Abstract
When both variables are subject to error in regression model, the least squares estimators are biased and inconsistent. The measurement error model is more appropriate to fit the data. This study focuses on the problem to construct interval estimation for fitting straight line in linear measurement error model when one of the error variances is known. We use the concepts of generalized pivotal quantity and construct the confidence interval for the slope because no pivot is available in this case. We compare the existing confidence intervals in terms of coverage probability and expected length via simulation studies. A real data example is also analyzed.
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Tsai, JR. Interval estimation for fitting straight line when both variables are subject to error. Comput Stat 28, 219–240 (2013). https://doi.org/10.1007/s00180-011-0295-8
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DOI: https://doi.org/10.1007/s00180-011-0295-8
Keywords
- Confidence level
- Coverage probability
- Error-in-variables model
- Expected length
- Interval estimation
- Measurement error models