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Perturbation by multiplicative noise and the Simulation Extrapolation method

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Perturbation by multiplicative noise and the Simulation Extrapolation method. / Biewen, Elena; Nolte, Sandra; Rosemann, Martin.

In: AStA Advances in Statistical Analysis, Vol. 92, No. 4, 12.2008, p. 375-389.

Research output: Contribution to journalJournal article

Harvard

Biewen, E, Nolte, S & Rosemann, M 2008, 'Perturbation by multiplicative noise and the Simulation Extrapolation method', AStA Advances in Statistical Analysis, vol. 92, no. 4, pp. 375-389. https://doi.org/10.1007/s10182-008-0089-7

APA

Biewen, E., Nolte, S., & Rosemann, M. (2008). Perturbation by multiplicative noise and the Simulation Extrapolation method. AStA Advances in Statistical Analysis, 92(4), 375-389. https://doi.org/10.1007/s10182-008-0089-7

Vancouver

Biewen E, Nolte S, Rosemann M. Perturbation by multiplicative noise and the Simulation Extrapolation method. AStA Advances in Statistical Analysis. 2008 Dec;92(4):375-389. https://doi.org/10.1007/s10182-008-0089-7

Author

Biewen, Elena ; Nolte, Sandra ; Rosemann, Martin. / Perturbation by multiplicative noise and the Simulation Extrapolation method. In: AStA Advances in Statistical Analysis. 2008 ; Vol. 92, No. 4. pp. 375-389.

Bibtex

@article{786ea6662d6445ddb4ab2dffbad4c4ed,
title = "Perturbation by multiplicative noise and the Simulation Extrapolation method",
abstract = "While most of the literature on measurement error focuses on additive measurement error, we consider in this paper the multiplicative case. We apply the Simulation Extrapolation method (SIMEX)—a procedure which was originally proposed by Cook and Stefanski (J. Am. Stat. Assoc. 89:1314–1328, 1994) in order to correct the bias due to additive measurement error—to the case where data are perturbed by multiplicative noise and present several approaches to account for multiplicative noise in the SIMEX procedure. Furthermore, we analyze how well these approaches reduce the bias caused by multiplicative perturbation. Using a binary probit model, we produce Monte Carlo evidence on how the reduction of data quality can be minimized.",
keywords = "Errors-in-variables in nonlinear models, Disclosure limitation methods, Multiplicative error, Simulation extrapolation method",
author = "Elena Biewen and Sandra Nolte and Martin Rosemann",
year = "2008",
month = dec
doi = "10.1007/s10182-008-0089-7",
language = "English",
volume = "92",
pages = "375--389",
journal = "AStA Advances in Statistical Analysis",
issn = "1863-8171",
publisher = "Springer",
number = "4",

}

RIS

TY - JOUR

T1 - Perturbation by multiplicative noise and the Simulation Extrapolation method

AU - Biewen, Elena

AU - Nolte, Sandra

AU - Rosemann, Martin

PY - 2008/12

Y1 - 2008/12

N2 - While most of the literature on measurement error focuses on additive measurement error, we consider in this paper the multiplicative case. We apply the Simulation Extrapolation method (SIMEX)—a procedure which was originally proposed by Cook and Stefanski (J. Am. Stat. Assoc. 89:1314–1328, 1994) in order to correct the bias due to additive measurement error—to the case where data are perturbed by multiplicative noise and present several approaches to account for multiplicative noise in the SIMEX procedure. Furthermore, we analyze how well these approaches reduce the bias caused by multiplicative perturbation. Using a binary probit model, we produce Monte Carlo evidence on how the reduction of data quality can be minimized.

AB - While most of the literature on measurement error focuses on additive measurement error, we consider in this paper the multiplicative case. We apply the Simulation Extrapolation method (SIMEX)—a procedure which was originally proposed by Cook and Stefanski (J. Am. Stat. Assoc. 89:1314–1328, 1994) in order to correct the bias due to additive measurement error—to the case where data are perturbed by multiplicative noise and present several approaches to account for multiplicative noise in the SIMEX procedure. Furthermore, we analyze how well these approaches reduce the bias caused by multiplicative perturbation. Using a binary probit model, we produce Monte Carlo evidence on how the reduction of data quality can be minimized.

KW - Errors-in-variables in nonlinear models

KW - Disclosure limitation methods

KW - Multiplicative error

KW - Simulation extrapolation method

U2 - 10.1007/s10182-008-0089-7

DO - 10.1007/s10182-008-0089-7

M3 - Journal article

VL - 92

SP - 375

EP - 389

JO - AStA Advances in Statistical Analysis

JF - AStA Advances in Statistical Analysis

SN - 1863-8171

IS - 4

ER -