scholarly journals Normal approximations for wavelet coefficients on spherical Poisson fields

2014 ◽  
Vol 409 (1) ◽  
pp. 212-227 ◽  
Author(s):  
Claudio Durastanti ◽  
Domenico Marinucci ◽  
Giovanni Peccati
Keyword(s):  
Wavelet Coefficients ◽  
Normal Approximations ◽  
Poisson Fields

Normal approximations to discrete unimodal distributions

1986 ◽  
Vol 23 (04) ◽  
pp. 1013-1018
Author(s):  
B. G. Quinn ◽  
H. L. MacGillivray
Keyword(s):  
Stationary Distribution ◽  
Sufficient Conditions ◽  
Random Variables ◽  
Hypergeometric Distribution ◽  
Death Process ◽  
Normal Approximations ◽  
Discrete Random Variables ◽  
Birth Death

Sufficient conditions are presented for the limiting normality of sequences of discrete random variables possessing unimodal distributions. The conditions are applied to obtain normal approximations directly for the hypergeometric distribution and the stationary distribution of a special birth-death process.

scholarly journals Bayesian modeling and significant features exploration in wavelet power spectra

2007 ◽  
Vol 14 (1) ◽  
pp. 79-88 ◽  
Author(s):  
D. V. Divine ◽  
F. Godtliebsen
Keyword(s):  
Data Analysis ◽  
Traditional Approach ◽  
Statistical Significance ◽  
Power Spectra ◽  
Simulated Data ◽  
Scale Space ◽  
Independent Random Variables ◽  
Discrete Wavelet ◽  
Wavelet Coefficients ◽  
Testing Procedures

Abstract. This study proposes and justifies a Bayesian approach to modeling wavelet coefficients and finding statistically significant features in wavelet power spectra. The approach utilizes ideas elaborated in scale-space smoothing methods and wavelet data analysis. We treat each scale of the discrete wavelet decomposition as a sequence of independent random variables and then apply Bayes' rule for constructing the posterior distribution of the smoothed wavelet coefficients. Samples drawn from the posterior are subsequently used for finding the estimate of the true wavelet spectrum at each scale. The method offers two different significance testing procedures for wavelet spectra. A traditional approach assesses the statistical significance against a red noise background. The second procedure tests for homoscedasticity of the wavelet power assessing whether the spectrum derivative significantly differs from zero at each particular point of the spectrum. Case studies with simulated data and climatic time-series prove the method to be a potentially useful tool in data analysis.

Normal approximations for distributions arising in the stochastic approach to chemical reaction kinetics

1981 ◽  
Vol 18 (01) ◽  
pp. 263-267 ◽  
Author(s):  
F. D. J. Dunstan ◽  
J. F. Reynolds
Keyword(s):  
Reaction Kinetics ◽  
Chemical Reactions ◽  
Chemical Reaction ◽  
Stochastic Approach ◽  
Algebraic Functions ◽  
Chemical Reaction Kinetics ◽  
Normal Approximations ◽  
Formal Solutions

Earlier stochastic analyses of chemical reactions have provided formal solutions which are unsuitable for most purposes in that they are expressed in terms of complex algebraic functions. Normal approximations are derived here for solutions to a variety of reactions. Using these, it is possible to investigate the level at which the classical deterministic solutions become inadequate. This is important in fields such as radioimmunoassay.

scholarly journals Optimal Selection of Wavelet Coefficients for Electrocardiograph Compression

ETRI Journal ◽  
2007 ◽  
Vol 29 (4) ◽  
pp. 530-532 ◽  
Author(s):  
María del Mar Elena ◽  
Jose Manuel Quero ◽  
Inmaculada Borrego
Keyword(s):  
Optimal Selection ◽  
Wavelet Coefficients ◽  
Selection Of

scholarly journals Mechanical Vibration Signal Denoising Using Quantum-Inspired Standard Deviation Based on Subband Based Gaussian Mixture Model

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Aidong Xu ◽  
Wenqi Huang ◽  
Peng Li ◽  
Huajun Chen ◽  
Jiaxiao Meng ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Mechanical Vibration ◽  
Vibration Signal ◽  
Gaussian Mixture ◽  
Wavelet Coefficients ◽  
Denoising Method ◽  
Reduction Effect ◽  
Thresholding Function

Aiming at improving noise reduction effect for mechanical vibration signal, a Gaussian mixture model (SGMM) and a quantum-inspired standard deviation (QSD) are proposed and applied to the denoising method using the thresholding function in wavelet domain. Firstly, the SGMM is presented and utilized as a local distribution to approximate the wavelet coefficients distribution in each subband. Then, within Bayesian framework, the maximum a posteriori (MAP) estimator is employed to derive a thresholding function with conventional standard deviation (CSD) which is calculated by the expectation-maximization (EM) algorithm. However, the CSD has a disadvantage of ignoring the interscale dependency between wavelet coefficients. Considering this limit for the CSD, the quantum theory is adopted to analyze the interscale dependency between coefficients in adjacent subbands, and the QSD for noise-free wavelet coefficients is presented based on quantum mechanics. Next, the QSD is constituted for the CSD in the thresholding function to shrink noisy coefficients. Finally, an application in the mechanical vibration signal processing is used to illustrate the denoising technique. The experimental study shows the SGMM can model the distribution of wavelet coefficients accurately and QSD can depict interscale dependency of wavelet coefficients of true signal quite successfully. Therefore, the denoising method utilizing the SGMM and QSD performs better than others.

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