Hide-and-Seek with time-series filters: a model-based Monte Carlo study

2019 ◽  
Vol 59 (5) ◽  
pp. 2335-2361
Author(s):  
Vadim Kufenko
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Monte Carlo Study ◽  
Model Based

scholarly journals INFERENCE ON NONSTATIONARY TIME SERIES WITH MOVING MEAN

2014 ◽  
Vol 32 (2) ◽  
pp. 431-457 ◽  
Author(s):  
Jiti Gao ◽  
Peter M. Robinson
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Monte Carlo Study ◽  
Semiparametric Model ◽  
Nonstationary Time Series ◽  
Finite Sample ◽  
Trend Function ◽  
Deterministic Trend ◽  
Short Memory ◽  
Memory Parameter

A semiparametric model is proposed in which a parametric filtering of a nonstationary time series, incorporating fractionally differencing with short memory correction, removes correlation but leaves a nonparametric deterministic trend. Estimates of the memory parameter and other dependence parameters are proposed, and shown to be consistent and asymptotically normally distributed with parametric rate. Tests with standard asymptotics for I(1) and other hypotheses are thereby justified. Estimation of the trend function is also considered. We include a Monte Carlo study of finite-sample performance.

Correction for scatter and septal penetration using convolution subtraction methods and model-based compensation in123I brain SPECT imaging—a Monte Carlo study

2006 ◽  
Vol 51 (22) ◽  
pp. 5753-5767 ◽  
Author(s):  
Anne Larsson ◽  
Michael Ljungberg ◽  
Susanna Jakobson Mo ◽  
Katrine Riklund ◽  
Lennart Johansson
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Study ◽  
Brain Spect ◽  
Spect Imaging ◽  
Model Based ◽  
Septal Penetration
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