Generalized M‐fluctuation tests for parameter instability

By Achim Zeileis, Kurt Hornik. 2007

204 citations recorded by the source corpus.

Subfields: Condensed Matter Physics, Economics and Econometrics, Finance

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Citations in the corpus

Generalized random forests (2016, methodology): “…at detected change-points in θ(x); and, from this perspective, our approach is closely related to standard techniques for moment-based change-point detection (Andrews, 1993; Hansen, 1992; Hjort and Koning, 2002; Nyblom, 1989; Ploberger and Krämer, 1992; Zeileis, 2005; Zeileis and Hornik, 2007).”
A Unified Approach to Structural Change Tests Based on ML Scores, F Statistics, and OLS Residuals (2005, methodology): “Recently, Hjort and Koning (2002) suggested a general class of ML score-based structural change tests (without mentioning explicitely that this generalizes the Nyblom–Hansen test).”
Gaining insight with recursive partitioning of generalized linear models (2013, methodology): “As mentioned above, the parameter stability of the individual score function contributions with respect to the splitting variable Z j is assessed by means of generalized M-ﬂuctuation tests (Zeileis and Hornik 2007) for any ordering of the values of Z j , σ ( Z ij ).”
Model-Based Recursive Partitioning (2008, methodology): “If there is parameter instability with respect to one partitioning variable, it will be picked up (for large enough n ) because all tests used are consistent (at rate √ n , see Zeileis and Hornik 2007).”
Generalized Measurement Invariance Tests with Application to Factor Analysis (2011, methodology): “The asymptotic distribution of this test statistic is then easily obtained by applying the same aggregation to the asymptotic process B 0 (Hjort and Koning 2002; Zeileis and Hornik 2007), so that corresponding p values can be derived.”
Distributional regression forests for probabilistic precipitation forecasting in complex terrain (2018, methodology): “Alternatively, one could use asymptotic M-fluctuation tests for parameter instability (Zeileis and Hornik 2007) as in modelbased recursive partitioning (MOB, Zeileis, Hothorn, and Hornik 2008).”
Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare (2022, background): “Furthermore, one can consider a broader class of so-called generalized M-ﬂ uctuation tests that gives the user more ﬂ exibility in deciding which metrics to track 51 .”
Accounting for Individual Differences in Bradley-Terry Models by Means of Recursive Partitioning (2011, methodology): “Under the null hypothesis of parameter stability, the cumulative sum process W ‘ ð(cid:4)Þ can be shown to converge to a k -dimensional Brownian bridge (Zeileis & Hornik, 2007), which can be used as the basis for statistical inference.”