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Order-restricted semiparametric inference for the power bias model

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Order-restricted semiparametric inference for the power bias model. / Davidov, O.; Fokianos, K.; Iliopoulos, G.
In: Biometrics, Vol. 66, No. 2, 06.2010, p. 549-557.

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

Harvard

Davidov, O, Fokianos, K & Iliopoulos, G 2010, 'Order-restricted semiparametric inference for the power bias model', Biometrics, vol. 66, no. 2, pp. 549-557. https://doi.org/10.1111/j.1541-0420.2009.01285.x

APA

Davidov, O., Fokianos, K., & Iliopoulos, G. (2010). Order-restricted semiparametric inference for the power bias model. Biometrics, 66(2), 549-557. https://doi.org/10.1111/j.1541-0420.2009.01285.x

Vancouver

Davidov O, Fokianos K, Iliopoulos G. Order-restricted semiparametric inference for the power bias model. Biometrics. 2010 Jun;66(2):549-557. Epub 2010 Jun 1. doi: 10.1111/j.1541-0420.2009.01285.x

Author

Davidov, O. ; Fokianos, K. ; Iliopoulos, G. / Order-restricted semiparametric inference for the power bias model. In: Biometrics. 2010 ; Vol. 66, No. 2. pp. 549-557.

Bibtex

@article{b705f0d777de43458bfbd3040cfc02c4,
title = "Order-restricted semiparametric inference for the power bias model",
abstract = "The power bias model, a generalization of length‐biased sampling, is introduced and investigated in detail. In particular, attention is focused on order‐restricted inference. We show that the power bias model is an example of the density ratio model, or in other words, it is a semiparametric model that is specified by assuming that the ratio of several unknown probability density functions has a parametric form. Estimation and testing procedures under constraints are developed in detail. It is shown that the power bias model can be used for testing for, or against, the likelihood ratio ordering among multiple populations without resorting to any parametric assumptions. Examples and real data analysis demonstrate the usefulness of this approach.",
author = "O. Davidov and K. Fokianos and G. Iliopoulos",
year = "2010",
month = jun,
doi = "10.1111/j.1541-0420.2009.01285.x",
language = "English",
volume = "66",
pages = "549--557",
journal = "Biometrics",
issn = "0006-341X",
publisher = "Wiley-Blackwell",
number = "2",

}

RIS

TY - JOUR

T1 - Order-restricted semiparametric inference for the power bias model

AU - Davidov, O.

AU - Fokianos, K.

AU - Iliopoulos, G.

PY - 2010/6

Y1 - 2010/6

N2 - The power bias model, a generalization of length‐biased sampling, is introduced and investigated in detail. In particular, attention is focused on order‐restricted inference. We show that the power bias model is an example of the density ratio model, or in other words, it is a semiparametric model that is specified by assuming that the ratio of several unknown probability density functions has a parametric form. Estimation and testing procedures under constraints are developed in detail. It is shown that the power bias model can be used for testing for, or against, the likelihood ratio ordering among multiple populations without resorting to any parametric assumptions. Examples and real data analysis demonstrate the usefulness of this approach.

AB - The power bias model, a generalization of length‐biased sampling, is introduced and investigated in detail. In particular, attention is focused on order‐restricted inference. We show that the power bias model is an example of the density ratio model, or in other words, it is a semiparametric model that is specified by assuming that the ratio of several unknown probability density functions has a parametric form. Estimation and testing procedures under constraints are developed in detail. It is shown that the power bias model can be used for testing for, or against, the likelihood ratio ordering among multiple populations without resorting to any parametric assumptions. Examples and real data analysis demonstrate the usefulness of this approach.

U2 - 10.1111/j.1541-0420.2009.01285.x

DO - 10.1111/j.1541-0420.2009.01285.x

M3 - Journal article

VL - 66

SP - 549

EP - 557

JO - Biometrics

JF - Biometrics

SN - 0006-341X

IS - 2

ER -