Article
Physics, Multidisciplinary
Shubhadeep Chakraborty, Ali Shojaie
Summary: The PC and FCI algorithms are popular methods for learning DAGs. However, these algorithms rely on the assumption of joint Gaussianity, which may not hold in many applications. In order to address this limitation, we propose nonparametric variants of the PC-stable and FCI-stable algorithms that use conditional distance covariance (CdCov) to test for conditional independence. Numerical studies show that our proposed algorithms perform well for both Gaussian and non-Gaussian graphical models.
Article
Statistics & Probability
Chien-Ming Chi, Patrick Vossler, Yingying Fan, Jinchi Lv
Summary: This paper investigates the consistency of the random forests algorithm in high-dimensional nonparametric regression problems and derives the consistency rates through a bias-variance decomposition analysis. The study finds that random forests can adapt to high dimensionality and allow for discontinuous regression functions. Moreover, the bias analysis characterizes how the bias of random forests depends on the sample size, tree height, and column subsampling parameter.
ANNALS OF STATISTICS
(2022)
Article
Computer Science, Artificial Intelligence
Yan Jin, Bowen Xiong, Kun He, Yangming Zhou, Yi Zhou
Summary: Maximal Clique Enumeration (MCE) is a fundamental and challenging problem in graph theory and various network applications. A new efficient algorithm FACEN based on the Bron-Kerbosch framework is proposed, which shows competitive performance with leading MCE methods and can handle very large graphs efficiently.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Mathematics
Yangyang Zhou, Dongyang Zhao, Mingyuan Ma, Jin Xu
Summary: This paper proves the applicability of dumbbell maximal planar graphs to the Total Coloring Conjecture, categorizes them into three types, and proposes an algorithm for computing their coloring properties.
Article
Biochemical Research Methods
David Schaller, Manuela Geiss, Marc Hellmuth, Peter F. F. Stadler
Summary: We propose a near-cubic algorithm to determine if Best match graphs (BMG) can be explained by a fully resolved gene tree and to construct such a tree. We prove that all binary trees are refinements of the unique binary-refinable tree (BRT) which is a significant refinement of the least resolved tree of a BMG. Additionally, we demonstrate the NP-completeness of editing an arbitrary vertex-colored graph to a binary-explainable BMG and provide an integer linear program formulation for this task.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Statistics & Probability
Jian Huang, Yuling Jiao, Bangti Jin, Jin Liu, Xiliang Lu, Can Yang
Summary: In this paper, a novel algorithm of primal-dual active set type is developed for recovering a sparse signal based on penalized least squares formulations. The algorithm shows superior computational efficiency and recovery accuracy in global convergence to the regression target under certain conditions.
STATISTICAL SCIENCE
(2021)
Article
Chemistry, Physical
Nirmala Parisutham, Nadarajan Rethnasamy
Summary: The physical and biological properties of a chemical molecule are related to its structure, with compounds sharing similar structure often having similar properties. Finding structural similarities between chemical structures helps identify common behaviors of molecules.
JOURNAL OF MOLECULAR STRUCTURE
(2021)
Article
Computer Science, Information Systems
Lianpeng Qiao, Rong-Hua Li, Zhiwei Zhang, Ye Yuan, Guoren Wang, Hongchao Qin
Summary: In this article, the problem of mining cohesive subgraphs from an uncertain graph is studied. A new (alpha,gamma)-quasi-clique model is introduced to model the cohesive subgraphs in an uncertain graph, and a basic enumeration algorithm and an advanced enumeration algorithm are proposed to find all maximal (alpha,gamma)-quasi-cliques. Several optimization techniques are also proposed to improve efficiency. Experimental results on five real-world datasets demonstrate that the proposed solutions are almost three times faster than the baseline approach.
IEEE TRANSACTIONS ON BIG DATA
(2023)
Article
Computer Science, Theory & Methods
Yangyang Zhou, Dongyang Zhao, Mingyuan Ma, Jin Xu
Summary: This paper proves that the Total Coloring Conjecture holds for recursive maximal planar graphs, especially for a main class of recursive maximal planar graphs called (2,2)-recursive maximal planar graphs. Linear time algorithms for total coloring of recursive maximal planar graphs and (2,2)-recursive maximal planar graphs are also provided.
THEORETICAL COMPUTER SCIENCE
(2022)
Article
Mathematics, Applied
Shao-Liang Chen, Rong-Xia Hao, Xiao-Wen Qin
Summary: This paper introduces the concepts of connected dominating sets and their connected domination numbers in graphs, and proposes an algorithm for finding connected dominating sets in maximal outerplanar graphs. An upper bound for the connected domination number of maximal outerplanar graphs is obtained through this algorithm. Additionally, the advantages of the results are evaluated through simulations.
DISCRETE APPLIED MATHEMATICS
(2022)
Article
Mathematics
Vicente Jara-Vera, Carmen Sanchez-Avila
Summary: In this study, we analyze cycles or planar polygonal graphs G that are maximal in their inner edges and provide a series of coloring results, such as chi(G)= 3 or P(G, 3) = 6, as well as construction algorithms. Some aspects of these graphs with various applications in path modeling, data flow design, computer networks, or best resource allocation are discussed.
INTERNATIONAL JOURNAL OF MATHEMATICS AND COMPUTER SCIENCE
(2021)
Proceedings Paper
Computer Science, Theory & Methods
Juhi Chaudhary, Sounaka Mishra, B. S. Panda
Summary: This paper investigates the problem of MIN-MAX-ACY-Matching and presents a linear-time algorithm to solve it in proper interval graphs.
ALGORITHMS AND DISCRETE APPLIED MATHEMATICS, CALDAM 2023
(2023)
Article
Statistics & Probability
Preetam Nandy, Alain Hauser, Marloes H. Maathuis
ANNALS OF STATISTICS
(2018)
Article
Statistics & Probability
Robin J. Evans, Thomas S. Richardson
Article
Immunology
Zofia Baranczuk, Janne Estill, Sara Blough, Sonja Meier, Aziza Merzouki, Marloes H. Maathuis, Olivia Keiser
JOURNAL OF THE INTERNATIONAL AIDS SOCIETY
(2019)
Article
Biology
F. Richard Guo, Thomas S. Richardson
Article
Computer Science, Information Systems
F. Richard Guo, Thomas S. Richardson
Summary: The study investigates the relative entropy of the empirical probability vector with respect to the true probability vector in multinomial sampling, generalizing a recent result and showing the moment generating function of the statistic is bounded by a polynomial. By characterizing the family of polynomials and developing Chernoff-type tail bounds, including a closed-form version, the research demonstrates dominance over classic methods and competitiveness with the state of the art, as shown in an application to estimating the proportion of unseen butterflies.
IEEE TRANSACTIONS ON INFORMATION THEORY
(2021)
Article
Biology
Linbo Wang, Yuexia Zhang, Thomas S. Richardson, James M. Robins
Summary: Instrumental variables are commonly used to address unmeasured confounding in observational studies and imperfect randomized controlled trials. This paper focuses on estimating the local average treatment effect under the binary instrumental variable model, highlighting the challenges of causal estimation with a binary outcome and proposing novel modelling and estimation procedures for improvement.
Article
Infectious Diseases
K. Mettler, Jewel Park, Orhun Ozbek, Linus K. Mettler, Po-Han Ho, Hye Chang Rhim, Marloes H. Maathuis
Summary: This study suggests using the diagnostic serial interval as a new indicator for measuring the effectiveness of contact tracing in controlling the epidemic. Results show that a shorter diagnostic serial interval can reduce the peak of the epidemic and the proportion of infected individuals, leaving more of the population susceptible at the end of the epidemic.
INTERNATIONAL JOURNAL OF INFECTIOUS DISEASES
(2021)
Editorial Material
Biology
F. Richard Guo, Thomas S. Richardson, James M. Robins
Article
Biology
J. Yin, S. Markes, T. S. Richardson, L. Wang
Summary: Generalized linear models, such as logistic regression, are widely used for modeling the association between a treatment and a binary outcome. However, the coefficients of logistic regression correspond to log odds ratios, whereas subject-matter scientists are often interested in relative risks. This paper proposes a novel binomial regression model that directly models the relative risk, addressing the limitations of previous models. The proposed methods are demonstrated to have desirable performance through Monte Carlo simulations and an analysis of survival rates for passengers on the Titanic.
Article
Oncology
Stefanie Hiltbrunner, Meta-Lina Spohn, Ramona Wechsler, Dilara Akhoundova, Lorenz Bankel, Sabrina Kasser, Svenja Bihr, Christian Britschgi, Marloes H. Maathuis, Alessandra Curioni-Fontecedro
Summary: This study identified high basophil counts as a potential biomarker for predicting treatment response in NSCLC patients receiving ICIs.
Article
Biology
Linbo Wang, Xiang Meng, Thomas S. Richardson, James M. Robins
Summary: This paper presents a method to solve the variation dependence problem of binary multiplicative SNMM by reparameterizing the noncausal nuisance parameters. This method allows for coherent modeling of heterogeneous effects in longitudinal studies with binary outcomes and provides a key building block for flexible doubly robust estimation of the causal parameters.
Editorial Material
Statistics & Probability
Thomas S. Richardson
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
(2022)
Correction
Statistics & Probability
S. A. Swanson, M. A. Hernan, M. Miller, J. M. Robins, T. S. Richardson
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Daniel Malinsky, Ilya Shpitser, Thomas Richardson
22ND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 89
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Ilya Shpitser, Robin J. Evans, Thomas S. Richardson
UNCERTAINTY IN ARTIFICIAL INTELLIGENCE
(2018)