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Model selection in irregular problems: Applications to mapping quantitative trait loci

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  • David Siegmund

Abstract

Two methods of model selection are discussed for changepoint-like problems, especially those arising in genetic linkage analysis. The first is a method that selects the model with the smallest p-value, while the second is a modification of the Bayes information criterion. The methods are compared theoretically and on examples from the literature. For these examples, they are roughly comparable although the p-value-based method is somewhat more liberal in selecting a high-dimensional model. Copyright 2004, Oxford University Press.

Suggested Citation

  • David Siegmund, 2004. "Model selection in irregular problems: Applications to mapping quantitative trait loci," Biometrika, Biometrika Trust, vol. 91(4), pages 785-800, December.
    • Handle: RePEc:oup:biomet:v:91:y:2004:i:4:p:785-800
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    File URL: http://hdl.handle.net/10.1093/biomet/91.4.785
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    Cited by:

    1. Yawei He & Zehua Chen, 2016. "The EBIC and a sequential procedure for feature selection in interactive linear models with high-dimensional data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 68(1), pages 155-180, February.
    2. Baierl, Andreas & Futschik, Andreas & Bogdan, Malgorzata & Biecek, Przemyslaw, 2007. "Locating multiple interacting quantitative trait loci using robust model selection," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6423-6434, August.
    3. Ryoto Ozaki & Yoshiyuki Ninomiya, 2023. "Information criteria for detecting change‐points in the Cox proportional hazards model," Biometrics, The International Biometric Society, vol. 79(4), pages 3050-3065, December.
    4. Chun Wang, 2021. "Using Penalized EM Algorithm to Infer Learning Trajectories in Latent Transition CDM," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 167-189, March.
    5. Yoshiyuki Ninomiya, 2015. "Change-point model selection via AIC," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(5), pages 943-961, October.
    6. Nancy R. Zhang & David O. Siegmund, 2007. "A Modified Bayes Information Criterion with Applications to the Analysis of Comparative Genomic Hybridization Data," Biometrics, The International Biometric Society, vol. 63(1), pages 22-32, March.
    7. Shan Luo & Jinfeng Xu & Zehua Chen, 2015. "Extended Bayesian information criterion in the Cox model with a high-dimensional feature space," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(2), pages 287-311, April.
    8. Satoshi Kuriki & Yoshiaki Harushima & Hironori Fujisawa & Nori Kurata, 2014. "Approximate tail probabilities of the maximum of a chi-square field on multi-dimensional lattice points and their applications to detection of loci interactions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(4), pages 725-757, August.
    9. Jian Li & Fugee Tsung & Changliang Zou, 2013. "Directional change‐point detection for process control with multivariate categorical data," Naval Research Logistics (NRL), John Wiley & Sons, vol. 60(2), pages 160-173, March.
    10. Yoshiyuki Ninomiya & Hironori Fujisawa, 2007. "A Conservative Test for Multiple Comparison Based on Highly Correlated Test Statistics," Biometrics, The International Biometric Society, vol. 63(4), pages 1135-1142, December.
    11. Wang, Tao & Zhu, Lixing, 2011. "Consistent tuning parameter selection in high dimensional sparse linear regression," Journal of Multivariate Analysis, Elsevier, vol. 102(7), pages 1141-1151, August.
    12. Pan, Jianmin & Chen, Jiahua, 2006. "Application of modified information criterion to multiple change point problems," Journal of Multivariate Analysis, Elsevier, vol. 97(10), pages 2221-2241, November.

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