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Comparing two samples by penalized logistic regression

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Comparing two samples by penalized logistic regression. / Fokianos, K.

In: Electronic Journal of Statistics, Vol. 2, 2008, p. 564-580.

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Fokianos, K 2008, 'Comparing two samples by penalized logistic regression', Electronic Journal of Statistics, vol. 2, pp. 564-580. https://doi.org/10.1214/07-EJS078

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Fokianos, K. / Comparing two samples by penalized logistic regression. In: Electronic Journal of Statistics. 2008 ; Vol. 2. pp. 564-580.

Bibtex

@article{e4a59dd21d9a4f439e7999bfca727526,
title = "Comparing two samples by penalized logistic regression",
abstract = "Inference based on the penalized density ratio model is proposed and studied. The model under consideration is specified by assuming that the log–likelihood function of two unknown densities is of some parametric form. The model has been extended to cover multiple samples problems while its theoretical properties have been investigated using large sample theory. A main application of the density ratio model is testing whether two, or more, distributions are equal. We extend these results by arguing that the penalized maximum empirical likelihood estimator has less mean square error than that of the ordinary maximum likelihood estimator, especially for small samples. In fact, penalization resolves any existence problems of estimators and a modified Wald type test statistic can be employed for testing equality of the two distributions. A limited simulation study supports further the theory.",
keywords = "Empirical likelihood , biased sampling, penalty , semiparametric , shrinkage, mean square error , power",
author = "K. Fokianos",
year = "2008",
doi = "10.1214/07-EJS078",
language = "English",
volume = "2",
pages = "564--580",
journal = "Electronic Journal of Statistics",
issn = "1935-7524",
publisher = "Institute of Mathematical Statistics",

}

RIS

TY - JOUR

T1 - Comparing two samples by penalized logistic regression

AU - Fokianos, K.

PY - 2008

Y1 - 2008

N2 - Inference based on the penalized density ratio model is proposed and studied. The model under consideration is specified by assuming that the log–likelihood function of two unknown densities is of some parametric form. The model has been extended to cover multiple samples problems while its theoretical properties have been investigated using large sample theory. A main application of the density ratio model is testing whether two, or more, distributions are equal. We extend these results by arguing that the penalized maximum empirical likelihood estimator has less mean square error than that of the ordinary maximum likelihood estimator, especially for small samples. In fact, penalization resolves any existence problems of estimators and a modified Wald type test statistic can be employed for testing equality of the two distributions. A limited simulation study supports further the theory.

AB - Inference based on the penalized density ratio model is proposed and studied. The model under consideration is specified by assuming that the log–likelihood function of two unknown densities is of some parametric form. The model has been extended to cover multiple samples problems while its theoretical properties have been investigated using large sample theory. A main application of the density ratio model is testing whether two, or more, distributions are equal. We extend these results by arguing that the penalized maximum empirical likelihood estimator has less mean square error than that of the ordinary maximum likelihood estimator, especially for small samples. In fact, penalization resolves any existence problems of estimators and a modified Wald type test statistic can be employed for testing equality of the two distributions. A limited simulation study supports further the theory.

KW - Empirical likelihood

KW - biased sampling

KW - penalty

KW - semiparametric

KW - shrinkage

KW - mean square error

KW - power

U2 - 10.1214/07-EJS078

DO - 10.1214/07-EJS078

M3 - Journal article

VL - 2

SP - 564

EP - 580

JO - Electronic Journal of Statistics

JF - Electronic Journal of Statistics

SN - 1935-7524

ER -