Comparison of statistical procedures for Gaussian graphical model selection

I. Grechikhin; V. A. Kalyagin

doi:10.1007/978-3-319-96247-4_19

Publications

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Comparison of statistical procedures for Gaussian graphical model selection

P. 269–279.

Grechikhin I., Kalyagin V. A.

Graphical models are used in a variety of problems to uncover hidden structures. There is an important number of different identification procedures to recover graphical model from observations. In this paper, undirected Gaussian graphical models are considered. Some Gaussian graphical model identification statistical procedures are compared using different measures, such as Type I and Type II errors, ROC AUC.

Language: English

DOI

Keywords: statistical inference Gaussian graphical model Identification procedure

In book

Computational Aspects and Applications in Large-Scale Networks. Springer Proceedings in Mathematics & Statistics

Valery A. Kalyagin, Panos M. Pardalos, Oleg Prokopyev, Irina Utkina Vol. 247. , Springer, 2018.

On convergence of diﬀerent bootstrap approximations for medians

I. E. Pile, Lobachevskii Journal of Mathematics 2025 Vol. 46 No. 12 P. 6341–6346

This study aims to investigate the convergence properties of the exact closed-form formula for the distribution of indices in a sorted sample being medians of Poisson-bootstrapped subsamples toward the binomial distribution. We find that although the two distributions do not coincide exactly, their asymptotic behaviors are of the same order, specifically O(1/√n) as the sample ...

Added: November 22, 2025

Community Detection through Likelihood Optimization: In Search of a Sound Model

Prokhorenkova Liudmila, Tikhonov A., , in: The World Wide Web ConferenceVol. WWW ’19: The Web Conference 2019.: NY: Association for Computing Machinery (ACM), 2019. P. 1498–1508.

Community detection is one of the most important problems in network analysis. Among many algorithms proposed for this task, methods based on statistical inference are of particular interest: they are mathematically sound and were shown to provide partitions of good quality. Statistical inference methods are based on fitting some random graph model (a.k.a. null model) ...

Added: April 21, 2020

Loss function, unbiasedness, and optimality of Gaussian graphical model selection

Kalyagin V. A., Koldanov A. P., Koldanov P. et al., Journal of Statistical Planning and Inference 2019 Vol. 201 P. 32–39

A Gaussian graphical model is a graphical representation of the dependence structure for a Gaussian random vector. Gaussian graphical model selection is a statistical problem that identifies the Gaussian graphical model from observations. There are several statistical approaches for Gaussian graphical model identification. Their properties, such as unbiasedeness and optimality, are not established. In this paper we study these ...

Added: February 13, 2019

Критика методологии проверки нулевой гипотезы: ограничения и возможные пути выхода. Часть II

Корнеев А. А., Rasskazova E., Кричевец А. Н. et al., Психологические исследования: электронный научный журнал 2016 Т. 9 № 47 С. 6–6

The paper continues the previous publication in the journal (Vol. 9, No. 45), which was devoted to criticism of the wide-spread approach in the statistical inferences, so called Null-hypothesis significance testing. In the second part we discuss the necessity of science social organization reformation, and describe some corresponding tendencies in the modern science: (1) equal chance for both ...

Added: October 22, 2016