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Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data

Author

Listed:
  • Fahrmeir, Ludwig

    (Department of Statistics, Ludwig Maxmilians University, Munich, Germany)

  • Kneib, Thomas

    (Department of Statistics, Ludwig Maxmilians University, Munich, Germany)

Abstract

Several recent advances in smoothing and semiparametric regression are presented in this book from a unifying, Bayesian perspective. Simulation-based full Bayesian Markov chain Monte Carlo (MCMC) inference, as well as empirical Bayes procedures closely related to penalized likelihood estimation and mixed models, are considered here. Throughout, the focus is on semiparametric regression and smoothing based on basis expansions of unknown functions and effects in combination with smoothness priors for the basis coefficients. Beginning with a review of basic methods for smoothing and mixed models, longitudinal data, spatial data and event history data are treated in separate chapters. Worked examples from various fields such as forestry, development economics, medicine and marketing are used to illustrate the statistical methods covered in this book. Most of these examples have been analysed using implementations in the Bayesian software, BayesX, and some with R Codes. These, as well as some of the data sets, are made publicly available on the website accompanying this book.

Suggested Citation

  • Fahrmeir, Ludwig & Kneib, Thomas, 2011. "Bayesian Smoothing and Regression for Longitudinal, Spatial and Event History Data," OUP Catalogue, Oxford University Press, number 9780199533022.
  • Handle: RePEc:oxp:obooks:9780199533022
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    Citations

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    Cited by:

    1. Alexander März & Nadja Klein & Thomas Kneib & Oliver Musshoff, 2016. "Analysing farmland rental rates using Bayesian geoadditive quantile regression," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 43(4), pages 663-698.
    2. Damien Rousselière, 2017. "A flexible approach to age dependence in organizational mortality. Comparing the life duration for cooperative and non-cooperative enterprises using a Bayesian Generalized Additive Discrete Time Survi," Working Papers SMART - LERECO 17-08, INRA UMR SMART-LERECO.
    3. Shortridge Ashton & Goldsberry Kirk & Adams Matthew, 2014. "Creating space to shoot: quantifying spatial relative field goal efficiency in basketball," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(3), pages 1-11, September.
    4. Jagannadha Pawan Tamvada, 2015. "The Spatial Distribution of Self-Employment in India: Evidence from Semiparametric Geoadditive Models," Regional Studies, Taylor & Francis Journals, vol. 49(2), pages 300-322, February.
    5. repec:jss:jstsof:v:074:i01 is not listed on IDEAS
    6. repec:eee:ecomod:v:355:y:2017:i:c:p:1-11 is not listed on IDEAS
    7. Ezra Gayawan & Samson B. Adebayo, 2013. "A Bayesian semiparametric multilevel survival modelling of age at first birth in Nigeria," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 28(45), pages 1339-1372, June.
    8. Andreas Groll & Gerhard Tutz, 2017. "Variable selection in discrete survival models including heterogeneity," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(2), pages 305-338, April.
    9. Susanne Konrath & Ludwig Fahrmeir & Thomas Kneib, 2015. "Bayesian accelerated failure time models based on penalized mixtures of Gaussians: regularization and variable selection," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(3), pages 259-280, July.

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