Surrogate Data Test on Time Series

By Dimitris Kugiumtzis. 2002

56 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/3e76c8e8490ef6c5d8ee4b7deedb8c79ba4af792

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Dmytro Iatsenko
2. Aneta Stefanovska
3. Valentina Ticcinelli
4. Aleksandra Pidde
5. Gemma Lancaster

In Economics and Econometrics

1. Dmytro Iatsenko
2. Aneta Stefanovska
3. Valentina Ticcinelli
4. Aleksandra Pidde
5. Gemma Lancaster

In Statistical and Nonlinear Physics

1. Dmytro Iatsenko
2. Aneta Stefanovska
3. Valentina Ticcinelli
4. Aleksandra Pidde
5. Gemma Lancaster

Citations in the corpus

• Surrogate data for hypothesis testing of physical systems (2018, methodology): “The author concludes that STAP surrogates, being a typical realization approach, have less power, i.e. may be more conservative, than the constrained realization approaches of AAFT and IAAFT when not using a pivotal test statistic [105].”