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    Please use this identifier to cite or link to this item: http://ir.cnu.edu.tw/handle/310902800/22383


    標題: Fitting Non-linear Models with AR(1) Structure: A Marginal Likelihood Approach
    擬合AR(1)結構之非線性模式:邊際概似法
    作者: H. Chiu
    V. Ramakrishnan
    貢獻者: 保健營養系
    關鍵字: Longitudinal data
    Non-linear models
    Growth curves
    Marginal likelihood
    AR(1)
    Restriced maximum likelihood
    Fundal height
    縱貫性數據
    非線性模式
    生長曲線
    邊際概似法
    AR(1)
    erstricted最大概似法
    胎兒基底高度
    日期: 2000
    上傳時間: 2010-03-22 11:23:30 (UTC+8)
    摘要: In the estimation of growth models the two main problems are a) they are oftennon-linear and b) the errors are serially correlated. Methodology for non-linear growth models with serially correlated errors usig the mixed models approach are available in the literature (Lindstrom and Bates (1990)). These methods are mainly useful for estimating population averages of growth from data that are observed over short time periods. In thisdissertation a method for estimating individual specific non-linear growth curves under first order auto-regressive (AR(1)) error structure for data that are observed over a long period of time is presented. In order to reduce the computational complexity the maximum likelihood estimation procedure is separated into two parts using a quasi-likelihood method called here themarginal likelihood method. In this method the AR(1)‘nuisance’parameter is estimated from a marginal likelihood that contains the AR(1) parameter alone, under a polynomial approximation of the non-linear model. Then the non-linear model parameters are estimated by assuming the AR(1) parameter to be known, where the known value is the estimate. Simulation results are presented to discuss the properties of the properties of the marginal likelihood estimation method and to test the validity of the polynomial approximation. The method is also compared with the Restricted Maximum Likelihood Estimation (REML) proposed under a mixed model structed by Harville(1977). The method is applied to two data sets from maternal and child health studies. These data on fundal heights are analyzed using the marginal likelihood approach and compared with other approaches.
    關聯: 嘉南學報 26 :p.153-167
    Appears in Collections:[嘉南學報] 26 期 (2000)
    [保健營養系(所) ] 期刊論文

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