Towards data driven selection of a penalty function for data driven Neyman tests

@inproceedings{Inglot2004TowardsDD,
  title={Towards data driven selection of a penalty function for data driven Neyman tests},
  author={Tadeusz Inglot and Teresa Ledwina},
  year={2004}
}
The data driven Neyman statistic consists of two elements: a score statistic in a finite dimensional submodel and a selection rule to determine the best fitted submodel. For instance, Schwarz BIC and Akaike AIC rules are often applied in such constructions. For moderate sample sizes AIC is sensitive in detecting complex models, while BIC works well for relatively simple structures. When the sample size is moderate, the choice of selection rule for determining a best fitted model from a number… CONTINUE READING

Citations

Publications citing this paper.

References

Publications referenced by this paper.
Showing 1-10 of 37 references

The penalty in data driven Neyman’s

W.C.M. Kallenberg
tests, Math. Methods Statist • 2002
View 7 Excerpts
Highly Influenced

Smooth test’ for goodness of fit

J. Neyman
Skand. Aktuarietidskr • 1937
View 9 Excerpts
Highly Influenced

Testing lack of fit in multiple regression, Biomertika

M. Aerts, G. Claeskens, J. D. Hart
2000
View 10 Excerpts
Highly Influenced

Ledwina, Intermediate approach to comparison of some goodness-of-fit tests, Ann

T. T. Inglot
Inst. Statist. Math • 2001
View 11 Excerpts
Highly Influenced

Generalized intermediate efficiency of goodness-of-fit tests, Math

T. Inglot
Methods Statist • 1999
View 10 Excerpts
Highly Influenced

Ledwina, Data driven smooth tests for composite hypotheses: Comparison of powers

T.W.C.M. Kallenberg
J. Statist. Comput. Simulation • 1997
View 6 Excerpts
Highly Influenced

Test of significance based on wavelet thresholding and Neyman’s truncation

J. Fan
J. Amer. Statist. Assoc • 1996
View 10 Excerpts
Highly Influenced

Data driven versions of Pearson’s chi-square test for uniformity

M. Bogdan
J. Statist. Comput. Simulation • 1995
View 10 Excerpts
Highly Influenced

Similar Papers

Loading similar papers…