Composite likelihood inference of fractional Gaussian processes with sequentially optimal subset selection
分数高斯过程的复合似然推断与序贯最优子集选择
The composite likelihood method reduces the computational cost of parameter estimation in time series by considering several subsets of observations instead of all observations at once. The asymptotic properties of this method are related to the Godambe information, an extension of the Fisher information that accounts for the dependence between subsets of observations. We aim to apply this method to linear Gaussian models, in particular fractional Brownian motion and…