Linear rank statistics in regression analysis with censored or truncated data

A class of generalized linear rank statistics is introduced for regression analysis in the presence of truncation or censoring on the response variable. Applications of these statistics to hypothesis testing and estimation are discussed. Martingale theory and stochastic integrals of multiparameter empirical processes are applied to analyze the test statistics and the rank estimates.

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