NDLI: A fast robust method for fitting gamma distributions

Content Provider	Springer Nature Link
Author	Riley, Geoff Clarke, Brenton R. McKinn, Peter L.
Copyright Year	2011
Abstract	The art of fitting gamma distributions robustly is described. In particular we compare methods of fitting via minimizing a Cramér Von Mises distance, an L 2 minimum distance estimator, and fitting a B-optimal M-estimator. After a brief prelude on robust estimation explaining the merits in terms of weak continuity and Fréchet differentiability of all the aforesaid estimators from an asymptotic point of view, a comparison is drawn with classical estimation and fitting. In summary, we give a practical example where minimizing a Cramér Von Mises distance is both efficacious in terms of efficiency and robustness as well as being easily implemented. Here gamma distributions arise naturally for “in control” representation indicators from measurements of spectra when using fourier transform infrared (FTIR) spectroscopy. However, estimating the in-control parameters for these distributions is often difficult, due to the occasional occurrence of outliers.
Ending Page	1014
Page Count	14
Starting Page	1001
File Format	PDF
ISSN	09325026
e-ISSN	16139798
Journal	Statistical Papers
Issue Number	4
Volume Number	53
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-09-30
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Gamma distributions Data analysis Fréchet differentiability Minimum distance estimation Robustness and adaptive procedures Statistics for Business/Economics/Mathematical Finance/Insurance Operations Research/Decision Theory Probability Theory and Stochastic Processes Economic Theory Robust estimation Weak continuity
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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