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Variable selection and estimation for longitudinal survey data

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  • Wang, Li
  • Wang, Suojin
  • Wang, Guannan

Abstract

There is wide interest in studying longitudinal surveys where sample subjects are observed successively over time. Longitudinal surveys have been used in many areas today, for example, in the health and social sciences, to explore relationships or to identify significant variables in regression settings. This paper develops a general strategy for the model selection problem in longitudinal sample surveys. A survey weighted penalized estimating equation approach is proposed to select significant variables and estimate the coefficients simultaneously. The proposed estimators are design consistent and perform as well as the oracle procedure when the correct submodel was known. The estimating function bootstrap is applied to obtain the standard errors of the estimated parameters with good accuracy. A fast and efficient variable selection algorithm is developed to identify significant variables for complex longitudinal survey data. Simulated examples are illustrated to show the usefulness of the proposed methodology under various model settings and sampling designs.

Suggested Citation

  • Wang, Li & Wang, Suojin & Wang, Guannan, 2014. "Variable selection and estimation for longitudinal survey data," Journal of Multivariate Analysis, Elsevier, vol. 130(C), pages 409-424.
    • Handle: RePEc:eee:jmvana:v:130:y:2014:i:c:p:409-424
    DOI: 10.1016/j.jmva.2014.05.006
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    References listed on IDEAS

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    1. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    2. Wang, Li & Wang, Suojin, 2011. "Nonparametric additive model-assisted estimation for survey data," Journal of Multivariate Analysis, Elsevier, vol. 102(7), pages 1126-1140, August.
    3. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    4. Johnson, Brent A. & Lin, D.Y. & Zeng, Donglin, 2008. "Penalized Estimating Functions and Variable Selection in Semiparametric Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 672-680, June.
    5. Wenjiang J. Fu, 2003. "Penalized Estimating Equations," Biometrics, The International Biometric Society, vol. 59(1), pages 126-132, March.
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    Cited by:

    1. Laura Dumitrescu & Wei Qian & J. N. K. Rao, 2021. "Inference for longitudinal data from complex sampling surveys: An approach based on quadratic inference functions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 246-274, March.
    2. Nathaniel W. Anderson & Anna J. Markowitz & Daniel Eisenberg & Neal Halfon & Kristin Anderson Moore & Frederick J. Zimmerman, 2022. "The Child and Adolescent Thriving Index 1.0: Developing a Measure of the Outcome Indicators of Well-Being for Population Health Assessment," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 15(6), pages 2015-2042, December.

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