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Nonparametric inference about service time distribution from indirect measurements.

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Nonparametric inference about service time distribution from indirect measurements. / Park, Juhyun; Hall, Peter.

In: Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 66, No. 4, 01.11.2004, p. 861-875.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Park, J & Hall, P 2004, 'Nonparametric inference about service time distribution from indirect measurements.', Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 66, no. 4, pp. 861-875. https://doi.org/10.1111/j.1467-9868.2004.B5725.x

APA

Park, J., & Hall, P. (2004). Nonparametric inference about service time distribution from indirect measurements. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 66(4), 861-875. https://doi.org/10.1111/j.1467-9868.2004.B5725.x

Vancouver

Park J, Hall P. Nonparametric inference about service time distribution from indirect measurements. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2004 Nov 1;66(4):861-875. https://doi.org/10.1111/j.1467-9868.2004.B5725.x

Author

Park, Juhyun ; Hall, Peter. / Nonparametric inference about service time distribution from indirect measurements. In: Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2004 ; Vol. 66, No. 4. pp. 861-875.

Bibtex

@article{0bea2a046d414ce68183afcbde40e5c6,
title = "Nonparametric inference about service time distribution from indirect measurements.",
abstract = "In studies of properties of queues, for example in relation to Internet traffic, a subject that is of particular interest is the {\textquoteleft}shape{\textquoteright} of service time distribution. For example, we might wish to know whether the service time density is unimodal, suggesting that service time distribution is possibly homogeneous, or whether it is multimodal, indicating that there are two or more distinct customer populations. However, even in relatively controlled experiments we may not have access to explicit service time data. Our only information might be the durations of service time clusters, i.e. of busy periods. We wish to {\textquoteleft}deconvolve{\textquoteright} these concatenations, and to construct empirical approximations to the distribution and, particularly, the density function of service time. Explicit solutions of these problems will be suggested. In particular, a kernel-based {\textquoteleft}deconvolution{\textquoteright} estimator of service time density will be introduced, admitting conventional approaches to the choice of bandwidth.",
author = "Juhyun Park and Peter Hall",
note = "RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research",
year = "2004",
month = nov,
day = "1",
doi = "10.1111/j.1467-9868.2004.B5725.x",
language = "English",
volume = "66",
pages = "861--875",
journal = "Journal of the Royal Statistical Society: Series B (Statistical Methodology)",
issn = "1369-7412",
publisher = "Wiley-Blackwell",
number = "4",

}

RIS

TY - JOUR

T1 - Nonparametric inference about service time distribution from indirect measurements.

AU - Park, Juhyun

AU - Hall, Peter

N1 - RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research

PY - 2004/11/1

Y1 - 2004/11/1

N2 - In studies of properties of queues, for example in relation to Internet traffic, a subject that is of particular interest is the ‘shape’ of service time distribution. For example, we might wish to know whether the service time density is unimodal, suggesting that service time distribution is possibly homogeneous, or whether it is multimodal, indicating that there are two or more distinct customer populations. However, even in relatively controlled experiments we may not have access to explicit service time data. Our only information might be the durations of service time clusters, i.e. of busy periods. We wish to ‘deconvolve’ these concatenations, and to construct empirical approximations to the distribution and, particularly, the density function of service time. Explicit solutions of these problems will be suggested. In particular, a kernel-based ‘deconvolution’ estimator of service time density will be introduced, admitting conventional approaches to the choice of bandwidth.

AB - In studies of properties of queues, for example in relation to Internet traffic, a subject that is of particular interest is the ‘shape’ of service time distribution. For example, we might wish to know whether the service time density is unimodal, suggesting that service time distribution is possibly homogeneous, or whether it is multimodal, indicating that there are two or more distinct customer populations. However, even in relatively controlled experiments we may not have access to explicit service time data. Our only information might be the durations of service time clusters, i.e. of busy periods. We wish to ‘deconvolve’ these concatenations, and to construct empirical approximations to the distribution and, particularly, the density function of service time. Explicit solutions of these problems will be suggested. In particular, a kernel-based ‘deconvolution’ estimator of service time density will be introduced, admitting conventional approaches to the choice of bandwidth.

U2 - 10.1111/j.1467-9868.2004.B5725.x

DO - 10.1111/j.1467-9868.2004.B5725.x

M3 - Journal article

VL - 66

SP - 861

EP - 875

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 -