Sequential block bootstrap in a Hilbert space with application to change point analysis

By Olimjon Sh. Sharipov, J. Tewes, Martin Wendler. 2014

69 citations recorded by the source corpus.

A new test for structural changes in functional data is investigated. It is based on Hilbert space theory and critical values are deduced from bootstrap iterations. Thus a new functional central limit theorem for the block bootstrap in a Hilbert space is required. The test can also be used to detect changes in the marginal distribution of random vectors, which is supplemented by a simulation study. Our methods are applied to hydrological data from Germany. The Canadian Journal of Statistics 44: 300–322; 2016 © 2016 Statistical Society of Canada

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/37fadc4e786f1c913fa4d74ceb47be8f71fd509b

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In Artificial Intelligence

1. Stanislav Volgushev
2. Kevin Kokot
3. Holger Dette
4. Thomas Kneib
5. Matt Taddy
6. Athanasios Kottas

In Finance

1. Stanislav Volgushev
2. Kevin Kokot
3. Holger Dette
4. Johan Segers
5. Jean-David Fermanian

In Statistics and Probability

1. Stanislav Volgushev
2. Kevin Kokot
3. Holger Dette
4. Johan Segers
5. Thomas Kneib
6. I. Van Keilegom
7. Natalie Neumeyer
8. Matt Taddy

Citations in the corpus

• Testing relevant hypotheses in functional time series via self‐normalization (2018, methodology): “One hydrological year (different definitions are possible but we consider the same as Sharipov et al. (2014)) starts at the first of November and ends at the 31-th of October which means that we consider the hydrological years 1910-2014. Note that Sharipov et al. (2014) considered the years 1910-201”