Multivariate density estimation using dimension reducing information and tail flattening transformations for truncated or censored data
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Multivariate density estimation using dimension reducing information and tail flattening transformations for truncated or censored data. / Buch-Kromann, Tine; Nielsen, Jens.
In: Annals of the Institute of Statistical Mathematics, Vol. 64, No. 1, 2012, p. 167-192.Research output: Contribution to journal › Journal article › Research › peer-review
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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