Influence diagnostics in heteroscedastic and/or autoregressive nonlinear elliptical models for correlated data

References

• Bocheng , W. and Xuping , Z. 2001 . Influence analysis in nonlinear models with random effects . Appl. Math. J. Chinese Univ. Ser. B , 16 : 35 – 44 .
   Google Scholar
• Cook , D. 1986 . Assessment of local influence . J. R. Stat. Soc. Ser. B , 48 : 133 – 169 .
   Google Scholar
• Cook , D. 1987 . Influence assessment . J. Appl. Stat. , 14 : 117 – 131 .
   Google Scholar
• Cysneiros , F. J.A. , Paula , G. A. and Galea , M. 2007 . Heteroscedastic symmetrical linear models . Statist. Probab. Lett. , 77 : 1084 – 1090 .
   Web of Science ®Google Scholar
• Davidian , M. and Giltinan , D. M. 1995 . Nonlinear Models for Repeated Measurement Data . Chapman & Hall, New York ,
   Google Scholar
• Fang , K. T. , Kotz , S. and Ng , K. W. 1990 . Symmetric Multivariate and Related Distributions . Chapman & Hall, New york ,
   Google Scholar
• Gilberg , F. , Urfer , W. and Edler , L. 1999 . Heteroscedastic nonlinear regression models with random effects and their application to enzyme kinetic data . Biom. J. , 41 : 543 – 557 .
   Web of Science ®Google Scholar
• Gómez , E. , Gómez-Villegas , M. A. and Martín , J. M. 1998 . A multivariate generalization of the power exponential family of distributions . Comm. Statist. Part A: Theory Methods , 27 : 589 – 600 .
   Web of Science ®Google Scholar
• Lange , K. 2004 . “ Optimization ” . In Springer Texts in Statistics , Berlin : Springer .
   Google Scholar
• Lange , K. L. , Little , R. J.A. and Taylor , J. M.G. 1989 . Robust statistical modeling using the t distribution . J. Amer. Statist. Assoc. , 84 : 881 – 896 .
   Web of Science ®Google Scholar
• Lee , Y. , Nelder , J. A. and Noh , M. 2007 . H-likelihood: Problems and solutions . Stat. Comput. , 17 : 49 – 55 .
   Web of Science ®Google Scholar
• Lee , S. Y. and Xu , L. 2004 . Influence analyses of nonlinear mixed-effects models . Comput. Statist. Data Anal. , 45 : 321 – 341 .
   Web of Science ®Google Scholar
• Lin , J. G. and Wei , B. C. 2007 . Testing for heteroscedasticity and/or autocorrelation in longitudinal mixed effect nonlinear models with AR (1) errors . Comm. Statist. Theory Methods , 36 : 567 – 586 .
   Web of Science ®Google Scholar
• Lucas , A. 1997 . Robustness of the student t based M-estimator . Comm. Statist. Theory Methods , 26 : 1165 – 1182 .
   Web of Science ®Google Scholar
• Meza , C. , Osorio , F. and De la Cruz , R. 2011 . Estimation in nonlinear mixed-effects models using heavy-tailed distributions . Stat. Comput , doi:10.1007/s11222-010-9212-1
   PubMed Web of Science ®Google Scholar
• Osorio , F. , Paula , G. A. and Galea , M. 2007 . Assessment of local influence in elliptical linear models with longitudinal structure . Comput. Statist. Data Anal. , 51 : 4354 – 4368 .
   Web of Science ®Google Scholar
• Patriota , A. G. 2011 . A note on influence diagnostics in nonlinear mixed-effects elliptical models . Comput. Statist. Data Anal. , 55 : 218 – 225 .
   Web of Science ®Google Scholar
• Paula , G. A. , Medeiros , M. and Vilca-Labra , F. E. 2009 . Influence diagnostics for linear models with first-order autoregressive elliptical errors . Statist. Probab. Lett. , 79 : 339 – 346 .
   Web of Science ®Google Scholar
• Pinheiro , J. C. and Bates , D. M. 2000 . Mixed-effects models in S and S-PLUS , Berlin : Springer .
   Google Scholar
• Poon , W. Y. and Poon , Y. S. 1999 . Conformal normal curvature and assessment of local influence . J. R. Stat. Soc. Ser. B , 61 : 51 – 61 .
   Google Scholar
• R Development Core Team, R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, 2008; software available at http://www.R-project.org
   Google Scholar
• Russo , C. M. , Paula , G. A. and Aoki , R. 2009 . Influence diagnostics in nonlinear mixed-effects elliptical models . Comput. Statist. Data Anal. , 53 : 4143 – 4156 .
   Web of Science ®Google Scholar
• Russo , C. M. , Aoki , R. and Paula , G. A. 2011 . Assessment of variance components in nonlinear mixed-effects elliptical models . Test (Madrid) , DOI 10.1007/s11749-011-0262-2
   Google Scholar
• Savalli , C. , Paula , G. A. and Cysneiros , F. J.A. 2006 . Assessment of variance components in elliptical linear mixed models . Statist. Model. , 6 : 59 – 76 .
   Web of Science ®Google Scholar
• Verbeke , G. and Molenberghs , G. 2000 . Linear Mixed Models for Longitudinal Data , New York : Spring Verlag .
   Google Scholar
• Yun , S. and Lee , Y. 2004 . Comparison of hierarchical and marginal likelihood estimators for binary outcomes . Comput. Statist. Data Anal , : 639 – 650 .
   Web of Science ®Google Scholar