|
|
Journal of Korean Society for Quality Management 1994;22(2): 89-. |
| A Note on the Small-Sample Calibration |
| Jong-In Lee1, Young-Mo Kim2 |
1Dept. of International Trade, Korea Maritime University
2Dept. of Nautical Science, Korea Marine Training & Research Institute |
|
|
|
|
|
| ABSTRACT |
|
We consider the linear calibration model: $y_1={alpha}+{eta}x_i+{sigma}{varepsilon}_i$, i = 1, ${cdots}$, n, $y={alpha}+{beta}x+{sigma}{varepsilon}$ where ($y_1$, ${cdots}$, $y_n$, y) stands for an observation vector, {$x_i$} fixed design vector, (${alpha}$, ${eta}$) vector of regression parameters, x unknown true value of interest and {${varepsilon}_i$}, ${varepsilon}$ are mutually uncorrelated measurement errors with zero mean and unit variance but otherwise unknown distributions. On the basis of simple small-sample low-noise approximation, we introduce a new method of comparing the mean squared errors of the various competing estimators of the true value x for finite sample size n. Then we show that a class of estimators including the classical and the inverse estimators are consistent and first-order efficient within the class of all regular consistent estimators irrespective of type of measurement errors. |
|
|
|
PDF Links
Full text via DOI 
Download Citation 
