scholarly journals Aliasing Error for Sampling Series Derivatives

2014 ◽  
Vol 13 (1) ◽  
pp. 1-20
Author(s):  
R. M. Asharabi
Keyword(s):  
Aliasing Error ◽  
Sampling Series

Aliasing Error of Sampling Series in Wavelet Subspaces

2008 ◽  
Vol 29 (1-2) ◽  
pp. 126-144 ◽  
Author(s):  
A. G. García ◽  
J. M. Kim ◽  
K. H. Kwon ◽  
G. Pérez-Villalón
Keyword(s):  
Aliasing Error ◽  
Sampling Series ◽  
Wavelet Subspaces

Classical Sampling Series of Band Limited Functions: Oversampling and Non-existence

2016 ◽  
Vol 15 (1) ◽  
pp. 131-138
Author(s):  
B. A. Bailey ◽  
W. R. Madych
Keyword(s):  
Sampling Series ◽  
Band Limited

A Modification of the Sampling Series with a Gaussian Multiplier

2006 ◽  
Vol 5 (1) ◽  
pp. 2-20
Author(s):  
Qian Liwen ◽  
Dennis B. Creamer
Keyword(s):  
Sampling Series

scholarly journals Vector sampling expansion in Riesz bases setting and its aliasing error

2008 ◽  
Vol 25 (3) ◽  
pp. 315-334 ◽  
Author(s):  
J.M. Kim ◽  
K.H. Kwon
Keyword(s):  
Riesz Bases ◽  
Aliasing Error

scholarly journals Translating surveillance data into incidence estimates

2018 ◽  
Author(s):  
Yoann Bourhis ◽  
Timothy R. Gottwald ◽  
Frank van den Bosch
Keyword(s):  
Disease Incidence ◽  
Surveillance Data ◽  
Estimation Methods ◽  
Full Model ◽  
Estimation Model ◽  
Sampling Series ◽  
Wide Range ◽  
Incidence Estimation

AbstractMonitoring a population for a disease requires the hosts to be sampled and tested for the pathogen. This results in sampling series from which to estimate the disease incidence,i.e. the proportion of hosts infected. Existing estimation methods assume that disease incidence is not changing between monitoring rounds, resulting in underestimation of the disease incidence. In this paper we develop an incidence estimation model accounting for epidemic growth with monitoring rounds sampling varying incidence. We also show how to accommodate the asymptomatic period characteristic to most diseases. For practical use, we produce an approximation of the model, which is subsequently shown accurate for relevant epidemic and sampling parameters. Both the approximation and the full model are applied to stochastic spatial simulations of epidemics. The results prove their consistency for a very wide range of situations.

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