[1] Cook R D, Weisberg S. Residual and influence in regression [M]. London: Chapman and Hall, 1982.
[2] Chatterjee S, Hadi A S. Influential observations, high leverage points and outliers in linear regression(with discussion)[J]. Statistical Science, 1986, 1(3): 379-416.
[3] McCullagh P, Nelder J A. Generalized linear models [M]. London: Chapman and Hall, 1989.
[4] Davison A C, Tsai C L. Regression model diagnostics [J]. International Statistical Review, 1992, 60(3): 337-355.
[5] Gay D M, Welsch R E. Maximum likelihood and quasi-likelihood for nonlinear exponential family regression models [J]. Journal of the American Statistical Association, 1988, 83(404): 990-998.
[6] Wei B C and Shi J Q. On statistical models in regression diagnostics [J]. Ann Inst Statist Math, 1994, 46(2):267-278.
[7] Wei B C. Exponential family nonlinear models [M]. Singapore: Springer, 1998.
[8] Ferrari D, Yang Y. Estimation of tail probability via the maximum Lq-likelihood method [R]. Minneapolis, MN, USA: School of Statistics, University of Minnesota, 2007.
[9] Kullback S. Information theory and statistics [M]. Wiley: New York, 1959.
[10] Shannon C E. A mathematical theory of communication [J]. Bell System Technical Journal, 1948, 27:379-423.
[11] Havrda J, Charvát F. Quantification method of classification processes: concept of structural entropy [J]. Kibernetika, 1967, 3: 30-35.
[12] Whitmore D A. Inverse Gaussian ratio estimation [J]. Journal of the Royal Statistical Society, Series C(Applied Statistics), 1986, 35(1):8-15.
[13] Wei B C, Lin J G, Xie F C. Statistical diagnosis [M].Beijing: Higher Education Press, 2009.(in Chinese).