Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/66235 
Year of Publication: 
1997
Series/Report no.: 
SFB 373 Discussion Paper No. 1997,12
Publisher: 
Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes, Berlin
Abstract: 
Motivated by an example in nutritional epidemiology, we investigate some design and analysis aspects of linear measurement error models with missing surrogate data. The specific problem investigated consists of an initial large sample in which the response (a food frequency questionnaire, FFQ) is observed, and then a smaller calibration study in which replicates of the error prone predictor are observed (food records or recalls, FR). The difference between our analysis and most of the measurement error model literature is that in our study, the selection into the calibration study can depend upon the value of the response. Rationale for this type of design is given. Two major problems are investigated. In the design of a calibration study, one has the option of larger sample sizes and fewer replicates, or smaller sample sizes and more replicates. Somewhat surprisingly, neither strategy is uniformly preferable in cases of practical interest. The answers depend on the instrument used (recalls or records) and the parameters of interest. The second problem investigated is one of analysis. In the usual linear model with no missing data, method of moments estimates and normal-theory maximum likelihood estimates are approximately equivalent, with the former method in most use because it can be calculated easily and explicitly. Both estimates are valid without any distributional assumptions. In contrast, in the missing data problem under consideration, only the moments estimate is distribution-free, but the maximum likelihood estimate has at least 50% greater precision in practical situations when normality obtains. Implications for the design of nutritional calibration studies are discussed.
Subjects: 
Measurement Error
Errors-in-Variables
Estimating Equations
Nutrition
Sampling Designs
Linear regression
Maximum Likelihood
Method of Moments
Missing Data
Model Robustness
Semiparametrics
Stratified Sampling
Weighting
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
File
Size
253.24 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.