Models for Measured Income

2008 ◽  
pp. 95
Author(s):  
Saralees Nadarajah ◽  
Samuel Kotz
Keyword(s):  
Measurement Error ◽  
Error Modeling ◽  
Multiplicative Measurement Error ◽  
Intertemporal Behavior

Measurement error can impact estimator precision, obscure estimated relationships between variables, and distort the estimated intertemporal behavior of important economic characteristics. A commonly known model for measurement error assumes that measured income is the product of true income and a multiplicative measurement error, which is distributed independently of the level of true income. Based on this model, we derive a collection of flexible parametric forms for the distribution of measured income. We feel that this work could serve as an important reference for measurement error modeling.

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.

scholarly journals Rarefaction, alpha diversity, and statistics

2017 ◽  
Author(s):  
Amy Willis
Keyword(s):  
Measurement Error ◽  
Microbial Diversity ◽  
Microbial Ecology ◽  
Ecosystem Function ◽  
Unknown Parameter ◽  
Alpha Diversity ◽  
Fundamental Question ◽  
Error Modeling ◽  
Extensive Literature ◽  
Statistical Methodology

AbstractUnderstanding the drivers of microbial diversity is a fundamental question in microbial ecology. Extensive literature discusses different methods for describing microbial diversity and documenting its effects on ecosystem function. However, it is widely believed that diversity depends on the number of reads that are sequenced. I discuss a statistical perspective on diversity, framing the diversity of an environment as an unknown parameter, and discussing the bias and variance of plug-in and rarefied estimates. I argue that by failing to account for both bias and variance, we invalidate analysis of alpha diversity. I describe the state of the statistical literature for addressing these problems, and suggest that measurement error modeling can address issues with variance, but bias corrections need to be utilized as well. I encourage microbial ecologists to avoid motivating their investigations with alpha diversity analyses that do not use valid statistical methodology.

scholarly journals Estimation of Eosinophil Cells in Cord Blood with References Based on Blood in Adults via Bayesian Measurement Error Modeling

2019 ◽  
Author(s):  
Yu Jiang ◽  
Hongmei Zhang ◽  
Shan V Andrews ◽  
Hasan Arshad ◽  
Susan Ewart ◽  
...  
Keyword(s):  
Measurement Error ◽  
Cord Blood ◽  
Blood Cells ◽  
White Blood Cells ◽  
Error Modeling ◽  
Supplementary Information ◽  
Estimation Accuracy ◽  
Bioconductor Package ◽  
High Quality ◽  
Eosinophil Cell

Abstract Motivation Eosinophils are phagocytic white blood cells with a variety of roles in the immune system. In situations where actual counts are not available, high quality approximations of their cell proportions using indirect markers are critical. Results We develop a Bayesian measurement error model to estimate proportions of eosinophils in cord blood, using the cord blood DNA methylation profiles, based on markers of eosinophil cell heterogeneity in blood of adults. The proposed method can be directly extended to other cells across different reference panels. We demonstrate the method’s estimation accuracy using B cells and show that the findings support the proposed approach. The method has been incorporated into the estimateCellCounts function in the minfi package to estimate eosinophil cells proportions in cord blood. Availability estimateCellCounts function is implemented and available in Bioconductor package minfi. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Semiparametric Bayesian measurement error modeling

2010 ◽  
Vol 101 (3) ◽  
pp. 512-524 ◽  
Author(s):  
María P. Casanova ◽  
Pilar Iglesias ◽  
Heleno Bolfarine ◽  
Victor H. Salinas ◽  
Alexis Peña
Keyword(s):  
Measurement Error ◽  
Error Modeling

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.

Comparison of Orthogonal Regression and Least Squares in Measurement Error Modeling for Prediction of Material Property

2013 ◽  
Vol 661 ◽  
pp. 166-170
Author(s):  
Guo Liang Ding ◽  
Biao Chu ◽  
Yi Jin ◽  
Chang An Zhu
Keyword(s):  
Measurement Error ◽  
Least Squares ◽  
Material Property ◽  
Measurement Errors ◽  
Error Modeling ◽  
Simple Linear Regression ◽  
Estimation Errors ◽  
Orthogonal Regression ◽  
Regression Methods ◽  
Quenched And Tempered Steel

A critical challenge in prediction of material property is the accuracy of estimation for regression coefficient between the structure or process of material and its macroscopic property. One source of the estimation errors is measurement errors which commonly exist in practice. To provide guidance on the use of simple linear regression methods in measurement error modeling for prediction of material property, we investigated and compared least squares (LS) and orthogonal regression (OR) theoretically. And their applications in prediction of tensile strength for quenched and tempered steel 45 were presented as an example. OR has better performance than LS in the prediction of material property in presence of measurement errors under certain conditions.

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