IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v122y2013icp239-250.html
   My bibliography  Save this article

High-dimensional AIC in the growth curve model

Author

Listed:
  • Fujikoshi, Yasunori
  • Enomoto, Rie
  • Sakurai, Tetsuro

Abstract

The AIC and its modifications have been proposed for selecting the degree in a polynomial growth curve model under a large-sample framework when the sample size n is large, but the dimension p is fixed. In this paper, first we propose a high-dimensional AIC (denoted by HAIC) which is an asymptotic unbiased estimator of the AIC-type risk function defined by the expected log-predictive likelihood or equivalently the Kullback–Leibler information, under a high-dimensional framework such that p/n→c∈[0,1). It is noted that our new criterion gives an estimator with small biases in a wide range of p and n. Next we derive asymptotic distributions of AIC and HAIC under the high-dimensional framework. Through a Monte Carlo simulation, we note that these new approximations are more accurate than the approximations based on a large-sample framework.

Suggested Citation

  • Fujikoshi, Yasunori & Enomoto, Rie & Sakurai, Tetsuro, 2013. "High-dimensional AIC in the growth curve model," Journal of Multivariate Analysis, Elsevier, vol. 122(C), pages 239-250.
    • Handle: RePEc:eee:jmvana:v:122:y:2013:i:c:p:239-250
    DOI: 10.1016/j.jmva.2013.07.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047259X13001383
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jmva.2013.07.006?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Satoh, Kenichi & Kobayashi, Mika & Fujikoshi, Yasunori, 1997. "Variable Selection for the Growth Curve Model," Journal of Multivariate Analysis, Elsevier, vol. 60(2), pages 277-292, February.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Imori, Shinpei & Rosen, Dietrich von, 2015. "Covariance components selection in high-dimensional growth curve model with random coefficients," Journal of Multivariate Analysis, Elsevier, vol. 136(C), pages 86-94.
    2. Xu, Shiyun & Shao, Menglin & Qiao, Wenxuan & Shang, Pengjian, 2018. "Generalized AIC method based on higher-order moments and entropy of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 1127-1138.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Soler, Julia M. P. & Singer, Julio M., 2000. "Optimal covariance adjustment in growth curve models," Computational Statistics & Data Analysis, Elsevier, vol. 33(1), pages 101-110, March.
    2. Siotani, Minoru & Wakaki, Hirofumi, 2006. "Contributions to multivariate analysis by Professor Yasunori Fujikoshi," Journal of Multivariate Analysis, Elsevier, vol. 97(9), pages 1914-1926, October.
    3. Hu, Jianhua & Xin, Xin & You, Jinhong, 2014. "Model determination and estimation for the growth curve model via group SCAD penalty," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 199-213.

    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:eee:jmvana:v:122:y:2013:i:c:p:239-250. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.