Geoffrey J McLachlan
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Explore the profile of Geoffrey J McLachlan including associated specialties, affiliations and a list of published articles.
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41
Citations
808
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Recent Articles
1.
Farzammehr M, Zadkarami M, McLachlan G, Lee S
J Appl Stat
. 2022 Jun;
47(5):804-826.
PMID: 35707324
This paper proposes a new regression model for the analysis of spatial panel data in the case of spatial heterogeneity and non-normality. In empirical economic research, the normality of error...
2.
Ng S, Tawiah R, McLachlan G, Gopalan V
Biostatistics
. 2021 Nov;
24(1):108-123.
PMID: 34752610
Multimorbidity constitutes a serious challenge on the healthcare systems in the world, due to its association with poorer health-related outcomes, more complex clinical management, increases in health service utilization and...
3.
Tawiah R, McLachlan G, Ng S
Biometrics
. 2019 Dec;
76(3):753-766.
PMID: 31863594
In the study of multiple failure time data with recurrent clinical endpoints, the classical independent censoring assumption in survival analysis can be violated when the evolution of the recurrent events...
4.
Tawiah R, McLachlan G, Ng S
Stat Methods Med Res
. 2019 Jul;
29(5):1368-1385.
PMID: 31293217
Many medical studies yield data on recurrent clinical events from populations which consist of a proportion of cured patients in the presence of those who experience the event at several...
5.
Tawiah R, Yau K, McLachlan G, Chambers S, Ng S
Stat Med
. 2018 Nov;
38(6):1036-1055.
PMID: 30474216
We present a multilevel frailty model for handling serial dependence and simultaneous heterogeneity in survival data with a multilevel structure attributed to clustering of subjects and the presence of multiple...
6.
Lee S, Leemaqz K, McLachlan G
IEEE Trans Neural Netw Learn Syst
. 2018 Jul;
29(11):5581-5591.
PMID: 29993871
Finite mixtures of skew distributions provide a flexible tool for modeling heterogeneous data with asymmetric distributional features. However, parameter estimation via the Expectation-Maximization (EM) algorithm can become very time consuming...
7.
Whole-Volume Clustering of Time Series Data from Zebrafish Brain Calcium Images via Mixture Modeling
Nguyen H, Ullmann J, McLachlan G, Voleti V, Li W, Hillman E, et al.
Stat Anal Data Min
. 2018 May;
11(1):5-16.
PMID: 29725490
Calcium is a ubiquitous messenger in neural signaling events. An increasing number of techniques are enabling visualization of neurological activity in animal models via luminescent proteins that bind to calcium...
8.
Ahfock D, Pyne S, Lee S, McLachlan G
Comput Stat Data Anal
. 2017 May;
104:79-90.
PMID: 28496285
The statistical matching problem involves the integration of multiple datasets where some variables are not observed jointly. This missing data pattern leaves most statistical models unidentifiable. Statistical inference is still...
9.
Nguyen H, McLachlan G, Orban P, Bellec P, Janke A
Neural Comput
. 2017 Jan;
29(4):990-1020.
PMID: 28095191
Mixture of autoregressions (MoAR) models provide a model-based approach to the clustering of time series data. The maximum likelihood (ML) estimation of MoAR models requires evaluating products of large numbers...
10.
Nguyen H, McLachlan G, Hill M
Methods Mol Biol
. 2016 Dec;
1549:109-117.
PMID: 27975287
Comparative profiling proteomics experiments are important tools in biological research. In such experiments, tens to hundreds of thousands of peptides are measured simultaneously, with the goal of inferring protein abundance...