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Found 6 papers, 1 papers with code
Goodness-of-Fit Tests for Inhomogeneous Random Graphs
Hypothesis testing of random networks is an emerging area of modern research, especially in the high-dimensional regime, where the number of samples is smaller or comparable to the size of the graph.
Degree Heterogeneity in Higher-Order Networks: Inference in the Hypergraph $\boldsymbolβ$-Model
To begin with, we derive the rates of convergence of the maximum likelihood (ML) estimate and establish their minimax rate optimality.
Bootstrapped Edge Count Tests for Nonparametric Two-Sample Inference Under Heterogeneity
In this regime, we study the asymptotic behavior of weighted edge count test statistic and show that it can be effectively re-calibrated to detect arbitrary deviations from the composite null.
Boosting the Power of Kernel Two-Sample Tests
The kernel two-sample test based on the maximum mean discrepancy (MMD) is one of the most popular methods for detecting differences between two distributions over general metric spaces.
Estimation in Tensor Ising Models
In this paper, we consider the problem of estimating the natural parameter of the $p$-tensor Ising model given a single sample from the distribution on $N$ nodes.
Testing Closeness With Unequal Sized Samples
We consider the problem of closeness testing for two discrete distributions in the practically relevant setting of \emph{unequal} sized samples drawn from each of them.
