scholarly journals Multiplicative Measurement Error and the Simulation Extrapolation Method

2008 ◽  
Author(s):  
Elena Biewen ◽  
Sandra Nolte (Lechner) ◽  
Martin Rosemann
Keyword(s):  
Measurement Error ◽  
Extrapolation Method ◽  
Simulation Extrapolation ◽  
Multiplicative Measurement Error

Effects of measurement error on Monte Carlo integration estimators of tree volume: critical height sampling and vertical Monte Carlo methods

2015 ◽  
Vol 45 (4) ◽  
pp. 463-470 ◽  
Author(s):  
Thomas B. Lynch
Keyword(s):  
Monte Carlo ◽  
Measurement Error ◽  
Sampling Error ◽  
Monte Carlo Integration ◽  
Critical Height ◽  
Volume Estimate ◽  
Cross Sectional ◽  
Individual Tree ◽  
Control Variate ◽  
Multiplicative Measurement Error

The effects of measurement error on Monte Carlo (MC) integration estimators of individual-tree volume that sample upper-stem heights at randomly selected cross-sectional areas (termed vertical methods) were studied. These methods included critical height sampling (on an individual-tree basis), vertical importance sampling (VIS), and vertical control variate sampling (VCS). These estimators were unbiased in the presence of two error models: additive measurement error with mean zero and multiplicative measurement error with mean one. Exact mathematical expressions were derived for the variances of VIS and VCS that include additive components for sampling error and measurement error, which together comprise total variance. Previous studies of sampling error for MC integration estimators of tree volume were combined with estimates of upper-stem measurement error obtained from the mensurational literature to compute typical estimates of total standard errors for VIS and VCS. Through examples, it is shown that measurement error can substantially increase the total root mean square error of the volume estimate, especially for small trees.

Simulation–extrapolation method to address errors in atomic bomb survivor dosimetry on solid cancer and leukaemia mortality risk estimates, 1950–2003

2015 ◽  
Vol 54 (3) ◽  
pp. 273-283 ◽  
Author(s):  
Rodrigue S. Allodji ◽  
Boris Schwartz ◽  
Ibrahima Diallo ◽  
Césaire Agbovon ◽  
Dominique Laurier ◽  
...  
Keyword(s):  
Mortality Risk ◽  
Atomic Bomb ◽  
Extrapolation Method ◽  
Solid Cancer ◽  
Atomic Bomb Survivor ◽  
Risk Estimates ◽  
Simulation Extrapolation

Some Variations on Standard Income Measurement Error Models

2004 ◽  
Author(s):  
Brigham B. Frandsen ◽  
James B. McDonald
Keyword(s):  
Measurement Error ◽  
Gini Coefficient ◽  
Measurement Error Models ◽  
The Gini Coefficient ◽  
Parametric Specification ◽  
Income Measurement ◽  
The Difference ◽  
Multiplicative Measurement Error ◽  
The Relationship ◽  
Measures Of Inequality

Measurement error can have a significant impact on measures of inequality. Using a fairly flexible parametric specification of an independent multiplicative measurement error (IMME) model we explore the relationship between changes in the variance of measurement error, for a given mean of measurement error, on the Gini Coefficient. While the measured Gini is greater than the true Gini, the difference decreases as the variance of measurement error decreases. Copulas are used to relax the assumption of independence of measurement error and true income. In this case the measured Gini can be larger or smaller than the true Gini, depending on the correlation between true income and measurement error. Using the same approach with simulations the effect of a different distribution of measurement error is investigated.

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