Serial Correlation in Unequally Spaced Longitudinal Data
研究了不等间距纵向数据中个体内误差的序列相关，用连续时间自回归移动平均建模，提出了两种精确似然计算方法，适用于统计和数据分析领域。
Serial correlation in the within subject error structure in longitudinal data with unequally spaced observations is modelled using continuous time autoregressive moving averages. The models considered have both fixed and random effects in addition to serially correlated within subject errors. Two approaches are presented for calculating the exact likelihood for a model when the errors are Gaussian. The first calculates the covariance matrices for each subject for assumed values of the unknown parameters and estimates the fixed parameters by weighted least squares. The second uses a state space model and the Kalman filter to calculate the exact likelihood. Both methods involve the use of complex arithmetic. Nonlinear optimization is used to obtain maximum likelihood estimates of the parameters.