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Simple fitting algorithms for incomplete categorical data

Author
Organization
Abstract
A popular approach to estimation based on incomplete data is the EM algorithm. For categorical data, this paper presents a simple expression of the observed data log-likelihood and its derivatives in terms of the complete data for a broad class of models and missing data patterns. We show that using the observed data likelihood directly is easy and has some advantages. One can gain considerable computational speed over the EM algorithm and a straightforward variance estimator is obtained for the parameter estimates. The general formulation treats a wide range of missing data problems in a uniform way. Two examples are worked out in full.
Keywords
NON-IGNORABLE NONRESPONSE, MAXIMUM-LIKELIHOOD ESTIMATION, LINEAR-MODELS, EM ALGORITHM, NONIGNORABLE NONRESPONSE, OUTCOME SUBJECT, ORDINAL DATA, REGRESSION, INFERENCE, coarsened data, EM algorithm, Fisher scoring algorithm, generalized linear models, longitudinal data, maximum likelihood estimation, missing values, multivariate categorical data, repeated measures

Citation

Please use this url to cite or link to this publication:

MLA
Molenberghs, G, and Els Goetghebeur. “Simple Fitting Algorithms for Incomplete Categorical Data.” JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY 59.2 (1997): 401–414. Print.
APA
Molenberghs, G, & Goetghebeur, E. (1997). Simple fitting algorithms for incomplete categorical data. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 59(2), 401–414.
Chicago author-date
Molenberghs, G, and Els Goetghebeur. 1997. “Simple Fitting Algorithms for Incomplete Categorical Data.” Journal of the Royal Statistical Society Series B-statistical Methodology 59 (2): 401–414.
Chicago author-date (all authors)
Molenberghs, G, and Els Goetghebeur. 1997. “Simple Fitting Algorithms for Incomplete Categorical Data.” Journal of the Royal Statistical Society Series B-statistical Methodology 59 (2): 401–414.
Vancouver
1.
Molenberghs G, Goetghebeur E. Simple fitting algorithms for incomplete categorical data. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY. 1997;59(2):401–14.
IEEE
[1]
G. Molenberghs and E. Goetghebeur, “Simple fitting algorithms for incomplete categorical data,” JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, vol. 59, no. 2, pp. 401–414, 1997.
@article{269028,
  abstract     = {A popular approach to estimation based on incomplete data is the EM algorithm. For categorical data, this paper presents a simple expression of the observed data log-likelihood and its derivatives in terms of the complete data for a broad class of models and missing data patterns. We show that using the observed data likelihood directly is easy and has some advantages. One can gain considerable computational speed over the EM algorithm and a straightforward variance estimator is obtained for the parameter estimates. The general formulation treats a wide range of missing data problems in a uniform way. Two examples are worked out in full.},
  author       = {Molenberghs, G and Goetghebeur, Els},
  issn         = {1369-7412},
  journal      = {JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY},
  keywords     = {NON-IGNORABLE NONRESPONSE,MAXIMUM-LIKELIHOOD ESTIMATION,LINEAR-MODELS,EM ALGORITHM,NONIGNORABLE NONRESPONSE,OUTCOME SUBJECT,ORDINAL DATA,REGRESSION,INFERENCE,coarsened data,EM algorithm,Fisher scoring algorithm,generalized linear models,longitudinal data,maximum likelihood estimation,missing values,multivariate categorical data,repeated measures},
  language     = {eng},
  number       = {2},
  pages        = {401--414},
  title        = {Simple fitting algorithms for incomplete categorical data},
  url          = {http://dx.doi.org/10.1111/1467-9868.00075},
  volume       = {59},
  year         = {1997},
}

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