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Density ratio model selection

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Density ratio model selection. / Fokianos, K.
In: Journal of Statistical Computation and Simulation, Vol. 77, No. 9, 2007, p. 805-819.

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

Harvard

Fokianos, K 2007, 'Density ratio model selection', Journal of Statistical Computation and Simulation, vol. 77, no. 9, pp. 805-819. https://doi.org/10.1080/10629360600673857

APA

Fokianos, K. (2007). Density ratio model selection. Journal of Statistical Computation and Simulation, 77(9), 805-819. https://doi.org/10.1080/10629360600673857

Vancouver

Fokianos K. Density ratio model selection. Journal of Statistical Computation and Simulation. 2007;77(9):805-819. Epub 2007 Aug 30. doi: 10.1080/10629360600673857

Author

Fokianos, K. / Density ratio model selection. In: Journal of Statistical Computation and Simulation. 2007 ; Vol. 77, No. 9. pp. 805-819.

Bibtex

@article{f39ba6a66a90447892e5b25c6d248da7,
title = "Density ratio model selection",
abstract = "The density ratio model presumes that the log-likelihood ratio of two unknown densities is of some known parametric linear form. However, the choice of the functional form has an impact on both estimation and testing. The problem of over/underfitting in the context of the density ratio model is examined and the theory shows that bias and loss of efficiency are introduced when the model is misspecified. The problem of identifying the appropriate functional form for an application of the density ratio model is addressed by means of model selection criteria, which perform reasonably well. Several simulations integrate the presentation.",
keywords = "Biased sampling, Empirical likelihood, Bias, Power, Misspecification, AIC, BIC",
author = "K. Fokianos",
year = "2007",
doi = "10.1080/10629360600673857",
language = "English",
volume = "77",
pages = "805--819",
journal = "Journal of Statistical Computation and Simulation",
issn = "0094-9655",
publisher = "Taylor and Francis Ltd.",
number = "9",

}

RIS

TY - JOUR

T1 - Density ratio model selection

AU - Fokianos, K.

PY - 2007

Y1 - 2007

N2 - The density ratio model presumes that the log-likelihood ratio of two unknown densities is of some known parametric linear form. However, the choice of the functional form has an impact on both estimation and testing. The problem of over/underfitting in the context of the density ratio model is examined and the theory shows that bias and loss of efficiency are introduced when the model is misspecified. The problem of identifying the appropriate functional form for an application of the density ratio model is addressed by means of model selection criteria, which perform reasonably well. Several simulations integrate the presentation.

AB - The density ratio model presumes that the log-likelihood ratio of two unknown densities is of some known parametric linear form. However, the choice of the functional form has an impact on both estimation and testing. The problem of over/underfitting in the context of the density ratio model is examined and the theory shows that bias and loss of efficiency are introduced when the model is misspecified. The problem of identifying the appropriate functional form for an application of the density ratio model is addressed by means of model selection criteria, which perform reasonably well. Several simulations integrate the presentation.

KW - Biased sampling

KW - Empirical likelihood

KW - Bias

KW - Power

KW - Misspecification

KW - AIC

KW - BIC

U2 - 10.1080/10629360600673857

DO - 10.1080/10629360600673857

M3 - Journal article

VL - 77

SP - 805

EP - 819

JO - Journal of Statistical Computation and Simulation

JF - Journal of Statistical Computation and Simulation

SN - 0094-9655

IS - 9

ER -