IDEAS home Printed from https://ideas.repec.org/p/inn/wpaper/2015-02.html
   My bibliography  Save this paper

Random intercept selection in structured additive regression models

Author

Listed:
  • Helene Roth
  • Stefan Lang
  • Helga Wagner

Abstract

This paper discusses random intercept selection within the context of semiparametric regression models with structured additive predictor (STAR). STAR models can deal simultaneously with nonlinear covariate effects and time trends, unit- or cluster-specific heterogeneity, spatial heterogeneity and complex interactions between covariates of different type. The random intercept selection is based on spike and slab priors for the variances of the random intercept coefficients. The aim is to achieve shrinkage of small random intercept coefficients to zero similar as for the LASSO in frequentist linear models. The mixture structure of the spike and slab prior allows for selective shrinkage, as coefficients are either heavily shrunk under the spike component or left almost unshrunk under the slab component. The hyperparameters of the spike and slab prior are chosen by theoretical considerations based on the prior inclusion probability of a particular random coefficient given the true effect size. Using extensive simulation experiments we compare random intercept models based on spike and slab priors for variances with the usual Inverse Gamma priors. A case study on malnutrition of children in Zambia illustrates the methodology in a real data example.

Suggested Citation

  • Helene Roth & Stefan Lang & Helga Wagner, 2015. "Random intercept selection in structured additive regression models," Working Papers 2015-02, Faculty of Economics and Statistics, Universität Innsbruck.
  • Handle: RePEc:inn:wpaper:2015-02
    as

    Download full text from publisher

    File URL: https://www2.uibk.ac.at/downloads/c4041030/wpaper/2015-02.pdf
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sibhatu, Kibrom T. & Steinhübel, Linda & Siregar, Hermanto & Qaim, Matin & Wollni, Meike, 2022. "Spatial heterogeneity in smallholder oil palm production," Forest Policy and Economics, Elsevier, vol. 139(C).
    2. Brown, Paul T. & Joshi, Chaitanya & Joe, Stephen & Rue, Håvard, 2021. "A novel method of marginalisation using low discrepancy sequences for integrated nested Laplace approximations," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    3. Roger Bivand & Giovanni Millo & Gianfranco Piras, 2021. "A Review of Software for Spatial Econometrics in R," Mathematics, MDPI, vol. 9(11), pages 1-40, June.
    4. Schmidt, Paul & Mühlau, Mark & Schmid, Volker, 2017. "Fitting large-scale structured additive regression models using Krylov subspace methods," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 59-75.
    5. Seiler, Johannes & Harttgen, Kenneth & Kneib, Thomas & Lang, Stefan, 2021. "Modelling children's anthropometric status using Bayesian distributional regression merging socio-economic and remote sensed data from South Asia and sub-Saharan Africa," Economics & Human Biology, Elsevier, vol. 40(C).
    6. Gressani, Oswaldo & Lambert, Philippe, 2021. "Laplace approximations for fast Bayesian inference in generalized additive models based on P-splines," Computational Statistics & Data Analysis, Elsevier, vol. 154(C).
    7. Jamie Roberman & Theophilus I. Emeto & Oyelola A. Adegboye, 2021. "Adverse Birth Outcomes Due to Exposure to Household Air Pollution from Unclean Cooking Fuel among Women of Reproductive Age in Nigeria," IJERPH, MDPI, vol. 18(2), pages 1-15, January.
    8. Piyali Basak & Antonio Linero & Debajyoti Sinha & Stuart Lipsitz, 2022. "Semiparametric analysis of clustered interval‐censored survival data using soft Bayesian additive regression trees (SBART)," Biometrics, The International Biometric Society, vol. 78(3), pages 880-893, September.
    9. Angel G. Ortiz & Daniel Wiese & Kristen A. Sorice & Minhhuyen Nguyen & Evelyn T. González & Kevin A. Henry & Shannon M. Lynch, 2020. "Liver Cancer Incidence and Area-Level Geographic Disparities in Pennsylvania—A Geo-Additive Approach," IJERPH, MDPI, vol. 17(20), pages 1-20, October.
    10. Sibhatu, Kibrom T. & Steinhübel, Linda & Siregar, Hermanto & Qaim, Matin & Wollni, Meike, 2021. "Spatial Heterogeneity of Oil Palm Production in Indonesia: Implications for Intervention Strategies," 2021 Conference, August 17-31, 2021, Virtual 315222, International Association of Agricultural Economists.
    11. Kenneth Harttgen & Stefan Lang & Judith Santer & Johannes Seiler, 2017. "Modeling under-5 mortality through multilevel structured additive regression with varying coefficients for Asia and Sub-Saharan Africa," Working Papers 2017-15, Faculty of Economics and Statistics, Universität Innsbruck.

    More about this item

    Keywords

    Bayesian hierarchical models; Bayesian model choice; MCMC; P-splines; spike and slab priors;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inn:wpaper:2015-02. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Janette Walde (email available below). General contact details of provider: https://edirc.repec.org/data/fuibkat.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.