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On the effect of misspecifying the density ratio model

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On the effect of misspecifying the density ratio model. / Fokianos, K.; Kaimi, I.
In: Annals of the Institute of Statistical Mathematics, Vol. 58, No. 3, 09.2006, p. 475-497.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Fokianos, K & Kaimi, I 2006, 'On the effect of misspecifying the density ratio model', Annals of the Institute of Statistical Mathematics, vol. 58, no. 3, pp. 475-497. https://doi.org/10.1007/s10463-005-0022-8

APA

Fokianos, K., & Kaimi, I. (2006). On the effect of misspecifying the density ratio model. Annals of the Institute of Statistical Mathematics, 58(3), 475-497. https://doi.org/10.1007/s10463-005-0022-8

Vancouver

Fokianos K, Kaimi I. On the effect of misspecifying the density ratio model. Annals of the Institute of Statistical Mathematics. 2006 Sept;58(3):475-497. Epub 2006 Jun 17. doi: 10.1007/s10463-005-0022-8

Author

Fokianos, K. ; Kaimi, I. / On the effect of misspecifying the density ratio model. In: Annals of the Institute of Statistical Mathematics. 2006 ; Vol. 58, No. 3. pp. 475-497.

Bibtex

@article{8ff3c1f2f027488baf529164e5aac25f,
title = "On the effect of misspecifying the density ratio model",
abstract = "The density ratio model specifies that the log-likelihood ratio of two unknown densities is of known linear form which depends on some finite dimensional parameters. The model can be broadened to allow for m-samples in a quite natural way. Estimation of both parametric and nonparametric part of the model is carried out by the method of empirical likelihood. However the assumed linear form has an impact on the estimation and testing for the parametric part. The goal of this study is to quantify the effect of choosing an incorrect linear form and its impact to inference. The issue of misspecification is addressed by embedding the unknown linear form to some parametric transformation family which yields ultimately to its identification. Simulated examples and data analysis integrate the presentation.",
keywords = "Biased sampling , Empirical likelihood , Box–Cox transformation , Mean square error , Bias, Power ",
author = "K. Fokianos and I. Kaimi",
year = "2006",
month = sep,
doi = "10.1007/s10463-005-0022-8",
language = "English",
volume = "58",
pages = "475--497",
journal = "Annals of the Institute of Statistical Mathematics",
issn = "0020-3157",
publisher = "Springer Netherlands",
number = "3",

}

RIS

TY - JOUR

T1 - On the effect of misspecifying the density ratio model

AU - Fokianos, K.

AU - Kaimi, I.

PY - 2006/9

Y1 - 2006/9

N2 - The density ratio model specifies that the log-likelihood ratio of two unknown densities is of known linear form which depends on some finite dimensional parameters. The model can be broadened to allow for m-samples in a quite natural way. Estimation of both parametric and nonparametric part of the model is carried out by the method of empirical likelihood. However the assumed linear form has an impact on the estimation and testing for the parametric part. The goal of this study is to quantify the effect of choosing an incorrect linear form and its impact to inference. The issue of misspecification is addressed by embedding the unknown linear form to some parametric transformation family which yields ultimately to its identification. Simulated examples and data analysis integrate the presentation.

AB - The density ratio model specifies that the log-likelihood ratio of two unknown densities is of known linear form which depends on some finite dimensional parameters. The model can be broadened to allow for m-samples in a quite natural way. Estimation of both parametric and nonparametric part of the model is carried out by the method of empirical likelihood. However the assumed linear form has an impact on the estimation and testing for the parametric part. The goal of this study is to quantify the effect of choosing an incorrect linear form and its impact to inference. The issue of misspecification is addressed by embedding the unknown linear form to some parametric transformation family which yields ultimately to its identification. Simulated examples and data analysis integrate the presentation.

KW - Biased sampling

KW - Empirical likelihood

KW - Box–Cox transformation

KW - Mean square error

KW - Bias

KW - Power

U2 - 10.1007/s10463-005-0022-8

DO - 10.1007/s10463-005-0022-8

M3 - Journal article

VL - 58

SP - 475

EP - 497

JO - Annals of the Institute of Statistical Mathematics

JF - Annals of the Institute of Statistical Mathematics

SN - 0020-3157

IS - 3

ER -