Influence diagnostics in linear and nonlinear mixed-effects models with censored data

@article{Matos2013InfluenceDI,
  title={Influence diagnostics in linear and nonlinear mixed-effects models with censored data},
  author={Larissa A. Matos and Victor H. Lachos and Narayanaswamy Balakrishnan and Filidor V. Labra},
  journal={Computational Statistics & Data Analysis},
  year={2013},
  volume={57},
  pages={450-464}
}
HIV RNA viral load measures are often subjected to some upper and lower detection limits depending on the quantification assays, and consequently the responses are either left or right censored. Linear and nonlinear mixed-effects models with modifications to accommodate censoring (LMEC and NLMEC) are routinely used to analyze this type of data. Recently, Vaida and Liu (2009) proposed an exact EM-type algorithm for LMEC/NLMEC, called SAGE algorithm (Meng and Van Dyk, 1997), that uses closed-form… CONTINUE READING

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