Pseudo-likelihood-based M-estimation of random graphs with dependent edges and parameter vectors of increasing dimension
依赖边随机图中参数维数增长的伪似然 M 估计
An important question in statistical network analysis is how to estimate models of discrete and dependent network data with intractable likelihood functions, without sacrificing computational scalability and statistical guarantees. We demonstrate that scalable estimation of random graph models with dependent edges is possible, by establishing convergence rates of pseudo-likelihood-based M-estimators for discrete undirected graphical models with exponential parameterizations…