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Monte Carlo likelihood inference for missing data models
Yun Ju Sung,
Charles J. Geyer
Statistics (Twin Cities)
Research output
:
Contribution to journal
›
Article
›
peer-review
29
Scopus citations
Overview
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Dive into the research topics of 'Monte Carlo likelihood inference for missing data models'. Together they form a unique fingerprint.
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Mathematics
Likelihood Inference
95%
Data Model
87%
Missing Data
80%
Maximum Likelihood Estimate
49%
Kullback-Leibler Information
36%
Logit
32%
Plug-in
32%
Generalized Linear Mixed Model
32%
Confidence Region
29%
Asymptotic Variance
27%
Monte Carlo method
26%
Minimizer
24%
Identically distributed
23%
Estimate
23%
Likelihood
22%
Closed-form
21%
Sample Size
19%
Infinity
17%
Approximation
13%
Business & Economics
Missing Data
100%
Inference
63%
Maximum Likelihood
38%
Generalized Linear Mixed Model
29%
Asymptotic Variance
26%
Sample Size
26%
Monte Carlo Method
22%
Logit
21%
Approximation
15%