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Asymptotically optimal nonparametric one- and two-way analysis of variance tests for the log linear model under censoring

Authors:

Prof Dr T J Terpstra

Institution:

1. Department of Applied Mathematics, University Twente, The Netherlands

Abstract:

We considerr ×c populations with failure ratesλ _ij(t) satisfying the condition

$\left\{ \begin{gathered} \lambda _{ij} (t) = \lambda (t) \exp (\alpha _i + \beta _j + \gamma _{ij} ), \hfill \\ \Sigma \alpha _i = 0, \Sigma \beta _j = 0, \mathop \Sigma \limits_j \gamma _{ij} = 0, \mathop \Sigma \limits_i \gamma _{ij} = 0, i \leqslant r, i \leqslant c, \hfill \\ \lambda (t) being unknown. \hfill \\ \end{gathered} \right.$

Keywords:	Survival analysis log linear model analysis of variance censoring partial likelihood non-parametric theory contiguity asymptotic optimal similar tests counting process intensity process martingale stochastic integral
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