A New Family of Divergences Originating From Model Adequacy Tests and Application to Robust Statistical Inference

Minimum divergence methods are popular tools in a variety of statistical applications. We consider tubular model adequacy tests, and demonstrate that the new divergences that are generated in the process are very useful in robust statistical inference. In particular, we show that the family of <i...

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Vydané v:IEEE transactions on information theory Ročník 64; číslo 8; s. 5581 - 5591
Hlavní autori: Ghosh, Abhik, Basu, Ayanendranath
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.08.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Adequacy
data models
Density measurement
Divergence
Estimation
Failure analysis
Function analysis
generalized S-divergence
Influence functions
Light rail systems
minimization of divergences
model adequacy test
Model testing
Optimization
Parameter estimation
Robustness
Statistical inference
Superluminescent diodes
Systematics
ISSN:0018-9448, 1557-9654
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Shrnutí:Minimum divergence methods are popular tools in a variety of statistical applications. We consider tubular model adequacy tests, and demonstrate that the new divergences that are generated in the process are very useful in robust statistical inference. In particular, we show that the family of <inline-formula> <tex-math notation="LaTeX">S </tex-math></inline-formula>-divergences can be alternatively developed using the tubular model adequacy tests; a further application of the paradigm generates a larger superfamily of divergences. We describe the properties of this larger class and its potential applications in robust inference. Along the way, the failure of the first order influence function analysis in capturing the robustness of these procedures is also established.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:0018-9448
1557-9654
DOI:10.1109/TIT.2018.2794537