Penalized Maximal F Test for Detecting Undocumented Mean Shift without Trend Change

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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).”
Accounting for Autocorrelation in Detecting Mean Shifts in Climate Data Series Using the Penalized Maximal t or F Test (2008, methodology): “Similarly, Wang (2008) proposed a penalized maximal F (PMF) test for mean shifts in time series without trend change, using an empirically constructed penalty function to even out the W-shaped FAR distribution of the common trend two-phase regression based test (TPR3; Wang 2003).”
New Techniques for the Detection and Adjustment of Shifts in Daily Precipitation Data Series (2010, methodology): “…the standard F test should be used to test for the statistical significance of the known/documented changepoint at time tc, that is, to compare the Fc statistic with its critical value from F kd,N k , a central F distribution with kd and (N 2 k) degrees of freedom (e.g., Wang 2008a).”
Recent changes of temperature extremes over Italy: an index-based analysis (2016, methodology): “An implementation of the t test and F-test is available in the R package RHtest (Wang and Feng 2010).”