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Median regression analysis from data with left and right censored observations
Institution:1. Department of Applied Statistics, Johannes Kepler University in Linz, Austria;2. Department of Statistics and Operation Analysis, Mendel University in Brno, Czech Republic;3. Department of Mathematics, Uppsala University, Sweden;1. Department of Computer Science, University of Würzburg, Am Hubland, 97074 Würzburg, Germany;2. LISP and Department of Computer Science, University of Évora, Rua Romão Ramalho, 59, 7000 Évora, Portugal
Abstract:Median regression models provide a robust alternative to regression based on the mean. We propose a methodology for fitting a median regression model from data with both left and right censored observations, in which the left censoring variable is always observed. First we set up an adjusted least absolute deviation estimating function using the inverse censoring weighted approach, whose solution specifies the estimator. We derive the consistency and asymptotic normality of the proposed estimator and describe the inference procedure for the regression parameter. Finally, we check the finite sample performance of the proposed procedure through simulation.
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