title: Masking unmasked in the proportional hazards model.
abstract: Influence measures based on the pairwise deletion approach and the differentiation approach are developed for unmasking observations masked by other observations in the proportional hazards model. These influential observations might have substantial impact on statistical inference and might provide important information for model adequacy. One numerical example based on real data is presented and discussed.
authors: Department of Statistics, Tung Hai University, Taichung, Taiwan, R.O.C. wenwei@mail.thu.edu.tw
authors: W H
authors: WH
authors: Wei
authors: M R
authors: MR
authors: Kosorok
mesh_terms: D001699:Biometry / Q000379:methods*; D011336:Probability; D016016:Proportional Hazards Models; D012044:Regression Analysis; D015203:Reproducibility of Results