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Merging information for semiparametric density estimation

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Merging information for semiparametric density estimation. / Fokianos, K.
In: Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 66, No. 4, 11.2004, p. 941-958.

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

Harvard

Fokianos, K 2004, 'Merging information for semiparametric density estimation', Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 66, no. 4, pp. 941-958. https://doi.org/10.1111/j.1467-9868.2004.05480.x

APA

Fokianos, K. (2004). Merging information for semiparametric density estimation. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 66(4), 941-958. https://doi.org/10.1111/j.1467-9868.2004.05480.x

Vancouver

Fokianos K. Merging information for semiparametric density estimation. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2004 Nov;66(4):941-958. Epub 2004 Oct 13. doi: 10.1111/j.1467-9868.2004.05480.x

Author

Fokianos, K. / Merging information for semiparametric density estimation. In: Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2004 ; Vol. 66, No. 4. pp. 941-958.

Bibtex

@article{622e8d6f578148608d37b10ede602a69,
title = "Merging information for semiparametric density estimation",
abstract = "Summary. The density ratio model specifies that the likelihood ratio of m−1 probability density functions with respect to the mth is of known parametric form without reference to any parametric model. We study the semiparametric inference problem that is related to the density ratio model by appealing to the methodology of empirical likelihood. The combined data from all the samples leads to more efficient kernel density estimators for the unknown distributions. We adopt variants of well‐established techniques to choose the smoothing parameter for the density estimators proposed.",
keywords = "Bandwidth, Biased sampling, Discrete choice models, Empirical likelihood , Kernel estimator , Retrospective sampling",
author = "K. Fokianos",
year = "2004",
month = nov,
doi = "10.1111/j.1467-9868.2004.05480.x",
language = "English",
volume = "66",
pages = "941--958",
journal = "Journal of the Royal Statistical Society: Series B (Statistical Methodology)",
issn = "1369-7412",
publisher = "Wiley-Blackwell",
number = "4",

}

RIS

TY - JOUR

T1 - Merging information for semiparametric density estimation

AU - Fokianos, K.

PY - 2004/11

Y1 - 2004/11

N2 - Summary. The density ratio model specifies that the likelihood ratio of m−1 probability density functions with respect to the mth is of known parametric form without reference to any parametric model. We study the semiparametric inference problem that is related to the density ratio model by appealing to the methodology of empirical likelihood. The combined data from all the samples leads to more efficient kernel density estimators for the unknown distributions. We adopt variants of well‐established techniques to choose the smoothing parameter for the density estimators proposed.

AB - Summary. The density ratio model specifies that the likelihood ratio of m−1 probability density functions with respect to the mth is of known parametric form without reference to any parametric model. We study the semiparametric inference problem that is related to the density ratio model by appealing to the methodology of empirical likelihood. The combined data from all the samples leads to more efficient kernel density estimators for the unknown distributions. We adopt variants of well‐established techniques to choose the smoothing parameter for the density estimators proposed.

KW - Bandwidth

KW - Biased sampling

KW - Discrete choice models

KW - Empirical likelihood

KW - Kernel estimator

KW - Retrospective sampling

U2 - 10.1111/j.1467-9868.2004.05480.x

DO - 10.1111/j.1467-9868.2004.05480.x

M3 - Journal article

VL - 66

SP - 941

EP - 958

JO - Journal of the Royal Statistical Society: Series B (Statistical Methodology)

JF - Journal of the Royal Statistical Society: Series B (Statistical Methodology)

SN - 1369-7412

IS - 4

ER -