Deconvolution of P (X < Y) with supersmooth error distributions

I. Dattner

doi:10.1016/j.spl.2013.04.024

Deconvolution of P (X < Y) with supersmooth error distributions

Research output: Contribution to journal › Article › peer-review

Abstract

This paper deals with the nonparametric estimation of P (X < Y) when both X and Y are observed with additional errors. We develop a deconvolution estimator and show that it is minimax optimal and adaptive in the case of supersmooth error distributions. Some numerical results are presented.

Original language	American English
Pages (from-to)	1880-1887
Number of pages	8
Journal	Statistics and Probability Letters
Volume	83
Issue number	8
DOIs	https://doi.org/10.1016/j.spl.2013.04.024
State	Published - Aug 2013
Externally published	Yes

Keywords

Adaptive estimator
Deconvolution
Measurement error
Receiver operating characteristic
Stress-strength model

All Science Journal Classification (ASJC) codes

Statistics and Probability
Statistics, Probability and Uncertainty

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10.1016/j.spl.2013.04.024

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title = "Deconvolution of P (X < Y) with supersmooth error distributions",

abstract = "This paper deals with the nonparametric estimation of P (X < Y) when both X and Y are observed with additional errors. We develop a deconvolution estimator and show that it is minimax optimal and adaptive in the case of supersmooth error distributions. Some numerical results are presented.",

keywords = "Adaptive estimator, Deconvolution, Measurement error, Receiver operating characteristic, Stress-strength model",

author = "I. Dattner",

year = "2013",

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doi = "10.1016/j.spl.2013.04.024",

language = "American English",

volume = "83",

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N2 - This paper deals with the nonparametric estimation of P (X < Y) when both X and Y are observed with additional errors. We develop a deconvolution estimator and show that it is minimax optimal and adaptive in the case of supersmooth error distributions. Some numerical results are presented.

AB - This paper deals with the nonparametric estimation of P (X < Y) when both X and Y are observed with additional errors. We develop a deconvolution estimator and show that it is minimax optimal and adaptive in the case of supersmooth error distributions. Some numerical results are presented.

KW - Adaptive estimator

KW - Deconvolution

KW - Measurement error

KW - Receiver operating characteristic

KW - Stress-strength model

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DO - 10.1016/j.spl.2013.04.024

M3 - Article

SN - 0167-7152

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SP - 1880

EP - 1887

JO - Statistics and Probability Letters

JF - Statistics and Probability Letters

IS - 8

ER -

Deconvolution of P (X < Y) with supersmooth error distributions

Abstract

Keywords

All Science Journal Classification (ASJC) codes

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