Detection of Undocumented Changepoints Using Multiple Test Statistics and Composite Reference Series.

By M. Menne, C. Naomie Williams. 2005

158 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Global and Planetary Change, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d300dd96a7a0cafa7a671bba87cf983076a97d79

How researchers see this paper

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

In Economics and Econometrics

1. Xiaolan L. Wang
2. P. Domonkos
3. M. Menne
4. C. Naomie Williams
5. Yang Feng
6. Colin M. Gallagher
7. Konstantinos Fokianos
8. David Gabauer

In Global and Planetary Change

1. Xiaolan L. Wang
2. P. Domonkos
3. M. Menne
4. C. Naomie Williams
5. Yang Feng
6. Colin M. Gallagher
7. Yuehua Wu
8. L. Haimberger

In Statistics and Probability

1. Xiaolan L. Wang
2. M. Menne
3. C. Naomie Williams
4. P. Domonkos
5. Yang Feng
6. Q. Lu
7. Robert B. Lund
8. Jaxk Reeves

Citations in the corpus

• Efficiency evaluation for detecting inhomogeneities by objective homogenisation methods (2011, result): “Pw-Fa characteristics in Mestre et al. (2008) are partly similar to the results shown in Fig.”
• Accounting for Autocorrelation in Detecting Mean Shifts in Climate Data Series Using the Penalized Maximal t or F Test (2008, background): “Therefore, more and more effort has been devoted to detecting and adjusting for artificial shifts in climate data series (e.g., Vincent et al. 2002; Menne and Williams 2005; DeGaetano 2006; Wang 2006; Wan et al. 2007; among others), and several statistical methods have been proposed for detecting…”
• Homogenization of Temperature Series via Pairwise Comparisons (2009, methodology): “In addition, the algorithm employs a recursive testing strategy to resolve multiple undocumented changepoints within a single time series (Menne and Williams 2005, hereafter MW05).”
• Penalized Maximal t Test for Detecting Undocumented Mean Change in Climate Data Series (2007, unknown): “However, note that one can implement statistical tests that are developed for the AMOC case with an appropriate recursive testing algorithm to detect multiple changepoints in a time series, as in Wang (2007), Menne and Williams (2005), and Wang and Feng (2004).”
• Penalized Maximal F Test for Detecting Undocumented Mean Shift without Trend Change (2008, methodology): “However, it is generally common practice to implement statistical tests that are developed for the AMOC case with an appropriate stepwise testing algorithm to detect multiple changepoints in a climate data time series, as in Wang (2008), Menne and Williams (2005), and Wang and Feng (2007).”
• Changepoint Detection in Periodic and Autocorrelated Time Series (2007, methodology): “In practice, climatologists typically apply AMOC methods recursively to series subsegments to detect multiple changepoints (see Wang 2006; Menne and Williams 2005; Wang and Feng 2007).”