Dr. Anatoly Yambartsev

Co-authors

Following

Followers

Research Focus & Metrics

Explore full metrics on Scilit

Academic Career Length

24Years (2003-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications

31 July 2012 in Journal of Statistical Physics

Journal Article

Phase Transition for the Ising Model on the Critical Lorentzian Triangulation

Maxim Krikun

Anatoly Yambartsev

13 October 2016 in Biology Direct

Journal Article

Unexpected links reflect the noise in networks

Gene covariation networks are commonly used to study biological processes. The inference of gene covariation networks from observational data can be challenging, especially considering the large number of players involved and the small number of biological replicates available for analysis. We propose a new statistical method for estimating the number of erroneous edges in reconstructed networks that strongly enhances commonly used inference approaches. This method is based on a special relationship between sign of correlation (positive/negative) and directionality (up/down) of gene regulation, and allows for the identification and removal of approximately half of all erroneous edges. Using the mathematical model of Bayesian networks and positive correlation inequalities we establish a mathematical foundation for our method. Analyzing existing biological datasets, we find a strong correlation between the results of our method and false discovery rate (FDR). Furthermore, simulation analysis demonstrates that our method provides a more accurate estimate of network error than FDR. Thus, our study provides a new robust approach for improving reconstruction of covariation networks. This article was reviewed by Eugene Koonin, Sergei Maslov, Daniel Yasumasa Takahashi.

1 June 2019 in Statistics & Probability Letters

Journal Article

Large deviations in a population dynamics with catastrophes

A. Logachov

O. Logachova

Anatoly Yambartsev

14 July 2011 in BMC Bioinformatics

Journal Article

Construct and Compare Gene Coexpression Networks with DAPfinder and DAPview

Background: DAPfinder and DAPview are novel BRB-ArrayTools plug-ins to construct gene coexpression networks and identify significant differences in pairwise gene-gene coexpression between two phenotypes. Results: Each significant difference in gene-gene association represents a Differentially Associated Pair (DAP). Our tools include several choices of filtering methods, gene-gene association metrics, statistical testing methods and multiple comparison adjustments. Network results are easily displayed in Cytoscape. Analyses of glioma experiments and microarray simulations demonstrate the utility of these tools. Conclusions: DAPfinder is a new friendly-user tool for reconstruction and comparison of biological networks.

15 August 2024 in Mathematics

Journal Article

Modifications to the Jarque–Bera Test

Vladimir Vasilyevich Glinskiy

The Jarque–Bera test is commonly used in statistics and econometrics to test the hypothesis that sample elements adhere to a normal distribution with an unknown mean and variance. This paper proposes several modifications to this test, allowing for testing hypotheses that the considered sample comes from: a normal distribution with a known mean (variance unknown); a normal distribution with a known variance (mean unknown); a normal distribution with a known mean and variance. For given significance levels,

α = 0.05

and

α = 0.01

, we compare the power of our normality test with the most well-known and popular tests using the Monte Carlo method: Kolmogorov–Smirnov (KS), Anderson–Darling (AD), Cramér–von Mises (CVM), Lilliefors (LF), and Shapiro–Wilk (SW) tests. Under the specific distributions, 1000 datasets were generated with the sample sizes

n = 25, 50, 75, 100, 150, 200, 250, 500,

and 1000. The simulation study showed that the suggested tests often have the best power properties. Our study also has a methodological nature, providing detailed proofs accessible to undergraduate students in statistics and probability, unlike the works of Jarque and Bera.

21 June 2016 in Journal of Applied Probability

Journal Article

Random walks in a queueing network environment

Mark Gannon

E. Pechersky

Iouri SOUKHOV

Anatoly Yambartsev

We propose a class of models of random walks in a random environment where an exact solution can be given for a stationary distribution. The environment is cast in terms of a Jackson/Gordon–Newell network although alternative interpretations are possible. The main tool is the detailed balance equations. The difference compared to earlier works is that the position of the random walk influences the transition intensities of the network environment and vice versa, creating strong correlations. The form of the stationary distribution is closely related to the well-known product formula.

About Me

Dr. Ludmila Serga received her PhD in Economics from the Moscow Institute of Economics and Statistics in 1991. She is currently the Head of the Department of Business Analytics, Accounting and Statistics at the Siberian Institute of Management, branch of the Russian Presidential Academy, Novosibirsk, Russia. Her research interests include the study in the field of digital economy and data analytics, and the study of regulated differentiation of socio-economic systems. Prof. Dr. Vladimir Glinsky received his Doctor in Economics degree from the Saint Petersburg State University of Economics in 2009. He is currently a Professor in the Department of Statistics at the Novosibirsk State University of Economics and Management, Novosibirsk, Russia. His research interests include statistical methods of data typology, business statistics, and the study of regulated differentiation and sustainable development of regional socio-economic systems. Dr. Anatoliy Yambartsev received his PhD degree in mathematics and applied mathematics from Moscow State University, Russia, in 1999. He is currently an associate professor in the Department of Statistics, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, SP, Brazil. He completed a two-year postdoc at the University of São Paulo, Brazil, and in 2005, became an assistant professor at the same university. His research interests include Markov processes, statistical mechanics, computational statistics, and biostatistics.

Dr. Anatoly Yambartsev

Dr. Anatoly Yambartsev

Research Focus & Metrics

Academic Career Length

Publication Type Breakdown

Representative Publications

About Me