Local influence analysis for skew-normal linear mixed models

Communications in Statistics - Theory and Methods

Volumn 38, Issue 4, 2009, Pages 484-496

  Montenegro, Lourdes C ^a   Lachos, Victor H ^b   Bolfarine, Heleno ^c  

^a FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

^b UNIVERSITY OF CAMPINAS   (Brazil)

^c UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

EM algorithm; Multivariate skew normal distribution; Q displace ment function; Skewness

Indexed keywords

BIT ERROR RATE; FORESTRY;

DATA SETS; EM-ALGORITHM; LINEAR MIXED MODELS; LOCAL INFLUENCES; MULTIVARIATE SKEW-NORMAL DISTRIBUTION; OBSERVED DATUM; Q-DISPLACE-MENT FUNCTION; SIMULATED DATUM; SKEWNESS;

NORMAL DISTRIBUTION;

EID: 60649109363     PISSN: 03610926     EISSN: 1532415X     Source Type: Journal    
DOI: 10.1080/03610920802238647     Document Type: Article

References (23)

1. Arellano-Valle, R. B., Bolfarine, H., Lachos, V. H. (2005). Skew-normal linear mixed models. J. Data Sci. 3:415-438.
2. Azzalini, A., Dalla-Valle, A. (1996). The multivariate skew-normal distribution. Biometrika 83:715-726.
3. Box, G. E. P., Jenkins, G. M., Reinsel, G. C. (1994). Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day.
4. Cook, R. D. (1977). Detection of influential observation in linear regression. Technometrics 19:5-18.
5. Cook, R. D. (1986). Assessment of local influence (with discussion). J. Roy. Statist. Soc. Ser B 48:133-169.
6. Fung, W. K., Zhu, Z. Y., Wei, B. C, He, X. (2002). Influence diagnostics and outlier tests for semiparametric mixed models. J. Roy. Statist. Soc. Ser. B 64:565-579.
7. Galea-Rojas, M., Paula, G. A., Bolfarine, H. (1997). Local influence in elliptical linear regression models. Statistician 46:71-79.
8. Lachos, V. H., Bolfarine, H., Arellano-Valle, R. B., Montenegro, L. C. (2007). Likelihood based inference for multivariate skew-normal regression models. Comtnun. Statist. Theor. Meth. 36:1769-1786.
9. Lee, S. Y., Tang, N. S. (2004). Local influence analysis of nonlinear structural equation models. Psycometrika 69:573-592.
10. Lee, S. Y., Xu, L. (2004). Influence analysis of nonlinear mixed-effects models. Computat. Statist. Data Anal. 45:321-341.
11. Lin, T. I., Lee, J. C. (2007). Estimation and prediction in linear mixed models with skew-normal random effects for longitudinal data. Statist. Med. 22:1490-1507.
12. Lessaffre, E., Verbeke, G. (1998). Local influence in linear mixed models. Biometrics 54:570-582.
13. Lu, B., Song, X. Y. (2006). Local influence of multivariate probit latent variable models. J. Multivariate Anal. 97:1783-1798.
14. Ma, Y., Genton, M. G., Davidian, M. (2004). Linear mixed effects models with flexible generalized skew-elliptical random effects. In: Genton, M. G., ed. Skew-Elliptical Distributions and Their Applications: A Journey Beyond Normality. Boca Raton, FL: Chapman & Hall/CRC, pp. 339-358.
15. Magder, L. S., Zeger, S. L. (1996). A smooth nonparametric estimate of a mixing distribution using mixtures of gaussians. J. Amer. Statist. Assoc. 91:1141-1151.
16. Magnus, J. R., Neudecker, H. (1988). Matrix Differential Calculus with Applications in Statistics and Econometrics. New York: Wiley.
17. Osorio, F., Paula, G. A., Galea, M. (2007). Assessment of local influence in elliptical linear models with longitudinal structure. Computat. Statist. Data Anal. 51:4354-4368.
18. Poon, W. Y., Poon, Y. S. (1999). Conformal normal curvature and assessment of local influence. J. Roy. Statist. Soc. Sen B 61:51-61.
19. Tao, H., Palta, M., Yandell, B. S., Newton, M. A. (1999). An estimation method for the semiparametric mixed effects model. Biometrics 55:102-110.
20. Verbeke, G., Lessafre, E. (1996). A linear mixed-effects model with heterogeneity in the random-effects population. J. Amer. Statist. Assoc. 91:217-221.
21. Verbeke, G., Molenberghs, G. (2000). Linear Mixed Models for Longitudinal Data. New York: Springer.
22. Zhang, D., Davidian, M. (2001). Linear mixed models with flexible distributions of random effects for longitudinal data. Biometrics 57:795-802.
23. Zhu, H., Lee, S. (2001). Local influence for incomplete-data models. J. Roy. Statist. Soc. Ser. B 63:111-126.