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作者：Cai, T; Wei, LJ; Wilcox, M
作者单位：Harvard University; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute
摘要：Inference procedures based on the partial likelihood function for the Cox proportional hazards model have been generalised to the case in which the data consist of a large number of independent small groups of correlated failure time observations (Lee, Wei & Amato, 1992; Liang, Self & Chang, 1993; Cai & Prentice, 1997). However, the Cox model may not fit the data well. A class of linear transformation models, which includes the proportional hazards and odds models as special cases, has been st...
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作者：Cribari-Neto, F; Ferrari, SLP; Cordeiro, GM
作者单位：Universidade Federal de Pernambuco; Universidade de Sao Paulo; Universidade Federal da Bahia
摘要：The heteroscedasticity-consistent covariance matrix estimator proposed by White (1980) is commonly used in practical applications and is implemented into a number of pieces of statistical software. However, although consistent, it can display substantial bias in small to moderately large samples, as shown by Monte Carlo simulations elsewhere. This paper defines modified White estimators which are approximately bias-free. Numerical results show that the modified estimators display much smaller ...
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作者：Yeo, IK; Johnson, RA
作者单位：Kangwon National University; University of Wisconsin System; University of Wisconsin Madison
摘要：We introduce a new power transformation family which is well defined on the whole real line and which is appropriate for reducing skewness and to approximate normality. It has properties similar to those of the Box-Cox transformation for positive variables, The large-sample properties of the transformation are investigated in the contect of a single random sample.
