Likelihood-based Inference with Missing Data Under Missing-at-Random

Cited 2 time in webofscience Cited 0 time in scopus
  • Hit : 374
  • Download : 0
Likelihood-based inference with missing data is challenging because the observed log likelihood is often an (intractable) integration over the missing data distribution, which also depends on the unknown parameter. Approximating the integral by Monte Carlo sampling does not necessarily lead to a valid likelihood over the entire parameter space because the Monte Carlo samples are generated from a distribution with a fixed parameter value. We consider approximating the observed log likelihood based on importance sampling. In the proposed method, the dependency of the integral on the parameter is properly reflected through fractional weights. We discuss constructing a confidence interval using the profile likelihood ratio test. A Newton-Raphson algorithm is employed to find the interval end points. Two limited simulation studies show the advantage of the Wilks inference over the Wald inference in terms of power, parameter space conformity and computational efficiency. A real data example on salamander mating shows that our method also works well with high-dimensional missing data.
Publisher
WILEY-BLACKWELL
Issue Date
2016-06
Language
English
Article Type
Article
Keywords

GENERALIZED LINEAR-MODELS; EM ALGORITHM; INCOMPLETE DATA; EMPIRICAL LIKELIHOOD; MULTIPLE IMPUTATION; INFORMATION MATRIX; MAXIMUM-LIKELIHOOD; DATA AUGMENTATION; RATIO TESTS

Citation

SCANDINAVIAN JOURNAL OF STATISTICS, v.43, no.2, pp.436 - 454

ISSN
0303-6898
DOI
10.1111/sjos.12184
URI
http://hdl.handle.net/10203/218766
Appears in Collection
MA-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 2 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0