Profile information matrix for nonlinear transformation models.
For semiparametric models, interval estimation and hypothesis testing based on the information matrix for the full model is a challenge because of potentially unlimited dimension. Use of the profile information matrix for a small set of parameters of interest is an appealing alternative. Existing approaches for the estimation of the profile information matrix are either subject to the curse of dimensionality, or are ad-hoc and approximate and can be unstable and numerically inefficient. We propose a numerically stable and efficient algorithm that delivers an exact observed profile information matrix for regression coefficients for the class of Nonlinear Transformation Models [A. Tsodikov (2003) J R Statist Soc Ser B 65:759-774]. The algorithm deals with the curse of dimensionality and requires neither large matrix inverses nor explicit expressions for the profile surface.
Tsodikov
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Department of Biostatistics, School of Public Health, University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48109-2029, USA. tsodikov@umich.edu
Garibotti
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D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D016013:Likelihood Functions; D017711:Nonlinear Dynamics; D012044:Regression Analysis; D018709:Statistics, Nonparametric; D016019:Survival Analysis