High-dimensional covariance estimation by pairwise likelihood truncation
通过成对似然截断估计高维协方差
Summary Pairwise likelihood is an approximation of the full likelihood function that facilitates the analysis of high-dimensional covariance models. By combining marginal bivariate likelihoods, it effectively simplifies high-dimensional dependencies, making the estimation process more manageable. We introduce estimation of sparse high-dimensional covariance matrices by maximizing a truncated version of the pairwise likelihood function, obtained by including pairwise terms…