Statistical approach to detection of signals by Monte Carlo singular spectrum analysis: Multiple testing

03/04/2019
by   Nina Golyandina, et al.
0

The statistical approach to detection of a signal in noisy series is considered in the framework of Monte Carlo singular spectrum analysis. This approach contains a technique to control both type I and type II errors and also compare criteria. For simultaneous testing of multiple frequencies, a multiple version of MC-SSA is suggested to control the family-wise error rate.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/01/2019

Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits

Monte Carlo (MC) permutation testing is considered the gold standard for...
research
03/11/2022

Combining Multiple Testing with Multivariate Singular Spectrum Analysis

Appropriate preprocessing is a fundamental prerequisite for analyzing a ...
research
06/07/2016

Reducing the error of Monte Carlo Algorithms by Learning Control Variates

Monte Carlo (MC) sampling algorithms are an extremely widely-used techni...
research
06/13/2020

Numerical analysis for inchworm Monte Carlo method: Sign problem and error growth

We consider the numerical analysis of the inchworm Monte Carlo method, w...
research
08/18/2020

On dropping the first Sobol' point

Quasi-Monte Carlo (QMC) points are a substitute for plain Monte Carlo (M...
research
12/28/2022

Choosing the Number of Topics in LDA Models – A Monte Carlo Comparison of Selection Criteria

Selecting the number of topics in LDA models is considered to be a diffi...

Please sign up or login with your details

Forgot password? Click here to reset