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McLachlan, G (1998) 25 years of applied statistics. Journal of Applied Statistics, 25 1: 3-22.     0
Aghaeepour, Nima, Chattopadhyay, Pratip, Chikina, Maria, Dhaene, Tom, Van Gassen, Sofie, Kursa, Miron, Lambrecht, Bart N., Malek, Mehrnoush, McLachlan, G. J., Qian, Yu, Qiu, Peng, Saeys, Yvan, Stanton, Rick, Tong, Dong, Vens, Celine, Walkowiak, Slawomir, Wang, Kui, Finak, Greg, Gottardo, Raphael, Mosmann, Tim, Nolan, Garry P., Scheuermann, Richard H. and Brinkman, Ryan R. (2016) A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes. Cytometry Part A, 89 1: 16-21. doi:10.1002/cyto.a.22732     16 Cited 19 times in Scopus19 1
Lee, Sharon X., Leemaqz, Kaleb L. and McLachlan, Geoffrey J. (2018) A Block EM Algorithm for Multivariate Skew Normal and Skew t-Mixture Models. IEEE Transactions on Neural Networks and Learning Systems, 29 99: 1-11. doi:10.1109/TNNLS.2018.2805317     0 Cited 1 times in Scopus1
Nguyen, Hien D., Lloyd-Jones, Luke R. and McLachlan, Geoffrey J. (2016) A block minorization-maximization algorithm for heteroscedastic regression. IEEE Signal Processing Letters, 23 8: 1131-1135. doi:10.1109/LSP.2016.2586180     1 Cited 1 times in Scopus1 1
Ganesalingam, S. and McLachlan, G. J. (1979) A case study of two clustering methods based on maximum likelihood. Statistica Neerlandica, 33 2: 81-90. doi:10.1111/j.1467-9574.1979.tb00665.x     Cited 13 times in Scopus13 0
Sun, Mingzhu and McLachlan, Geoffrey J (2013). A common factor-analytic model for classification. In: Li, GZ, Kim, S, Hughes, M, McLachlan, G, Sun, H, Hu, X, Ressom, H, Liu, B and Liebman, M, Bioinformatics and Biomedicine (BIBM), 2013 IEEE International Conference on. IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013, Shanghai China, (19-24). 18 - 21 December 2013. doi:10.1109/BIBM.2013.6732722     0 0
Nikulin, Vladimir, Huang, Tian-Hsiang and McLachlan, Geoffrey J. (2010). A comparative study of two matrix factorization methods applied to the classification of gene expression rate. In: T. Park, L. Chen, L. Wong, S. Tsui, M. Ng and X. Hu, Proceedings of 2010 IEEE International Conference on Bioinformatics and Biomedicine. IEEE International Conference on Bioinformatics & Biomedicine, Hong Kong, (618-621). 18-21 December 2010.    
Ganesalingam, S and McLachlan, GJ (1980) A Comparison of the Mixture and Classification Approaches to Cluster-Analysis. Communications in Statistics Part A-Theory and Methods, 9 9: 923-933. doi:10.1080/03610928008827932     8 Cited 9 times in Scopus9 0
McLachlan, G (1993) A Connection Between the Logit Model, Normal Discriminant-Analysis, and Multivariate Normal Mixtures - Comment. American Statistician, 47 1: 88-88.     1
Advances in Data Analysis and Classification (2015) Volume 9 Issue 4    
Lloyd-Jones, Luke R., Nguyen, Hien D. and McLachlan, Geoffrey J. (2017) A globally convergent algorithm for a lasso-penalized mixture of linear regression models. Computational Statistics and Data Analysis, 119 19-38. doi:10.1016/j.csda.2017.09.003     0 0 0
Jones, P. N. and McLachlan, G. J. (1990) Algorithm AS 254: maximum likelihood estimation from grouped and truncated data with finite normal mixture models. Applied Statistics - Journal of the Royal Statistical Society Series C, 39 2: 273-282. doi:10.2307/2347776     7 0
McLachlan, GJ, Bean, RW and Peel, D (2002) A mixture model-based approach to the clustering of microarray expression data. Bioinformatics, 18 3: 413-422. doi:10.1093/bioinformatics/18.3.413     332 Cited 369 times in Scopus369 3
Ng, S. K., McLachlan, G. J., Wang, K., Jones, L. Ben-Tovim and Ng, S. W. (2006). A mixture model with random-effects components for clustering correlated gene-expression profiles. In: , , (1745-1752). . doi:10.1093/bioinformatics/btl165     Cited 103 times in Scopus103
Ng, SK, McLachlan, GJ, Wang, K, Jones, LBT and Ng, SW (2006) A Mixture model with random-effects components for clustering correlated gene-expression profiles. Bioinformatics, 22 14: 1745-1752. doi:10.1093/bioinformatics/btl165     92 Cited 103 times in Scopus103 0
Tawiah, Rchard, Yau, Kelvin K. W., McLachlan, Geoffrey J., Chambers, Suzanne and Ng, Shu-Kay (2018) A multilevel survival model with random covariates and unobservable random effects. Statistics in Medicine, . doi:10.1002/sim.8041     3
McLachlan, G. J., Ng, S. K., Adams, P., McGiffin, D. C. and Galbraith, A. J. (1997) An algorithm for fitting mixtures of Gompertz distributions to censored survival data. Journal of Statistical Software, 2 7: 1-23.     Cited 7 times in Scopus7
McLachlan, GJ, McLaren, CE and Matthews, D (1995) An Algorithm for the Likelihood Ratio Test of One Versus 2 Components in a Normal Mixture Model Fitted to Grouped and Truncated Data. Communications in Statistics-Simulation and Computation, 24 4: 965-985. doi:10.1080/03610919508813288     3 Cited 3 times in Scopus3 0
Holt, JN and McLachlan, GJ (1979). Analysis of Some Censored Survival Data From a Large-Scale Study of Melanoma. In: Biometrics. , , (697-697). .     0
McLachlan, G. J., Do, K. and Ambroise, C Analyzing Microarray Gene Expression Data. New York: Wiley-Interscience, 2004.    
McGiffin, DC, Obrien, MF, Galbraith, AJ, McLachlan, GJ, Stafford, EG, Gardner, Mah, Pohlner, PG, Early, L and Kear, L (1993) An Analysis of Risk-Factors for Death and Mode-Specific Death After Aortic-Valve Replacement with Allograft, Xenograft, and Mechanical Valves. Journal of Thoracic and Cardiovascular Surgery, 106 5: 895-911.     19
McGiffin, DC, Galbraith, AJ, OBrien, MF, McLachlan, GJ, Naftel, DC, Adams, P, Reddy, S and Early, L (1997) An analysis of valve re-replacement after aortic valve replacement with biologic devices. Journal of Thoracic And Cardiovascular Surgery, 113 2: 311-318. doi:10.1016/S0022-5223(97)70328-3     29 Cited 32 times in Scopus32 0
Ng, S. K. and McLachlan, G. J. (2003) An EM-based Semi-Parametric Mixture Model Approach to the Regression Analysis of Competing-Risks Data. Statistics In Medicine, 22 7: 1097-1111. doi:10.1002/sim.1371     21 Cited 22 times in Scopus22 0
McLachlan, G. J. (2012). An enduring interest in classification: supervised and unsupervised. In Mohamed Medhat Gaber (Ed.), Journeys to data mining: experiences from 15 renowned researchers (pp. 147-171) Heidelberg, Germany: Springer. doi:10.1007/978-3-642-28047-4_12     0 1
Ng, S. K., McLachlan, G. J. and Lee, A. H. (2006) An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization. Artificial Intelligence In Medicine, 36 3: 257-267. doi:10.1016/j.artmed.2005.07.003     15 Cited 19 times in Scopus19 0
McLachlan, GJ (1980) A Note On Bias Correction in Maximum Likelihood Estimation with Logistic Discrimination. Technometrics, 22 4: 621-627. doi:10.2307/1268202     20 0
Quinn, BG, McLachlan, GJ and Hjort, NL (1987) A Note On the Aitkin-Rubin Approach to Hypothesis-Testing in Mixture-Models. Journal of the Royal Statistical Society Series B-Methodological, 49 3: 311-314.     19
McGiffin, DC, Galbraith, AJ, McLachlan, GJ, Stower, RE, Wong, ML, Stafford, EG, Gardner, Mah, Pohlner, PG and Obrien, MF (1992) Aortic-Valve Infection - Risk-Factors for Death and Recurrent Endocarditis After Aortic-Valve Replacement. Journal of Thoracic and Cardiovascular Surgery, 104 2: 511-520.     108
Do, K. A., McLachlan, G. J., Bean, R. W. and Wen, S. (2007) Application of gene shaving and mixture models to cluster microarray gene expression data. Cancer Informatics, 5 25-43.   3 Cited 1 times in Scopus1
Jones, LBT, Bean, R, McLachlan, G and Zhu, J (2005) Application of mixture models to detect differentially expressed genes. Intelligent Data Engineering And Automated Learning Ideal 2005, Proceedings, 3578 -: 422-431.     1 Cited 1 times in Scopus1
Lee, Sharon X., McLachlan, Geoffrey J. and Pyne, Saumyadipta (2016). Application of mixture models to large datasets. In Saumyadipta Pyne, B. L. S. Prakasa Rao and S. B. Rao (Ed.), Big data analytics: methods and applications (pp. 57-74) New Delhi, India: Springer India. doi:10.1007/978-81-322-3628-3_4     0 0
Tian, Ting, McLachlan, Geoffrey J., Dieter, Mark J. and Basford, Kaye E. (2015) Application of multiple imputation for missing values in three-way three-mode multi-environment trial data. PLoS One, 10 12: e0144370.1-e0144370.25. doi:10.1371/journal.pone.0144370     1 Cited 1 times in Scopus1 0
Tian, T., McLachlan, G., Dieters, M. and Basford, K. (2014). Application of multiple imputation to incomplete three-way three-mode multi-environment trial data. In: Abstracts for the XXVIIth International Biometric Conference. International Biometric Conference, Florence (Italy), (). 6-11 July 2014.    
Lin, Tsung-I, Wu, Pal H., McLachlan, Geoffrey J. and Lee, Sharon X. (2014) A robust factor analysis model using the restricted skew-t distribution. Test, 24 3: 510-531. doi:10.1007/s11749-014-0422-2     10 Cited 11 times in Scopus11 0
Zhao, Yun, Lee, Andy H., Yau, Kelvin K. W., Burke, Valerie and McLachlan, Geoffrey J. (2009) A score test for assessing the cured proportion in the long-term survivor mixture model. Statistics In Medicine, 28 27: 3454-3466. doi:10.1002/sim.3696     9 Cited 9 times in Scopus9 0
Xiang, L., Lee, A. H., Yau, K. K. W. and McLachlan, G. J. (2007) A Score Test for Overdispersion in Zero-Inflated Poisson Mixed Regression Model. Statistics in Medicine, 26 7: 1608-1622. doi:10.1002/sim.2616     37 Cited 39 times in Scopus39 0
Xiang, Liming, Lee, Andy H., Yau, Kelvin K. W. and McLachlan, Geoffrey J. (2006) A Score Test for Zero-Inflation in Correlated Count Data. Statistics In Medicine, 25 10: 1660-1671. doi:10.1002/sim.2308     24 Cited 26 times in Scopus26 0
McLachlan, GJ, Bean, RW and Jones, LBT (2006) A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays. Bioinformatics, 22 13: 1608-1615. doi:10.1093/bioinformatics/btl148     97 Cited 107 times in Scopus107 0
Lee, Sharon X., Leemaqz, Kaleb L. and McLachlan, Geoffrey J. (2016). A simple parallel EM algorithm for statistical learning via mixture models. In: Alan Wee-Chung Liew, Brian Lovell, Clinton Fookes, Jun Zhou, Yongsheng Gao, Michael Blumenstein and Zhiyong Wang, 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA). International Conference on Digital Image Computing, Gold Coast, QLD, Australia, (295-302). 30 November - 2 December,2016. doi:10.1109/DICTA.2016.7796997     3 Cited 5 times in Scopus5 0
Zhao, Yun, Lee, Andy H., Yau, Kelvin K.W. and McLachlan, Geoffrey J. (2011) Assessing the adequacy of Weibull survival models: a simulated envelope approach. Journal of Applied Statistics, 38 10: 2089-2097. doi:10.1080/02664763.2010.545115     1 0 0
McLachlan, GJ (1986) Assessing the Performance of An Allocation Rule. Computers & Mathematics with Applications-Part a, 12 2: 261-272. doi:10.1016/0898-1221(86)90079-9     16 Cited 19 times in Scopus19 0
McLachla.GJ (1974) Asymptotic Distributions of Conditional Error Rate and Risk in Discriminant-Analysis. Biometrika, 61 1: 131-135. doi:10.1093/biomet/61.1.131     16 Cited 19 times in Scopus19 0
Lawoko, Cro and McLachlan, GJ (1986) Asymptotic Error Rates of the W-Statistics and Z-Statistics When the Training Observations Are Dependent. Pattern Recognition, 19 6: 467-471. doi:10.1016/0031-3203(86)90045-2     5 Cited 7 times in Scopus7 0
McLachla.GJ (1972) Asymptotic Expansion for Variance of Errors of Misclassification of Linear Discriminant Function. Australian Journal of Statistics, 14 1: 68-72. doi:10.1111/j.1467-842X.1972.tb00339.x     13 Cited 11 times in Scopus11
McLachla.GJ (1973) Asymptotic Expansion of Expectation of Estimated Error Rate in Discriminant-Analysis. Australian Journal of Statistics, 15 3: 210-214. doi:10.1111/j.1467-842X.1973.tb00201.x     19 Cited 24 times in Scopus24
Nguyen, Hien D. and McLachlan, Geoffrey J. (2014). Asymptotic inference for hidden process regression models. In: 2014 IEEE Workshop on Statistical Signal Processing, SSP 2014. 2014 IEEE Workshop on Statistical Signal Processing (SSP 2014), Gold Coast, Australia, (256-259). 29 June - 2 July 2014. doi:10.1109/SSP.2014.6884624     0 Cited 2 times in Scopus2 0
McLachlan, GJ and Scot, D (1995) Asymptotic Relative Efficiency of the Linear Discriminant Function Under Partial Nonrandom Classification of the Training Data. Journal of Statistical Computation and Simulation, 52 4: 415-426. doi:10.1080/00949659508811689     3 Cited 3 times in Scopus3 0
McLachla.GJ (1972) Asymptotic Results for Discriminant Analysis When Initial Samples Are Misclassified. Technometrics, 14 2: 415-&. doi:10.2307/1267432     20 0
McLachla.GJ (1974) Asymptotic Unbiased Technique for Estimating Error Rates in Discriminant-Analysis. Biometrics, 30 2: 239-249. doi:10.2307/2529646     41 Cited 42 times in Scopus42 0
Suarez, E., Sariol, C. A., Burguete, A. and McLachlan, G. J. (2007) A tutorial in genetic epidemiology and some considerations in statistical modeling. Puerto Rico Health Sciences Journal, 26 4: 401-421.     Cited 3 times in Scopus3

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