Efficient Estimation of Quantiles in Missing Data Models

@article{Daz2015EfficientEO,
  title={Efficient Estimation of Quantiles in Missing Data Models},
  author={Iv{\'a}n D{\'i}az},
  journal={arXiv: Methodology},
  year={2015}
}
  • Iván Díaz
  • Published 2015
  • Mathematics
  • arXiv: Methodology
  • We propose a novel targeted maximum likelihood estimator (TMLE) for quantiles in semiparametric missing data models. Our proposed estimator is locally efficient, $\sqrt{n}$-consistent, asymptotically normal, and doubly robust, under regularity conditions. We use Monte Carlo simulation to compare our proposed method to existing estimators. The TMLE is superior to all competitors, with relative efficiency up to three times smaller than the inverse probability weighted estimator (IPW), and up to… CONTINUE READING

    Tables from this paper.

    Citations

    Publications citing this paper.
    SHOWING 1-10 OF 10 CITATIONS

    Causal Inference with Covariate Balance Optimization

    VIEW 1 EXCERPT
    CITES BACKGROUND

    Estimating scaled treatment effects with multiple outcomes

    Quantile regression 40 years on

    VIEW 7 EXCERPTS
    CITES METHODS & BACKGROUND
    HIGHLY INFLUENCED

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 44 REFERENCES

    The Relative Performance of Targeted Maximum Likelihood Estimators

    VIEW 3 EXCERPTS

    Targeted Estimation of Nuisance Parameters to Obtain Valid Statistical Inference

    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL

    A General Implementation of TMLE for Longitudinal Data Applied to Causal Inference in Survival Analysis

    VIEW 7 EXCERPTS
    HIGHLY INFLUENTIAL