy of the Sine Power Lomax Model for Data Fitting

Modelling ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 78-104
Author(s):  
Vasili B. V. Nagarjuna ◽  
R. Vishnu Vardhan ◽  
Christophe Chesneau
Keyword(s):  
Statistical Model ◽  
Simulation Study ◽  
Statistical Models ◽  
Goodness Of Fit ◽  
Data Fitting ◽  
Applied Science ◽  
Survival Distribution ◽  
Lomax Distribution ◽  
Model Based

Every day, new data must be analysed as well as possible in all areas of applied science, which requires the development of attractive statistical models, that is to say adapted to the context, easy to use and efficient. In this article, we innovate in this direction by proposing a new statistical model based on the functionalities of the sinusoidal transformation and power Lomax distribution. We thus introduce a new three-parameter survival distribution called sine power Lomax distribution. In a first approach, we present it theoretically and provide some of its significant properties. Then the practicality, utility and flexibility of the sine power Lomax model are demonstrated through a comprehensive simulation study, and the analysis of nine real datasets mainly from medicine and engineering. Based on relevant goodness of fit criteria, it is shown that the sine power Lomax model has a better fit to some of the existing Lomax-like distributions.

Diffusion-weighted imaging of prostate cancer using a statistical model based on the gamma distribution

2014 ◽  
Vol 42 (1) ◽  
pp. 56-62 ◽  
Author(s):  
Hiroshi Shinmoto ◽  
Koichi Oshio ◽  
Chiharu Tamura ◽  
Shigeyoshi Soga ◽  
Teppei Okamura ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Statistical Model ◽  
Gamma Distribution ◽  
Diffusion Weighted Imaging ◽  
Model Based ◽  
Diffusion Weighted

A simple method for surface radiation estimating using FY-4A data

2021 ◽  
Author(s):  
Lijuan Wang ◽  
Hongchao Zuo ◽  
Wei Wang
Keyword(s):  
Statistical Model ◽  
Pso Algorithm ◽  
Surface Radiation ◽  
Shortwave Radiation ◽  
Wave Radiation ◽  
Percentage Error ◽  
Simple Method ◽  
Model Based ◽  
Geostationary Meteorological Satellite ◽  
Moderate Resolution Imaging Spectroradiometer

AbstractFY-4A is a geostationary meteorological satellite with four advanced payloads, which can be used to quantitatively detect the earth's atmospheric system with multi spectral and high spatial-temporal resolution. However, the applicable model limits the application of the FY-4A satellite data. In this paper, the empirical statistical model developed for the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor is extended for FY-4A Advanced Geosynchronous Radiation Imager (AGRI), and it is applied to observed data to evaluate the applicability of the model for AGRI measurements. To improve the accuracy of radiation estimation, the artificial intelligent particle swarm optimization (PSO) algorithm was used for model optimizing. Results show that the estimated radiation has diurnal variation, which accords with the characteristics of radiation variation. The estimated net surface shortwave radiation (Sn) and observed values show good correlation. However, large deviations from observations are found in the estimated values when the empirical model based on MODIS is directly used to process AGRI data. Thus, the empirical statistical model based on MODIS can be applied to AGRI data, but the empirical parameters need to be revised. Optimization of the empirical statistical model by the PSO algorithm can effectively improve the accuracy of radiation estimate. The Mean absolute percentage error (MAPE) of Sn estimated by optimized models is reduced to 15%. The MAPE of the net surface long-wave radiation (Ln) estimated by optimized models is reduced to 31%, and the MAPE of the net radiation (Rn) estimated by optimized models is reduced to 27%. However, for the uncertainty caused by error accumulation effect, the influence of PSO optimization on Rn is not as obvious as that of Ln. However, from the analysis of error distribution, it shows that PSO optimization does improve the estimation results of Rn. Based on AGRI data, the surface radiation can be estimated simply, and the regional or larger scale surface radiation retrieval can quickly realize by this method which has large application potential and popularization value.

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