Multivariate density estimation using dimension reducing information and tail flattening transformations for truncated or censored data

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Standard

Multivariate density estimation using dimension reducing information and tail flattening transformations for truncated or censored data. / Buch-Kromann, Tine; Nielsen, Jens.

I: Annals of the Institute of Statistical Mathematics, Bind 64, Nr. 1, 2012, s. 167-192.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Buch-Kromann, T & Nielsen, J 2012, 'Multivariate density estimation using dimension reducing information and tail flattening transformations for truncated or censored data', Annals of the Institute of Statistical Mathematics, bind 64, nr. 1, s. 167-192. https://doi.org/10.1007/s10463-010-0313-6

APA

Buch-Kromann, T., & Nielsen, J. (2012). Multivariate density estimation using dimension reducing information and tail flattening transformations for truncated or censored data. Annals of the Institute of Statistical Mathematics, 64(1), 167-192. https://doi.org/10.1007/s10463-010-0313-6

Vancouver

Buch-Kromann T, Nielsen J. Multivariate density estimation using dimension reducing information and tail flattening transformations for truncated or censored data. Annals of the Institute of Statistical Mathematics. 2012;64(1):167-192. https://doi.org/10.1007/s10463-010-0313-6

Author

Buch-Kromann, Tine ; Nielsen, Jens. / Multivariate density estimation using dimension reducing information and tail flattening transformations for truncated or censored data. I: Annals of the Institute of Statistical Mathematics. 2012 ; Bind 64, Nr. 1. s. 167-192.

Bibtex

@article{cf75f4bcea7842f4b27a40f71c8960d1,
title = "Multivariate density estimation using dimension reducing information and tail flattening transformations for truncated or censored data",
abstract = "This paper introduces a multivariate density estimator for truncated and censored data with special emphasis on extreme values based on survival analysis. A local constant density estimator is considered. We extend this estimator by means of tail flattening transformation, dimension reducing prior knowledge and a combination of both. The asymptotic theory is derived for the proposed estimators. It shows that the extensions might improve the performance of the density estimator when the transformation and the prior knowledge is not too far away from the true distribution. A simulation study shows that the density estimator based on tail flattening transformation and prior knowledge substantially outperforms the one without prior knowledge, and therefore confirms the asymptotic results. The proposed estimators are illustrated and compared in a data study of fire insurance claims.",
author = "Tine Buch-Kromann and Jens Nielsen",
year = "2012",
doi = "10.1007/s10463-010-0313-6",
language = "English",
volume = "64",
pages = "167--192",
journal = "Annals of the Institute of Statistical Mathematics",
issn = "0020-3157",
publisher = "Springer",
number = "1",

}

RIS

TY - JOUR

T1 - Multivariate density estimation using dimension reducing information and tail flattening transformations for truncated or censored data

AU - Buch-Kromann, Tine

AU - Nielsen, Jens

PY - 2012

Y1 - 2012

N2 - This paper introduces a multivariate density estimator for truncated and censored data with special emphasis on extreme values based on survival analysis. A local constant density estimator is considered. We extend this estimator by means of tail flattening transformation, dimension reducing prior knowledge and a combination of both. The asymptotic theory is derived for the proposed estimators. It shows that the extensions might improve the performance of the density estimator when the transformation and the prior knowledge is not too far away from the true distribution. A simulation study shows that the density estimator based on tail flattening transformation and prior knowledge substantially outperforms the one without prior knowledge, and therefore confirms the asymptotic results. The proposed estimators are illustrated and compared in a data study of fire insurance claims.

AB - This paper introduces a multivariate density estimator for truncated and censored data with special emphasis on extreme values based on survival analysis. A local constant density estimator is considered. We extend this estimator by means of tail flattening transformation, dimension reducing prior knowledge and a combination of both. The asymptotic theory is derived for the proposed estimators. It shows that the extensions might improve the performance of the density estimator when the transformation and the prior knowledge is not too far away from the true distribution. A simulation study shows that the density estimator based on tail flattening transformation and prior knowledge substantially outperforms the one without prior knowledge, and therefore confirms the asymptotic results. The proposed estimators are illustrated and compared in a data study of fire insurance claims.

U2 - 10.1007/s10463-010-0313-6

DO - 10.1007/s10463-010-0313-6

M3 - Journal article

VL - 64

SP - 167

EP - 192

JO - Annals of the Institute of Statistical Mathematics

JF - Annals of the Institute of Statistical Mathematics

SN - 0020-3157

IS - 1

ER -

ID: 49600227