1 Erratum to: Stat Papers (2012) 53:133–149 DOI 10.1007/s00362-010-0321-x
As per the request of Dr. Takeshi Emura, his address has been updated in the affiliation section.
We provide corrections for Emura and Konno (2012). We also numerically verify the corrected formulae. Appendix gives a real data used for numerical analysis.
2 Correction in the score function [p. 138, definition of \({\mathbf{U}}_{i}^{*} ({\varvec{\uptheta }})\)]
For \({{\varvec{\uptheta }'}}=(\mu _L ,\;\mu _X ,\;\sigma _L^2 ,\;\sigma _X^2)\), the corrected score function is
where
The error occurred in the third and fourth components of \({\mathbf{U}}_{i}^{*} ({\varvec{\uptheta }})\).
To confirm that Eqs. (1) and (2) are correct, we focus on the third component of Eq. (1):
We compute the score functions using a real data from Appendix. Then, compare Eq. (3) with the numerical derivative
where \(h=10^{-7}\). The results are given in Table 1. We see that there is virtually no difference between the corrected formula and the numerical derivative. On the other hand, the values of the formula of Emura and Konno (2012) are remarkably different from those of the numerical derivative.
3 Correction in the function \({\dot{w}}(c)\) [p. 139]
Emura and Konno (2012) considered a function \(w(\cdot ):\,\;(0,1)\rightarrow [0,1]\), defined as
They showed that \(w\) is strictly decreasing, reflecting the decreasing loss of information at inclusion probability \(c\). However, they do not give the formula of \(\dot{w}(c)=dw(c)/dc\), and their claim \(\dot{w}(1/2)= {\sqrt{{2/\pi }}} (1-4/\pi )\) is incorrect.
Here we provide an explicit derivative given by
With this formula, one has
We have confirmed the correctness of Eqs. (4) and (5) in Table 2.
References
Chung H (2013) Application of approximate reasoning using triangular and sine-curved membership functions. In: Balas VE et al (eds) New concepts and applications in soft computing. Springer-Verlag, Berlin, pp 141–155
Emura T, Konno Y (2012) Multivariate normal distribution approaches for dependently truncated data. Stat Papers 53:133–149
Acknowledgments
This work was financially supported by the National Science Council of Taiwan (NSC101-2118-M008-002-MY2). The authors thank Pan Chi-Hung for finding and correcting the errors in our paper.
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The online version of the original article can be found under doi:10.1007/s00362-010-0321-x.
Appendix: Data on scores from examination and homework (Chung 2013)
Appendix: Data on scores from examination and homework (Chung 2013)
The example is due to Chung (2013). Table 3 records two evaluation items: \(X^{O}=\) score from examination and \(Y^O=\) score from homework are recorded for 10 students. Define a weighted mean \(Z^O=0.75X^O+0.25Y^O\). Students receive a “fair” if \(Z^{O}\ge 60\) and a “fail” if \(Z^{O}<60\), respectively. Suppose that a teacher can obtain samples for those students with \(Z^{O}\ge 60\). Then, \(Z^{O}\!\ge 60\) is equal to \(X^{O}\ge L^O\), where \(L^{O}=80-Y^{O}/3\). Accordingly, the observed data is \((L_j , X_j )\), subject to \(L_j \le X_j \), for \(j=1,2,\ldots , 7\) in which 7 out of 10 samples are included.
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Emura, T., Konno, Y. Erratum to: Multivariate normal distribution approaches for dependently truncated data. Stat Papers 55, 1233–1236 (2014). https://doi.org/10.1007/s00362-014-0626-2
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DOI: https://doi.org/10.1007/s00362-014-0626-2