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    題名: A goodness-of-fit test for logistic-normal models using nonparametric smoothing method
    作者: Lin, Kuo-Chin;Chen, Yi-Ju
    貢獻者: 淡江大學統計學系
    關鍵詞: Goodness-of-fit;Logistic-normal models;Longitudinal binary data;Nonparametric smoothing
    日期: 2011-02
    上傳時間: 2013-06-13 11:27:52 (UTC+8)
    出版者: Amsterdam: Elsevier BV * North-Holland
    摘要: Logistic-normal models can be applied for analysis of longitudinal binary data. The aim of this article is to propose a goodness-of-fit test using nonparametric smoothing techniques for checking the adequacy of logistic-normal models. Moreover, the leave-one-out cross-validation method for selecting the suitable bandwidth is developed. The quadratic form of the proposed test statistic based on smoothing residuals provides a global measure for checking the model with categorical and continuous covariates. The formulae of expectation and variance of the proposed statistics are derived, and their asymptotic distribution is approximated by a scaled chi-squared distribution. The power performance of the proposed test for detecting the interaction term or the squared term of continuous covariates is examined by simulation studies. A longitudinal dataset is utilized to illustrate the application of the proposed test.
    關聯: Journal of Statistical Planning and Inference 141(2), pp.1069-1076
    DOI: 10.1016/j.jspi.2010.09.016
    顯示於類別:[統計學系暨研究所] 期刊論文

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