Robust regression in R

Robust statistics philosopy

Robust regression

R ressources

Examples

Bibliography

References

Robust regression in R

Eva Cantoni

Research Center for Statistics and Geneva School of Economics and Management,

University of Geneva, Switzerland

April 4th, 2017

Robust statistics philosopy

Robust regression

R ressources

Examples

Bibliography

References

1 Robust statistics philosopy 2 Robust regression 3 R ressources 4 Examples 5 Bibliography

Robust statistics philosopy

Robust regression

R ressources

Examples

Against what is robust statistics robust?

Bibliography

References

Robust Statistics aims at producing consistent and possibly efficient estimators and test statistics with stable level when the model is slightly misspecified.

Model misspecification encompasses a relatively large set of possibilities, and robust statistics cannot deal with all types of model misspecifications.

By "slight model misspecification", we suppose that the data generating process lies in a neighborhood of the true (postulated) model, the one that is considered as "useful" for the problem under investigation.

Robust statistics philosopy

Robust regression

R ressources

Examples

Against what is robust statistics robust?

Bibliography

References

This neighborhood is formalized as

F = (1 - )F + G ,

(1)

? F is the postulated model, ? is a set of parameters of interest, ? G is an arbitrary distribution and ? 0 1 captures "the amount of model misspecification"

Robust statistics philosopy

Robust regression

R ressources

Examples

Against what is robust statistics robust?

Bibliography

References

Inference G arbitrary G = z G = F(, ) G such that F = F(, )

arbitrary G = z G = F(, ) G such that F = F(, )

Classical

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