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Perturbation by multiplicative noise and the Simulation Extrapolation method

Research output: Contribution to journalJournal article


<mark>Journal publication date</mark>12/2008
<mark>Journal</mark>AStA Advances in Statistical Analysis
Number of pages15
<mark>Original language</mark>English


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.