Interval Estimation in Linear Measurement Error Models
Date Issued
2009
Date
2009
Author(s)
Tsai, Jia-Ren
Abstract
The dissertation discusses interval estimation in linear regression model with homoscedastic and heteroscedastic measurement errors in both axes. First of all, we introduce some interval methods and propose a new approach to find confidence interval for the slope in homoscedastic measurement error models. The performance of the interval estimation is compared in terms of both coverage probability and its diameter via simulation studies. Second, we suggest two approaches to estimate confidence intervals for the slope and joint confidence regions for both intercept and slope in heteroscedastic measurement error models. Application of these methods are illustrated with real data sets. The performances of the confidence interval estimation are also studied numerically via Monte Carlo simulation in terms of coverage probability.
Subjects
Confidence interval
Converge probability
Excepted length
Heteroscedastic measurement errors
Homoscedastic measurement errors
Identifiability
Measurement error models
Type
thesis
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