Efficient and doubly-robust methods for variable selection and parameter estimation in longitudinal data analysis

Journal article

Fu, Liya, Yang, Zhuoran, Cai, Fengjing and Wang, You-Gan. (2021). Efficient and doubly-robust methods for variable selection and parameter estimation in longitudinal data analysis. Computational Statistics. 36(2), pp. 781-804. https://doi.org/10.1007/s00180-020-01038-3

Publication dates
Authors	Fu, Liya, Yang, Zhuoran, Cai, Fengjing and Wang, You-Gan
Abstract	New technologies have produced increasingly complex and massive datasets, such as next generation sequencing and microarray data in biology, dynamic treatment regimes in clinical trials and long-term wide-scale studies in the social sciences. Each study exhibits its unique data structure within individuals, clusters and possibly across time and space. In order to draw valid conclusion from such large dimensional data, we must account for intracluster correlations, varying cluster sizes, and outliers in response and/or covariate domains to achieve valid and efficient inferences. A weighted rank-based method is proposed for selecting variables and estimating parameters simultaneously. The main contribution of the proposed method is four fold: (1) variable selection using adaptive lasso is extended to robust rank regression so that protection against outliers in both response and predictor variables is obtained; (2) within-subject correlations are incorporated so that efficiency of parameter estimation is improved; (3) the computation is convenient via the existing function in statistical software R. (4) the proposed method is proved to have desirable asymptotic properties for fixed number of covariates (p). Simulation studies are carried out to evaluate the proposed method for a number of scenarios including the cases when p equals to the number of subjects. The simulation results indicate that the proposed method is efficient and robust. A hormone dataset is analyzed for illustration. By adding additional redundant variables as covariates, the penalty approach and weighting schemes are proven to be effective.
Keywords	Correlated data; Outliers; Rank-based method; Variable selection
Year	01 Jan 2021
Journal	Computational Statistics
Journal citation	36 (2), pp. 781-804
Publisher	Springer
ISSN	0943-4062
Digital Object Identifier (DOI)	https://doi.org/10.1007/s00180-020-01038-3
Web address (URL)	https://link.springer.com/article/10.1007/s00180-020-01038-3
Open access	Published as non-open access
Research or scholarly	Research
Page range	781-804
Publisher's version	License All rights reserved File Access Level Controlled
Output status	Published
Print	12 Oct 2020
Publication process dates
Accepted	01 Oct 2020
Deposited	11 Jan 2023
Additional information	©Springer-Verlag GmbH Germany, part of Springer Nature 2020
Place of publication	Germany

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Wang, You-Gan, Zhao, Yudong and Fu, Liya. (2016). The Buckley–James estimator and induced smoothing. Australian and New Zealand Journal of Statistics. 58(2), pp. 211-225. https://doi.org/10.1111/anzs.12155

Maximum likelihood estimation of natural mortality and quantification of temperature effects on catchability of brown tiger prawn (penaeus esculentus) in Moreton Bay (Australia) using logbook data

Kienzle, Marco, Sterling, David, Zhou, Shijie and Wang, You-Gan. (2016). Maximum likelihood estimation of natural mortality and quantification of temperature effects on catchability of brown tiger prawn (penaeus esculentus) in Moreton Bay (Australia) using logbook data. Ecological Modelling. 322, pp. 1-9. https://doi.org/10.1016/j.ecolmodel.2015.11.008

Otolith morphology of four mackerel species (Scomberomorus spp.) in Australia : Species differentiation and prediction for fisheries monitoring and assessment

Zischke, Mitchell T., Litherland, Lenore, Tilyard, Benjamin R., Stratford, Nicholas J., Jones, Ebony L. and Wang, You-Gan. (2016). Otolith morphology of four mackerel species (Scomberomorus spp.) in Australia : Species differentiation and prediction for fisheries monitoring and assessment. Fisheries Research. 176, pp. 39-47. https://doi.org/10.1016/j.fishres.2015.12.003

Efficient parameter estimation via Gaussian copulas for quantile regression with longitudinal data

Fu, Liya and Wang, You-Gan. (2016). Efficient parameter estimation via Gaussian copulas for quantile regression with longitudinal data. Journal of Multivariate Analysis. 143, pp. 492-502. https://doi.org/10.1016/j.jmva.2015.07.004

Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data

Lloyd-Jones, Luke R., Nguyen, Hien D., McLachlan, Geoffrey J., Sumpton, Wayne and Wang, You-Gan. (2016). Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data. Biometrics. 72(4), pp. 1255-1265. https://doi.org/10.1111/biom.12531

Movement and growth of the coral reef holothuroids Bohadschia argus and Thelenota ananas

Purcell, Steven W., Piddocke, Toby P., Dalton, Steven J. and Wang, You-Gan. (2016). Movement and growth of the coral reef holothuroids Bohadschia argus and Thelenota ananas. Marine Ecology Progress Series. 551, pp. 201-214. https://doi.org/10.3354/meps11720

Improved confidence intervals for the linkage disequilibrium method for estimating effective population size

Jones, A. T., Ovenden, J. R. and Wang, Y.-G.. (2016). Improved confidence intervals for the linkage disequilibrium method for estimating effective population size. Heredity. 117(4), pp. 217-223. https://doi.org/10.1038/hdy.2016.19