References
- Azzalini A, Capitanio A. Statistical applications of the multivariate skew-normal distribution. J. Roy. Statist. Soc. 1999;61:579–602.
- Cysneiros FJA, Vanegas LH. Residuals and their statistical properties in symmetrical nonlinear models. Statist. Probab. Lett. 2008;78:3269–3273. doi: 10.1016/j.spl.2008.06.011
- Vanegas LH, Cysneiros FJA. Assessment of diagnostic procedures in symmetrical nonlinear regression models. Comput. Statist. Data Anal. 2010;54:1002–1016. doi: 10.1016/j.csda.2009.10.013
- Cancho VC, Lachos VH, Ortega EMM. A nonlinear regression model with skew-normal errors. Statist. Papers 2010;51:547–558.
- Xie FC, Lin JG, Wei BC. Diagnostics for skew-normal nonlinear regression models with ar(1) errors. Comput. Statist. Data Anal. 2009;53(12):4403–4416. doi: 10.1016/j.csda.2009.06.010
- Xie FC, Wei BC, Lin JG. Homogeneity diagnostics for skew-normal nonlinear regression models. Statist. Probab. Lett. 2009;79:821–827. doi: 10.1016/j.spl.2008.11.001
- Montenegro LC, Lachos V, Bolfarine H. Local influence analysis of skew-normal linear mixed models. Comm. Statist. Theory Methods 2009;38:484–496. doi: 10.1080/03610920802238647
- Branco MD, Dey DK. A general class of multivariate skew-elliptical distributions. J. Multivariate Anal. 2001;79:99–113. doi: 10.1006/jmva.2000.1960
- Lange KL, Sinsheimer JS. Normal/independent distributions and their applications in robust regression. J. Comput. Graph. Statist. 1993;2:175–198.
- Garay AM, Lachos VH, Abanto-Valle CA. Nonlinear regression models based on scale mixtures of skew-normal distributions. J. Korean Statist. Soc. 2011;40:115–124. doi: 10.1016/j.jkss.2010.08.003
- Cook RD, Weisberg S. Residuals and influence in regression. Boca Raton (FL): Chapman & Hall/CRC; 1982.
- Cook RD. Assessment of local influence (with discussion). J. Roy. Statist. Soc. Ser. B 1986;48:133–169.
- Galea M, Paula GA, Cysneiros FJA. On diagnostics in symmetrical nonlinear models. Statist. Probab. Lett. 2005;73(4):459–467. doi: 10.1016/j.spl.2005.04.033
- Lin J, Xie F, Wei B. Statistical diagnostics for skew-t-normal nonlinear models. Comm. Statist. Simulation Comput. 2009;38(10):2096–2110. doi: 10.1080/03610910903249502
- Cook R, Weisberg S. Diagnostics for heteroscedasticity in regression. Biometrika 1983;70(1):1–10. doi: 10.1093/biomet/70.1.1
- Lin J, Wei B. Testing for heteroscedasticity in nonlinear regression models. Comm. Statist. Theory Methods 2003;32(1):171–192. doi: 10.1081/STA-120017806
- Cysneiros F, Paula G, Galea M. Heteroscedastic symmetrical linear models. Statist. Probab. Lett. 2007;77(11):1084–1090. doi: 10.1016/j.spl.2007.01.012
- Gómez H, Venegas O, Bolfarine H. Skew-symmetric distributions generated by the distribution function of the normal distribution. Environmetrics 2007;18(4):395–408. doi: 10.1002/env.817
- Basso RM, Lachos VH, Cabral CR, Ghosh P. Robust mixture modeling based on scale mixtures of skew-normal distributions. Comput. Statist. Data Anal. 2010;54(12):2926–2941. doi: 10.1016/j.csda.2009.09.031
- Lucas A. Robustness of the student t based m-estimator. Comm. Statist. Theory Methods 1997;26(5):1165–1182. doi: 10.1080/03610929708831974
- Meza C, Osorio F, De la Cruz R. Estimation in nonlinear mixed-effects models using heavy-tailed distributions. Stat. Comput. 2012;22:121–139. doi: 10.1007/s11222-010-9212-1
- Cox D, Hinkley D. Theoretical statistics. New York (NY): Chapman & Hall/CRC; 1974.
- Cook R. Detection of influential observation in linear regression. Technometrics 1977;19(1):15–18. doi: 10.2307/1268249
- Poon W, Poon YS. Conformal normal curvature and assessment of local influence. J. Roy. Statist. Soc. Ser. B 1999;61:51–61. doi: 10.1111/1467-9868.00162
- Atkinson A. Two graphical displays for outlying and influential observations in regression. Biometrika 1981;68(1):13–20. doi: 10.1093/biomet/68.1.13
- Osorio F, Paula GA, Galea M. Assessment of local influence in elliptical linear models with longitudinal structure. Comput. Statist. Data Anal. 2007;51:4354–4368. doi: 10.1016/j.csda.2006.06.004
- Bates D, Watts D. Relative curvature measures of nonlinearity. J. Roy. Statist. Soc. Ser. B, 1980;42:1–25.
- Cho H, Ibrahim J, Sinha D, Zhu H. Bayesian case influence diagnostics for survival models. Biometrics 2009;65: 116–124. doi: 10.1111/j.1541-0420.2008.01037.x