Accounting for Autocorrelation in Detecting Mean Shifts in Climate Data Series Using the Penalized Maximal t or F Test

394 citations recorded by the source corpus.

How researchers see this paper

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

Homogenization of precipitation time series with ACMANT (2015, methodology): “and Moberg 1997), penalized maximum t test (PMT) of RHTest (Wang 2008) and Climatol (www.”
Benchmarking homogenization algorithms for monthly data (2012, neutral): “The exception is absolute homogenization (PMFred abs) [23], which typically makes the benchmark data more inhomogeneous for both monthly and yearly mean values.”
Penalized Maximal F Test for Detecting Undocumented Mean Shift without Trend Change (2008, methodology): “Recently, two stepwise algorithms have been developed to empirically account for serial correlation in detecting multiple changepoints using PMT or PMFT (Wang 2008).”
New Techniques for the Detection and Adjustment of Shifts in Daily Precipitation Data Series (2010, methodology): “Note that the distribution function of Fmax is complicated and unknown; hence, the Fmax threshold values are estimated through Monte Carlo simulations (Lund and Reeves 2002; Wang 2003).”
A New Approach to Homogenize Daily Radiosonde Humidity Data (2011, methodology): “Testing for a mean shift at an unknown (or undocumented) location is different from testing for a mean shift at a known location because the critical value for the former is much larger than for the latter (Wang 2008a,b).”