scholarly journals Estimation of a delta-contaminated density of a random intensity of Poisson data

2016 ◽  
Vol 10 (1) ◽  
pp. 683-705
Author(s):  
Daniela De Canditiis ◽  
Marianna Pensky
2021 ◽  
Vol 11 (10) ◽  
pp. 4524
Author(s):  
Victor Getmanov ◽  
Vladislav Chinkin ◽  
Roman Sidorov ◽  
Alexei Gvishiani ◽  
Mikhail Dobrovolsky ◽  
...  

Problems of digital processing of Poisson-distributed data time series from various counters of radiation particles, photons, slow neutrons etc. are relevant for experimental physics and measuring technology. A low-pass filtering method for normalized Poisson-distributed data time series is proposed. A digital quasi-Gaussian filter is designed, with a finite impulse response and non-negative weights. The quasi-Gaussian filter synthesis is implemented using the technology of stochastic global minimization and modification of the annealing simulation algorithm. The results of testing the filtering method and the quasi-Gaussian filter on model and experimental normalized Poisson data from the URAGAN muon hodoscope, that have confirmed their effectiveness, are presented.


2021 ◽  
pp. 001316442199253
Author(s):  
Robert C. Foster

This article presents some equivalent forms of the common Kuder–Richardson Formula 21 and 20 estimators for nondichotomous data belonging to certain other exponential families, such as Poisson count data, exponential data, or geometric counts of trials until failure. Using the generalized framework of Foster (2020), an equation for the reliability for a subset of the natural exponential family have quadratic variance function is derived for known population parameters, and both formulas are shown to be different plug-in estimators of this quantity. The equivalent Kuder–Richardson Formulas 20 and 21 are given for six different natural exponential families, and these match earlier derivations in the case of binomial and Poisson data. Simulations show performance exceeding that of Cronbach’s alpha in terms of root mean square error when the formula matching the correct exponential family is used, and a discussion of Jensen’s inequality suggests explanations for peculiarities of the bias and standard error of the simulations across the different exponential families.


2002 ◽  
Vol 56 (2) ◽  
pp. 165-178 ◽  
Author(s):  
B. A. Mair ◽  
Murali Rao ◽  
J. M. M. Anderson

2007 ◽  
Vol 18 (1) ◽  
pp. 27-40 ◽  
Author(s):  
Dimitris Karlis ◽  
Panagiotis Tsiamyrtzis

2014 ◽  
Vol 52 (3) ◽  
pp. 397-413 ◽  
Author(s):  
Luca Zanni ◽  
Alessandro Benfenati ◽  
Mario Bertero ◽  
Valeria Ruggiero

1985 ◽  
Vol 29 (03) ◽  
pp. 170-188
Author(s):  
G. Ferro ◽  
A. E. Mansour

The success of implementing reliability analysis in structural design depends to a large extent on the ability to combine the loads acting on the structure, and on extrapolating their magnitudes to obtain the extreme value of the total combined load. In this paper, a new theory is proposed to combine the slamming and wave-induced responses of a ship moving in irregular seas. The slamming and wave-induced responses are both considered as stochastic processes, and the properties of the combined response are determined on that basis. The slamming loads alone are considered as a train of impulses of random intensity and random arrival time as has been shown by Mansour and Lozow [1],3 but the dependence between the intensity and arrival time is considered in the stochastic modeling. The extreme value of the combined response is then investigated for use in design applications. An example of application to a cargo ship is given and a sensitivity analysis is conducted to determine how sensitive the results are to some of the important input parameters.


It is a well-known fact that when a camera or other imaging system captures an image, often, the vision system for which it is captured cannot implement it directly. There may be several reasons behind this fact such as there can exist random intensity variation in the image. There can also be illumination variation in the image or poor contrast. These drawbacks must be tackled at the primitive stages for optimum vision processing. This chapter will discuss different filtering approaches for this purpose. The chapter begins with the Gaussian filter, followed by a brief review of different often used approaches. Moreover, this chapter will also render different filtering approaches including their hardware architectures.


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