Advanced search
Publication Cover
Journal of Statistical Computation and Simulation Volume 92, 2022 - Issue 13
Submit an article Journal homepage
299
Views
1
CrossRef citations to date
0
Altmetric
Research Article

A procedure for variable selection in double generalized linear models

Lucas L. Cavalaroa Department of Statistics, Federal University of São Carlos, São Carlos, Brazil;b Department of Applied Mathematics and Statistics, University of São Paulo, São Carlos, BrazilCorrespondence[email protected]
View further author information
&
Gustavo H. A. Pereiraa Department of Statistics, Federal University of São Carlos, São Carlos, BrazilORCID Iconhttps://orcid.org/0000-0002-5491-0937View further author information
ORCID Icon
Pages 2703-2720 | Received 21 Jul 2021, Accepted 17 Feb 2022, Published online: 08 Mar 2022

References

  • Smyth GK. Generalized linear models with varying dispersion. J R Stat Soc. 1989;51(1):47–60.
  • Akaike H. A new look at the statistical model identification. IEEE Trans Automat Contr. 1974;19(6):716–723.
  • Akaike H. A Bayesian analysis of the minimum AIC procedure. Ann Inst Stat Math. 1978;30:9–14.
  • Schwarz G. Estimating the dimension of a model. Ann Stat. 1978;6(2):461–464.
  • Hannan EJ, Quinn BG. The determination of the order of an autoregression. J R Stat Soc. 1979;41(2):190–195.
  • Hurvich CM, Tsai C-L. Regression and time series model selection in small samples. Biometrika. 1989;76(2):297–307.
  • Draper N, Smith H. Applied regression analysis. New York: John Wiley and Sons; 1967.
  • Claeskens G, Hjort NL. Model selection and model averaging. Vol. 330. Cambridge: Cambridge University Press; 2008.
  • Cottet R, Kohn RJ, Nott DJ. Variable selection and model averaging in semiparametric overdispersed generalized linear models. J Am Stat Assoc. 2008;103(482):661–671.
  • Xu D, Zhang Z, Wu L. Variable selection in high-dimensional double generalized linear models. Stat Pap. 2014;55(2):327–347.
  • Bayer F, Cribari-Neto F. Bootstrap-based model selection criteria for beta regressions. TEST. 2015;24(4):776–795.
  • Bayer FM, Cribari-Neto F. Model selection criteria in beta regression with varying dispersion. Commun Stat Simul Comput. 2017;46(1):729–746.
  • Antoniadis A, Gijbels I, Lambert-Lacroix S, et al. Joint estimation and variable selection for mean and dispersion in proper dispersion models. Electron J Stat. 2016;10(1):1630–1676.
  • R Development Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2008.
  • Stasinopoulos MD, Rigby RA, Heller GZ, et al. Flexible regression and smoothing: using GAMLSS in R. Boca Raton: Chapman and Hall/CRC Press; 2017.
  • Ramires GT, Nakamura RL, Righetto JA, et al. Validation of stepwise-based procedure in GAMLSS. J Data Sci. 2021;19(1):96–110.
  • Stasinopoulos DM, Rigby RA. Generalized additive models for location scale and shape (GAMLSS) in R. J Stat Softw. 2007;23(7):1–46.
  • Demirtas H. Pseudo-random number generation in R for commonly used multivariate distributions. J Mod Appl Stat Methods. 2004;3(2):485–497.
  • Buckland ST, Burnham KP, Augustin NH. Model selection: an integral part of inference. Biometrics. 1997;53(2):603–618.
  • Winner. PGA performance statistics and winnings; 2020 [cited 2020 Jun 3]. Available from: http://users.stat.ufl.edu/winner/data/pga2004.dat
  • Filliben JJ. The probability plot correlation coefficient test for normality. Technometrics. 1975;17(1):111–117.
  • Dunn PK, Smyth GK. Randomized quantile residuals. J Comput Graph Stat. 1996;5(3):236–244.
  • Breiman L. Random forests. Mach Learn. 2001;45(1):5–32.
  • Hoerl AE, Kennard RW. Ridge regression: biased estimation for nonorthogonal problems. Technometrics. 1970;12(1):55–67.
  • Smithson M, Verkuilen J. A better lemon squeezer? maximum-likelihood regression with beta-distributed dependent variables. Psychol Methods. 2006;11(1):54–71.
  • Santos-Neto M, Cysneiros FJA, Leiva V, et al. Reparameterized Birnbaum–Saunders regression models with varying precision. Electron J Stat. 2016;10(2):2825–2855.
  • Rigby RA, Stasinopoulos DM. Generalized additive models for location, scale and shape. J R Stat Soc. 2005;54(3):507–554.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.