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
Authors | Fu, Liya, Yang, Zhuoran, Cai, Fengjing and Wang, You-Gan |
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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 |
Publication dates | |
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 |
https://acuresearchbank.acu.edu.au/item/8y92v/efficient-and-doubly-robust-methods-for-variable-selection-and-parameter-estimation-in-longitudinal-data-analysis
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