Approximating fragmented functional data by segments of Markov chains
针对仅观测到部分域的函数型数据（碎片），结合马尔可夫链与非参数平滑技术，实现曲线延拓、线性预测及均值协方差估计，并给出近似预测区间。
We consider curve extension and linear prediction for functional data observed only on a part of their domain, in the form of fragments. We suggest an approach based on a combination of Markov chains and nonparametric smoothing techniques, which enables us to extend the observed fragments and construct approximated prediction intervals around them, construct mean and covariance function estimators, and derive a linear predictor. The procedure is illustrated on real and simulated data.