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Discrete Choice Models for Nonmonotone Nonignorable Missing Data: Identification and Inference.

Eric J Tchetgen Tchetgen1, Linbo Wang1, BaoLuo Sun1

  • 1Department of Biostatistics, Harvard University.

Statistica Sinica
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PubMed
Summary
This summary is machine-generated.

This study introduces a novel semiparametric approach for handling nonmonotone missing data, crucial for social and health sciences. It addresses nonignorable missingness, offering robust statistical inference beyond traditional methods.

Keywords:
doubly robustinverse-probability-weightingmissing not at randomnonmonotone missing datapattern mixture

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Area of Science:

  • Statistics
  • Social Sciences
  • Health Sciences

Background:

  • Nonmonotone missing data are common in empirical studies, potentially causing selection bias and reduced efficiency.
  • Standard missing-at-random assumptions are often unsuitable for nonmonotone nonresponse.
  • Existing methods typically require full data parametric models, limiting semiparametric inference.

Purpose of the Study:

  • To develop a versatile approach for semiparametric inference with nonmonotone and nonignorable missing data.
  • To establish conditions for nonparametric identification and propose a general inference framework.
  • To extend existing estimation techniques for a specific discrete choice model.

Main Methods:

  • Utilizing a discrete choice model (DCM) to represent a broad range of nonignorable nonresponse mechanisms.
  • Providing sufficient conditions for nonparametric identification of parameters.
  • Developing a general framework for parametric and semiparametric inference under arbitrary DCMs.
  • Generalizing inverse-probability weighting, pattern-mixture, doubly robust, and multiply robust estimation for the logit discrete choice nonresponse model (LDCM).

Main Results:

  • The proposed approach enables semiparametric inference for nonmonotone, nonignorable missing data.
  • The study provides theoretical guarantees for nonparametric identification.
  • A flexible framework is established for various inference scenarios, including specialized extensions for LDCM.

Conclusions:

  • The developed method offers a powerful tool for analyzing complex missing data patterns in social and health sciences.
  • It overcomes limitations of traditional methods by accommodating nonignorable nonresponse without full parametric specification.
  • The proposed framework enhances the reliability and efficiency of statistical inferences in the presence of challenging missing data.