The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
The EM Algorithm and Extensions
Abstract
The first unified account of the theory, methodology, and applications of the EM algorithm and its extensionsSince its inception in 1977, the Expectation-Maximization (EM) algorithm has been the subject of intense scrutiny, dozens of applications, numerous extensions, and thousands of publications. The algorithm and its extensions are now standard tools applied to incomplete data problems in virtually every field in which statistical methods are used. Until now, however, no single source offered a complete and unified treatment of the subject.The EM Algorithm and Extensions describes the formulation of the EM algorithm, details its methodology, discusses its implementation, and illustrates applications in many statistical contexts. Employing numerous examples, Geoffrey McLachlan and Thriyambakam Krishnan examine applications both in evidently incomplete data situations-where data are missing, distributions are truncated, or observations are censored or grouped-and in a broad variety of situations in which incompleteness is neither natural nor evident. They point out the algorithm's shortcomings and explain how these are addressed in the various extensions.Areas of application discussed include: Regression Medical imaging Categorical data analysis Finite mixture analysis Factor analysis Robust statistical modeling Variance-components estimation Survival analysis Repeated-measures designs For theoreticians, practitioners, and graduate students in statistics as well as researchers in the social and physical sciences, The EM Algorithm and Extensions opens the door to the tremendous potential of this remarkably versatile statistical tool.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Cited by
Missing data: our view of the state of the art
Schafer, J. L., & Graham, J. W. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.2.147 · Cited by 11,638
Top 10 algorithms in data mining
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., et al. (2008) · Knowledge and Information Systems · doi:10.1007/s10115-007-0114-2 · Cited by 5,667
Self-citationClustering by fast search and find of density peaks
Rodriguez, A., & Laio, A. (2014) · Science · doi:10.1126/science.1242072 · Cited by 4,942
Text classification from labeled and unlabeled documents using EM
Nigam, K., Mccallum, A. K., Thrun, S., & Mitchell, T. (2000) · Machine Learning · doi:10.1023/a:1007692713085 · Cited by 2,736
A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants
Neal, R. M., & Hinton, G. E. (1998) · Learning in Graphical Models · doi:10.1007/978-94-011-5014-9_12 · Cited by 2,187
Warfield, S. K., Zou, K. H., & Wells, W. M. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.828354 · Cited by 2,072
Using Bayesian model averaging to calibrate forecast ensembles.
Raftery, A. E., Gneiting, T., Balabdaoui, F., & Polakowski, M. (2005) · Monthly Weather Review · doi:10.1175/mwr2906.1 · Cited by 1,915
Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation
King, G., Honaker, J., Joseph, A., & Scheve, K. (2001) · American Political Science Review · doi:10.1017/s0003055401000235 · Cited by 1,834
Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning
Sun, Y., Babu, P., & Palomar, D. P. (2017) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2016.2601299 · Cited by 1,820
Robust Monte Carlo localization for mobile robots
Thrun, S., Fox, D., Burgard, W., & Dellaert, F. (2001) · Artificial Intelligence · doi:10.1016/s0004-3702(01)00069-8 · Cited by 1,809
Beyond SEM: General latent variable modeling
Muthén, B. O. (2002) · Behaviormetrika · doi:10.2333/bhmk.29.81 · Cited by 1,074
What to do about missing values in time series cross- section data
Honaker, J., & King, G. (2010) · American Journal of Political Science · doi:10.1111/j.1540-5907.2010.00447.x · Cited by 966
The EM Algorithm—an Old Folk-song Sung to a Fast New Tune
Meng, X.-L., & Van Dyk, D. (1997) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00082 · Cited by 802
Rates and risk factors for dementia and Alzheimer’s disease: Results from EURODEM pooled analyses
Launer, L. J., Andersen, K., Dewey, M. E., Letenneur, L., Ott, A., Amaducci, L. A., Brayne, C., Copeland, J. R. M., Dartigues, J.-F., Kragh-Sorensen, P., et al. (1999) · Neurology · doi:10.1212/wnl.52.1.78 · Cited by 758
Variations and extension of the convex–concave procedure
Lipp, T., & Boyd, S. (2016) · Optimization and Engineering · doi:10.1007/s11081-015-9294-x · Cited by 744
Yu, S.-Z. (2010) · Artificial Intelligence · doi:10.1016/j.artint.2009.11.011 · Cited by 734
Multi-model ensemble hydrologic prediction using Bayesian model averaging
Duan, Q., Ajami, N. K., Gao, X., & Sorooshian, S. (2007) · Advances in Water Resources · doi:10.1016/j.advwatres.2006.11.014 · Cited by 724
A Scaling Model for Estimating Time‐Series Party Positions from Texts
Slapin, J. B., & Proksch, S. (2008) · American Journal of Political Science · doi:10.1111/j.1540-5907.2008.00338.x · Cited by 684
Privacy-preserving k -means clustering over vertically partitioned data
Vaidya, J., & Clifton, C. (2003) · Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/956750.956776 · Cited by 626
Ajami, N. K., Duan, Q., & Sorooshian, S. (2007) · Water Resources Research · doi:10.1029/2005wr004745 · Cited by 597
Texture optimization for example-based synthesis
Kwatra, V., Essa, I., Bobick, A., & Kwatra, N. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073263 · Cited by 608
Discrete temporal models of social networks
Hanneke, S., Fu, W., & Xing, E. P. (2010) · Electronic Journal of Statistics · doi:10.1214/09-ejs548 · Cited by 587
Chang, M. J., Sharkness, J., Hurtado, S., & Newman, C. B. (2014) · Journal of Research in Science Teaching · doi:10.1002/tea.21146 · Cited by 506
Priors in whole-genome regression: the Bayesian alphabet returns
Gianola, D. (2013) · Genetics · doi:10.1534/genetics.113.151753 · Cited by 457
Learning Motion Patterns of People for Compliant Robot Motion
Bennewitz, M., Burgard, W., Cielniak, G., & Thrun, S. (2005) · The International Journal of Robotics Research · doi:10.1177/0278364904048962 · Cited by 399
Data mining and machine learning in astronomy
BALL, N. M., & BRUNNER, R. J. (2010) · International Journal of Modern Physics D · doi:10.1142/s0218271810017160 · Cited by 383
High-dimensional data clustering
Bouveyron, C., Girard, S., & Schmid, C. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.02.009 · Cited by 382
Adjusting the Outputs of a Classifier to New a Priori Probabilities: A Simple Procedure
Saerens, M., Latinne, P., & Decaestecker, C. (2002) · Neural Computation · doi:10.1162/089976602753284446 · Cited by 361
Chang, M. J., Eagan, M. K., Lin, M. H., & Hurtado, S. (2011) · The Journal of Higher Education · doi:10.1353/jhe.2011.0030 · Cited by 330
Robust guided image filtering using Nonconvex potentials
Ham, B., Cho, M., & Ponce, J. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2669034 · Cited by 219
Where Have All the Interactions Gone? Estimating the Coverage of Two-Hybrid Protein Interaction Maps
Huang, H., Jedynak, B. M., & Bader, J. S. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030214 · Cited by 182
Hurtado, S., Eagan, M. K., Cabrera, N. L., Lin, M. H., Park, J., & Lopez, M. (2008) · Research in Higher Education · doi:10.1007/s11162-007-9068-1 · Cited by 132
The growing ubiquity of algorithms in society: implications, impacts and innovations
Olhede, S. C., & Wolfe, P. J. (2018) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2017.0364 · Cited by 120
“discussion is provided in [61,62], as well as by [63] which considers algorithms less tractable to analysis.”
BackgroundRuiz-Correa, S., Sze, R. W., Starr, J. R., Lin, H.-T. J., Speltz, M. L., Cunningham, M. L., & Hing, A. V. (2006) · The Cleft Palate-Craniofacial Journal · doi:10.1597/04-208.1 · Cited by 90
Chamroukhi, F., Mohammed, S., Trabelsi, D., Oukhellou, L., & Amirat, Y. (2013) · Neurocomputing · doi:10.1016/j.neucom.2013.04.003 · Cited by 88
Cluster randomized trials with treatment noncompliance.
Jo, B., Asparouhov, T., Muthén, B. O., Ialongo, N. S., & Brown, C. H. (2008) · Psychological Methods · doi:10.1037/1082-989x.13.1.1 · Cited by 83
Proyer, R. T. (2012) · Pabst Science Publishers · doi:10.5167/uzh-63532 · Cited by 77
An Emergency Department Patient Flow Model Based on Queueing Theory Principles
Wiler, J. L., Bolandifar, E., Griffey, R. T., Poirier, R. F., & Olsen, T. (2013) · Academic Emergency Medicine · doi:10.1111/acem.12215 · Cited by 69
Barriers and drivers for non-shoppers in B2C e-commerce: a latent class exploratory analysis.
Iglesias-Pradas, S., Pascual-Miguel, F., Hernández-García, Á., & Chaparro-Peláez, J. (2013) · Computers in Human Behavior · doi:10.1016/j.chb.2012.01.024 · Cited by 59
Unconstraining methods in revenue management systems: research overview and prospects
Guo, P., Xiao, B., & Li, J. (2012) · Advances in Operations Research · doi:10.1155/2012/270910 · Cited by 40
Learning and inference in parametric switching linear dynamic systems
Oh, S. M., Rehg, J. M., Balch, T., & Dellaert, F. (2005) · Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 · doi:10.1109/iccv.2005.135 · Cited by 40
Mixture-based clustering for the ordered stereotype model
Fernández, D., Arnold, R., & Pledger, S. (2016) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2014.11.004 · Cited by 39
Oseguera, L., Hurtado, S., Denson, N., Cerna, O., & Saenz, V. (2006) · Journal of Women and Minorities in Science and Engineering · doi:10.1615/jwomenminorscieneng.v12.i2-3.40 · Cited by 14
(2011) · Cited by 11
Yang, L., McClean, S., Donnelly, M., Khan, K., & Burke, K. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3669942 · Cited by 9
Metadata
- Authors
- Kushary, D.
- Venue
- Technometrics
- Publisher
- Informa UK Limited
- Published
- 1998-08
- Volume
- 40
- Issue
- 3
- Pages
- 260-260
- ISSN
- 0040-1706 (ISSN-L)
- 1537-2723
- OpenAlex
- W2117853077
- Cited by
- 5,417
- References
- 1
- FWCI
- 114.21
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
