Author image not provided
 Xiaohua Zhou

Authors:
Add personal information
  Affiliation history
Bibliometrics: publication history
Average citations per article0.00
Citation Count0
Publication count3
Publication years2013-2015
Available for download1
Average downloads per article84.00
Downloads (cumulative)84
Downloads (12 Months)28
Downloads (6 Weeks)5
SEARCH
ROLE
Arrow RightAuthor only


AUTHOR'S COLLEAGUES
See all colleagues of this author




BOOKMARK & SHARE


3 results found Export Results: bibtexendnoteacmrefcsv

Result 1 – 3 of 3
Sort by:

1 published by ACM
December 2015 ACM Transactions on Intelligent Systems and Technology (TIST) - Special Issue on Causal Discovery and Inference: Volume 7 Issue 2, January 2016
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 5,   Downloads (12 Months): 28,   Downloads (Overall): 84

Full text available: PDFPDF
Noncompliance and missing data often occur in randomized trials, which complicate the inference of causal effects. When both noncompliance and missing data are present, previous papers proposed moment and maximum likelihood estimators for binary and normally distributed continuous outcomes under the latent ignorable missing data mechanism. However, the latent ignorable ...
Keywords: missing not at random, outcome-dependent missing, Causal inference, instrumental variable, noncompliance, principal stratification

2
April 2015 Computational Statistics & Data Analysis: Volume 84 Issue C, April 2015
Publisher: Elsevier Science Publishers B. V.
Bibliometrics:
Citation Count: 0

Latent class models with crossed subject-specific and test(rater)-specific random effects have been proposed to estimate the diagnostic accuracy (sensitivity and specificity) of a group of binary tests or binary ratings. However, the computation of these models are hindered by their complicated Monte Carlo Expectation-Maximization (MCEM) algorithm. In this article, a ...
Keywords: Composite likelihood, Latent class models, Sensitivity and specificity, Imperfect reference standards, Random effects

3
May 2013 Journal of Multivariate Analysis: Volume 117, May, 2013
Publisher: Academic Press, Inc.
Bibliometrics:
Citation Count: 0

Life history data arising in clusters with pre-specified assessment time points for patients often feature incomplete data since patients may choose to visit the clinic based on their needs. Markov process models provide a useful tool describing disease progression for life history data. The literature mainly focuses on time homogeneous ...
Keywords: 62N02, Transition intensity, Cluster, 62F10, Markov non-homogeneous, Random effects, 62H12, Missing not at random



The ACM Digital Library is published by the Association for Computing Machinery. Copyright © 2018 ACM, Inc.
Terms of Usage   Privacy Policy   Code of Ethics   Contact Us