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Communications in Statistics - Theory and Methods Volume 49, 2020 - Issue 4
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Original Articles

Variable selection for semiparametric random-effects conditional density models with longitudinal data

Xiaohui YuanSchool of Mathematics and Statistics, Changchun University of Technology, Changchun, Jilin, China; View further author information
,
Yue WangSchool of Mathematics and Statistics, Changchun University of Technology, Changchun, Jilin, China; View further author information
&
Tianqing LiuSchool of Mathematics, Jilin University, Changchun, Jilin, ChinaCorrespondence[email protected]
View further author information
Pages 977-996 | Received 07 Apr 2018, Accepted 26 Nov 2018, Published online: 31 Dec 2018

References

  • Anderson, J. A. 1979. Multivariate logistic compounds. Biometrika 66 (1):17–26. doi: 10.1093/biomet/66.1.17.
  • Chan, K. C. G. 2013. Nuisance parameter elimination for proportional likelihood ratio models with nonignorable missingness and random truncation. Biometrika 100 (1):269–76. doi: 10.1093/biomet/ass056.
  • Chow, Y. S., and H. Teicher. 1978. Probability theory. New York: Springer-Verlag, Inc.
  • Craven, P., and G. Wahba. 1979. Smoothing noisy data with spline functions: estimating the correct degree of smoothing by the method of generalized crossvalidation. Numerische Mathematik 31 (4):377–403. doi: 10.1007/BF01404567.
  • Diggle, P. J., P. Heagerty, K. Y. Liang, and S. L. Zeger. 2002. Analysis of longitudinal data. 2nd ed. New York: Oxford University Press.
  • Fan, J., and R. Li. 2001. Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association 96 (456):1348–60. doi: 10.1198/016214501753382273.
  • Fan, J., and R. Li. 2002. Variable selection for cox’s proportional hazards model and frailty model. The Annals of Statistics 30 (1):74–99. doi: 10.1214/aos/1015362185.
  • Friedman, J., T. Hastie, and R. Tibshirani. 2010. Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33 (1):1–22.
  • Kalbfleisch, J. D. 1978. Likelihood methods and nonparametric tests. Journal of the American Statistical Association 73 (361):167–70. doi: 10.1080/01621459.1978.10480021.
  • Kaslow, R. A., D. G. Ostrow, R. Detels, J. P. Phair, B. F. Polk, and C. R. Rinaldo. 1987. The multicenter AIDS cohort study: Rationale, organization, and selected characteristics of the participants. Am J Epidemiol 126 (2):310–8. doi: 10.1093/aje/126.2.310.
  • Kay, R., and S. Little. 1987. Transformations of the explanatory variables in the logistic regression model for binary data. Biometrika 74 (3):495–501. doi: 10.1093/biomet/74.3.495.
  • Li, J., and M. Gu. 2012. ALASSO for general transformation models with right censored data. Computational Statistics & Data Analysis 56 (8):2583–97. doi: 10.1016/j.csda.2012.02.023.
  • Liang, K. Y., and J. Qin. 2000. Regression analysis under non-standard situations: a pairwise pseudolikelihood approach. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 62 (4):773–86. doi: 10.1111/1467-9868.00263.
  • Liu, T., C. O. Wu, Z. Li, and Y. Li. 2018. Estimation of concomitant intervention effects in longitudinal studies with random-effects conditional density models. Stat SINICA 28 :1333–49.
  • Lu, W., and H. Zhang. 2007. Variable selection for proportional odds model. Statistics in Medicine 26 (20):3771–81.
  • Qin, J., M. Berwick, R. Ashbolt, and T. Dwyer. 2002. Quantifying the change of melanoma incidence by breslow thickness. Biometrics 58 (3):665–70.
  • Tibshirani, R. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society 58:267–88.
  • Tibshirani, R. 1997. The LASSO method for variable selection in the cox model. Statistics in Medicine 16 (4):385–95.
  • Zhao, P., and B. Yu. 2006. On model selection consistency of lasso. Journal of Machine Learning Research 7:2541–63.
  • Zou, H. 2006. The adaptive LASSO and its oracle properties. Journal of the American Statistical Association 101 (476):1418–29. doi: 10.1198/016214506000000735.

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