Conditioning in a Missing Data Problem.

Abstract

Observations are recorded on variables x and y but a mechanism, which may depend on the observed x values, causes some of the y values to be missing. For three parametric examples, exact or approximate ancillary statistics are constructed. Conditioning on these ancillaries enables the missing data mechanism to be ignored under certain conditions. A correspondence is shown between these conditional procedures and the use of the observed information matrix in measuring the dispersion of the maximum likelihood estimator. Keywords: Affine ancillary; Ancillary statistic; Conditional inference; Curved exponential family; Ignorability; Information; Missing data; Survey sampling.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1985
Accession Number
ADA158202

Entities

People

  • C. J. Skinner

Organizations

  • University of Wisconsin–Madison

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Classification
  • Data Science
  • Dispersions
  • Distribution Theory
  • Estimators
  • Information Science
  • Materials
  • Mathematics
  • Observation
  • Probability
  • Sequences
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Sampling
  • Statistics
  • Surveys
  • United States

Fields of Study

  • Mathematics

Readers

  • Geospatial Intelligence and Artificial Intelligence Analytics
  • Graph Algorithms and Convex Optimization.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms