Corrections to: Multivariate normal distribution approaches for dependently truncated data

Pan, Chi-Hung and Emura, Takeshi (2014): Corrections to: Multivariate normal distribution approaches for dependently truncated data. Forthcoming in: Statistical Papers

Abstract

We provide corrections for Emura and Konno (2010). We also numerically verify the corrected formulae. Appendix gives a real data used for numerical analysis.

• Item Type: MPRA Paper
• Language: English
• Keywords: Dependent truncation • Information matrix • Maximum likelihood • Multivariate analysis
• Subjects: C - Mathematical and Quantitative Methods > C0 - General > C02 - Mathematical Methods; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General; ; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C34 - Truncated and Censored Models ; Switching Regression Models; C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C83 - Survey Methods ; Sampling Methods

References

Chung H (2013) Application of approximate reasoning using triangular and sine-curved membership functions, V.E. Balas et al. (Eds.): New Concepts and Applications in Soft Computing, pp. 141–155, Springer-Verlag Berlin Heidelberg

Emura T, Konno Y (2012) Multivariate normal distribution approaches for dependently truncated data. Stat Papers 53:133-149