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Variable selection of varying dispersion student-t regression models

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Abstract

The Student-t regression model is a useful extension of the normal model, which can be used for statistical modeling of data sets involving errors with heavy tails and/or outliers and providesrobust estimation of means and regression coefficients. In this paper, the varying dispersion Student-t regression model is discussed, in which both the mean and the dispersion depend upon explanatory variables. The problem of interest is simultaneously select significant variables both in mean and dispersion model. A unified procedure which can simultaneously select significant variable is given. With appropriate selection of the tuning parameters, the consistency and the oracle property of the regularized estimators are established. Both the simulation study and two real data examples are used to illustrate the proposed methodologies.

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References

  1. Lange K, Little R, and Taylor J, Robust statistical modelling using the t distribution, Journal of the American Statistical Association, 1989, 84: 881–896.

    MathSciNet  Google Scholar 

  2. Smyth G and Verbyla A, Adjusted likelihood methods for modelling dispersion in generalized linear models, Environmetrics, 1999, 10: 696–709.

    Article  Google Scholar 

  3. Park R, Estimation with heteroscedastic error terms, Econometrica, 1966, 34: 888.

    Article  Google Scholar 

  4. Taylor J and Verbyla A, Joint modelling of location and scale parameters of the t distribution, Statistics Modelling, 2004, 4: 91–112

    Article  MATH  MathSciNet  Google Scholar 

  5. Wu L and Li H, Variable selection for joint mean and dispersion models of the inverse Gaussian distribution, Metrika, 2012, 75: 795–808.

    Article  MATH  MathSciNet  Google Scholar 

  6. Wu L, Zhang Z, and Xu D, Variable selection in joint location and scale models of the skew-normal distribution, Journal of Statistical Computation and Simulation, 2013, 83: 1266–1278.

    Article  MathSciNet  Google Scholar 

  7. Carroll R and Ruppert D, Transformation and Weighting in Regression, Chapman and Hall, New York, 1988.

    Book  MATH  Google Scholar 

  8. Lin J, Zhu L, and Xie F, Heteroscedasticity diagnostics for t linear regression models, Metrika, 2009, 70: 59–77.

    Article  MathSciNet  Google Scholar 

  9. Zhang Z and Wang D, Simultaneous variable selection for heteroscedastic regression models, Sceince China Mathematics, 2011, 54(: 515–530.

    Article  MATH  Google Scholar 

  10. Wu L, Zhang Z, and Xu D, Variable selection in joint mean and variance models, Systems Engineering—Theory & Practice, 2012, 32(8): 1754–1760.

    Google Scholar 

  11. Lucas A, Robustness of the Student-t based M-estimator, Communications in Statistics, Theory and Methods, 1997, 26: 1165–1182.

    Article  MATH  Google Scholar 

  12. Tibshirani R, Regression shrinkage and selection via the LASSO, Journal of the Royal Statistical Society,Series B, 1996, 58: 267–288.

    MATH  MathSciNet  Google Scholar 

  13. Zou H, The adaptive LASSO and its oracle properties, Journal of the American Statistical Association, 2006, 101: 1418–1429.

    Article  MATH  MathSciNet  Google Scholar 

  14. Yuan M and Lin Y, Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society, Series B, 2006, 68: 49–67.

    Article  MATH  MathSciNet  Google Scholar 

  15. Fan J and Li R, Variable selection via nonconcave penalized likelihood and its oracle properties, Journal of the American Statistical Association, 2001, 96: 1348–1360.

    Article  MATH  MathSciNet  Google Scholar 

  16. Fu W, Penalized regression: The bridge versus the LASSO, Journal of Computational and Graphical Statistics, 1998, 7: 397–416.

    MathSciNet  Google Scholar 

  17. Breiman L, Better subset selection using nonnegative garrote, Techonometrics, 1995, 37: 373–384.

    Article  MATH  MathSciNet  Google Scholar 

  18. Zou H and Li R, One-step sparse estimates in nonconcave penalized likelihood models (with discussion), The Annals of Statistics, 2008, 36: 1509–1533.

    Article  MATH  MathSciNet  Google Scholar 

  19. Zhang C, Nearly unbiased variable selection under minimax concave penalty, The Annals of Statistics, 2010, 38: 894–942.

    Article  MATH  MathSciNet  Google Scholar 

  20. Wang H, Li R, and Tsai C, Tuning parameter selectors for the smoothly clipped absolute deviation method, Biometrika, 2007, 94: 553–568.

    Article  MATH  MathSciNet  Google Scholar 

  21. Zhao P and Xue L, Variable selection for semiparametric varying coefficient partially linear models, Statistics and Probability Letters, 2009, 79: 2148–2157.

    Article  MATH  MathSciNet  Google Scholar 

  22. Weisberg S, Applied Linear Regression, Wiley, New York, 1985.

    MATH  Google Scholar 

  23. Cook R and Weisberg S, Residuals and Influence in Regression, Chapman and Hall, New York, 1982.

    MATH  Google Scholar 

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Correspondence to Riquan Zhang.

Additional information

The research was supported in part by the National Natural Science Foundation of China under Grant Nos. 11171112, 11101114, 11201190, the National Statistical Science Research Major Program of China under GrantNo. 2011LZ051, the 111 Project of China under Grant No. B14019, the Doctoral Fund of Ministry of Educationof China under Grant No. 20130076110004, the Natural Science Project of Jiangsu Province EducationDepartment under Grant No. 13KJB110024, the Natural Science Fund of Nantong University under Grant No.13ZY001, and the Research Project of Social Science and Humanity Fund of the Ministry of Education underGrant No. 14YJC910007.

This paper was recommended for publication by Editor SUN Liuquan.

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Zhao, W., Zhang, R. Variable selection of varying dispersion student-t regression models. J Syst Sci Complex 28, 961–977 (2015). https://doi.org/10.1007/s11424-014-2223-9

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  • DOI: https://doi.org/10.1007/s11424-014-2223-9

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