-
Minmax regret 1-sink location problems on dynamic flow path networks with parametric weights J. Comb. Optim. (IF 0.9) Pub Date : 2024-08-26 Tetsuya Fujie, Yuya Higashikawa, Naoki Katoh, Junichi Teruyama, Yuki Tokuni
-
Co-factor analysis of citation networks J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-08-27 Alex Hayes, Karl Rohe
One compelling use of citation networks is to characterize papers by their relationships to the surrounding literature. We propose a method to characterize papers by embedding them into two distinc...
-
Efficient estimation of the modified Gromov–Hausdorff distance between unweighted graphs J. Comb. Optim. (IF 0.9) Pub Date : 2024-08-23 Vladyslav Oles, Nathan Lemons, Alexander Panchenko
-
Meta-heuristic-based hybrid deep learning model for vulnerability detection and prevention in software system J. Comb. Optim. (IF 0.9) Pub Date : 2024-08-20 Lijin Shaji, R. Suji Pramila
-
Fast Bayesian Inference for Spatial Mean-Parameterized Conway–Maxwell–Poisson Models J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-08-21 Bokgyeong Kang, John Hughes, Murali Haran
Count data with complex features arise in many disciplines, including ecology, agriculture, criminology, medicine, and public health. Zero inflation, spatial dependence, and non-equidispersion are ...
-
The prize-collecting single machine scheduling with bounds and penalties J. Comb. Optim. (IF 0.9) Pub Date : 2024-08-16 Guojun Hu, Pengxiang Pan, Suding Liu, Ping Yang, Runtao Xie
-
Degrees of Freedom: Search Cost and Self-consistency J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-08-08 Lijun Wang, Hongyu Zhao, Xiaodan Fan
Model degrees of freedom ( df ) is a fundamental concept in statistics because it quantifies the flexibility of a fitting procedure and is indispensable in model selection. To investigate the gap b...
-
Beyond time-homogeneity for continuous-time multistate Markov models J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-08-08 Emmett B. Kendall, Jonathan P. Williams, Gudmund H. Hermansen, Frederic Bois, Vo Hong Thanh
Multistate Markov models are a canonical parametric approach for data modeling of observed or latent stochastic processes supported on a finite state space. Continuous-time Markov processes describ...
-
Scalable Estimation for Structured Additive Distributional Regression J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-08-08 Nikolaus Umlauf, Johannes Seiler, Mattias Wetscher, Thorsten Simon, Stefan Lang, Nadja Klein
Obtaining probabilistic models is of high relevance in many recent applications. However, estimation of such distributional models with very large datasets remains a difficult task. In particular, ...
-
Using rejection sampling probability of acceptance as a measure of independence J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-08-06 Markku Kuismin
This paper proposes a new association statistic for determining whether random variables are statistically independent. The proposed association statistic can also be used to examine the strength o...
-
Augmentation Samplers for Multinomial Probit Bayesian Additive Regression Trees J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-08-05 Yizhen Xu, Joseph Hogan, Michael Daniels, Rami Kantor, Ann Mwangi
The multinomial probit (MNP) (Imai and van Dyk, 2005) framework is based on a multivariate Gaussian latent structure, allowing for natural extensions to multilevel modeling. Unlike multinomial logi...
-
Blocked Gibbs sampler for hierarchical Dirichlet processes J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-08-05 Snigdha Das, Yabo Niu, Yang Ni, Bani K. Mallick, Debdeep Pati
Posterior computation in hierarchical Dirichlet process (HDP) mixture models is an active area of research in nonparametric Bayes inference of grouped data. Existing literature almost exclusively f...
-
Models for two-dimensional bin packing problems with customer order spread J. Comb. Optim. (IF 0.9) Pub Date : 2024-08-07 Mateus Martin, Horacio Hideki Yanasse, Maristela O. Santos, Reinaldo Morabito
-
Approximating the probabilistic p-Center problem under pressure J. Comb. Optim. (IF 0.9) Pub Date : 2024-08-07 Marc Demange, Marcel A. Haddad, Cécile Murat
-
On the complexity of minimum maximal acyclic matchings J. Comb. Optim. (IF 0.9) Pub Date : 2024-08-07 Juhi Chaudhary, Sounaka Mishra, B. S. Panda
-
Polynomial algorithms for sparse spanners on subcubic graphs J. Comb. Optim. (IF 0.9) Pub Date : 2024-08-07 R. Gómez, F. K. Miyazawa, Y. Wakababayashi
-
r-Euler-Mahonian statistics on permutations J. Comb. Theory A (IF 0.9) Pub Date : 2024-08-06 Shao-Hua Liu
Let and denote the permutation statistics -descent number and -excedance number, respectively. We prove that the pairs of permutation statistics and are equidistributed, where denotes the -major index defined by Don Rawlings and denotes the -Denert's statistic defined by Guo-Niu Han. When , this result reduces to the equidistribution of and , which was conjectured by Denert in 1990 and proved that
-
Customer churn prediction using a novel meta-classifier: an investigation on transaction, Telecommunication and customer churn datasets J. Comb. Optim. (IF 0.9) Pub Date : 2024-08-03 Fatemeh Ehsani, Monireh Hosseini
-
The q-Onsager algebra and the quantum torus J. Comb. Theory A (IF 0.9) Pub Date : 2024-08-02 Owen Goff
The -Onsager algebra, denoted , is defined by two generators and two relations called the -Dolan-Grady relations. Recently, Terwilliger introduced some elements of , said to be alternating. These elements are denoted
-
An infinite family of hyperovals of Q+(5,q), q even J. Comb. Theory A (IF 0.9) Pub Date : 2024-08-01 Bart De Bruyn
We construct an infinite family of hyperovals on the Klein quadric , even. The construction makes use of ovoids of the symplectic generalized quadrangle that is associated with an elliptic quadric which arises as solid intersection with . We also solve the isomorphism problem: we determine necessary and sufficient conditions for two hyperovals arising from the construction to be isomorphic.
-
First zagreb spectral radius of unicyclic graphs and trees J. Comb. Optim. (IF 0.9) Pub Date : 2024-07-30 Parikshit Das, Kinkar Chandra Das, Sourav Mondal, Anita Pal
-
Algorithms for a two-machine no-wait flow shop scheduling problem with two competing agents J. Comb. Optim. (IF 0.9) Pub Date : 2024-07-30 Qi-Xia Yang, Long-Cheng Liu, Min Huang, Tian-Run Wang
-
An improved upper bound for the online graph exploration problem on unicyclic graphs J. Comb. Optim. (IF 0.9) Pub Date : 2024-07-29 Koji M. Kobayashi, Ying Li
-
Risk-adjusted exponential gradient strategies for online portfolio selection J. Comb. Optim. (IF 0.9) Pub Date : 2024-07-28 Jin’an He, Fangping Peng, Xiuying Xie
-
Maximizing stochastic set function under a matroid constraint from decomposition J. Comb. Optim. (IF 0.9) Pub Date : 2024-07-28 Shengminjie Chen, Donglei Du, Wenguo Yang, Suixiang Gao
-
A proof of the Etzion-Silberstein conjecture for monotone and MDS-constructible Ferrers diagrams J. Comb. Theory A (IF 0.9) Pub Date : 2024-07-24 Alessandro Neri, Mima Stanojkovski
Ferrers diagram rank-metric codes were introduced by Etzion and Silberstein in 2009. In their work, they proposed a conjecture on the largest dimension of a space of matrices over a finite field whose nonzero elements are supported on a given Ferrers diagram and all have rank lower bounded by a fixed positive integer . Since stated, the Etzion-Silberstein conjecture has been verified in a number of
-
Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-07-15 Jiajun Liang, Qian Zhang, Wei Deng, Qifan Song, Guang Lin
This work introduces a novel and efficient Bayesian federated learning algorithm, namely, the Federated Averaging stochastic Hamiltonian Monte Carlo (FA-HMC), for parameter estimation and uncertain...
-
Using early rejection Markov chain Monte Carlo and Gaussian processes to accelerate ABC methods J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-07-15 Xuefei Cao, Shijia Wang, Yongdao Zhou
Approximate Bayesian computation (ABC) is a class of Bayesian inference algorithms that targets problems with intractable or unavailable likelihood functions. It uses synthetic data drawn from the ...
-
New string attractor-based complexities for infinite words J. Comb. Theory A (IF 0.9) Pub Date : 2024-07-18 Julien Cassaigne, France Gheeraert, Antonio Restivo, Giuseppe Romana, Marinella Sciortino, Manon Stipulanti
A is a set of positions in a word such that each distinct factor has an occurrence crossing a position from the set. This definition comes from the data compression field, where the size of a smallest string attractor represents a lower bound for the output size of a large family of string compressors exploiting repetitions in words, including BWT-based and LZ-based compressors. For finite words, the
-
Embedding and the first Laplace eigenvalue of a finite graph J. Comb. Optim. (IF 0.9) Pub Date : 2024-07-16 Takumi Gomyou, Toshimasa Kobayashi, Takefumi Kondo, Shin Nayatani
Göring–Helmberg–Wappler introduced optimization problems regarding embeddings of a graph into a Euclidean space and the first nonzero eigenvalue of the Laplacian of a graph, which are dual to each other in the framework of semidefinite programming. In this paper, we introduce a new graph-embedding optimization problem, and discuss its relation to Göring–Helmberg–Wappler’s problems. We also identify
-
Computational methods for fast Bayesian model assessment via calibrated posterior p-values J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-07-11 Sally Paganin, Perry de Valpine
Posterior predictive p-values (ppps) have become popular tools for Bayesian model assessment, being general-purpose and easy to use. However, interpretation can be difficult because their distribut...
-
Stochastic Block Smooth Graphon Model J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-07-08 Benjamin Sischka, Göran Kauermann
In this paper, we propose combining the stochastic blockmodel and the smooth graphon model, two of the most prominent modeling approaches in statistical network analysis. Stochastic blockmodels are...
-
A Tidy Framework and Infrastructure to Systematically Assemble Spatio-temporal Indexes from Multivariate Data J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-07-08 H. Sherry Zhang, Dianne Cook, Ursula Laa, Nicolas Langrené, Patricia Menéndez
Indexes are useful for summarizing multivariate information into single metrics for monitoring, communicating, and decision-making. While most work has focused on defining new indexes for specific ...
-
Continuous-time multivariate analysis J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-07-08 Biplab Paul, Philip T. Reiss, Erjia Cui, Noemi Foà
The starting point for much of multivariate analysis (MVA) is an n × p data matrix whose n rows represent observations and whose p columns represent variables. Some multivariate data sets, however,...
-
Fast Computer Model Calibration using Annealed and Transformed Variational Inference J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-07-08 Dongkyu Derek Cho, Won Chang, Jaewoo Park
Computer models play a crucial role in numerous scientific and engineering domains. To ensure the accuracy of simulations, it is essential to properly calibrate the input parameters of these models...
-
Cluster braid groups of Coxeter-Dynkin diagrams J. Comb. Theory A (IF 0.9) Pub Date : 2024-07-10 Zhe Han, Ping He, Yu Qiu
Cluster exchange groupoids are introduced by King-Qiu as an enhancement of cluster exchange graphs to study stability conditions and quadratic differentials. In this paper, we introduce the cluster exchange groupoid for any finite Coxeter-Dynkin diagram Δ and show that its fundamental group is isomorphic to the corresponding braid group associated with Δ.
-
Functional Time Series Analysis and Visualization Based on Records J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-07-08 Israel Martínez-Hernández, Marc G. Genton
In many phenomena, data are collected on a large scale and at different frequencies. In this context, functional data analysis (FDA) has become an important statistical methodology for analyzing an...
-
Restricted bargraphs and unimodal compositions J. Comb. Theory A (IF 0.9) Pub Date : 2024-07-05 Rigoberto Flórez, José L. Ramírez, Diego Villamizar
In this paper, we present a study on , which are polygons created by connecting unit squares along their edges. Specifically, we focus on a related concept called a , which is a path on a lattice in traced along the boundaries of a column convex polyomino where the lower edge is on the -axis. To explore new variations of bargraphs, we introduce the notion of , which incorporate an additional restriction
-
A hybrid grey wolf optimizer for engineering design problems J. Comb. Optim. (IF 0.9) Pub Date : 2024-07-03 Shuilin Chen, Jianguo Zheng
-
Injective edge-coloring of claw-free subcubic graphs J. Comb. Optim. (IF 0.9) Pub Date : 2024-07-03 Qing Cui, Zhenmeng Han
-
Approximation algorithms for two clustered arc routing problems J. Comb. Optim. (IF 0.9) Pub Date : 2024-07-03 Xiaoguang Bao, Xinhao Ni
-
Finding a second Hamiltonian decomposition of a 4-regular multigraph by integer linear programming J. Comb. Optim. (IF 0.9) Pub Date : 2024-07-03 Andrei V. Nikolaev, Egor V. Klimov
-
Improved kernelization and fixed-parameter algorithms for bicluster editing J. Comb. Optim. (IF 0.9) Pub Date : 2024-07-03 Manuel Lafond
-
Positivity and tails of pentagonal number series J. Comb. Theory A (IF 0.9) Pub Date : 2024-07-04 Nian Hong Zhou
In this paper, we refine a result of Andrews and Merca on truncated pentagonal number series. Subsequently, we establish some positivity results involving Andrews–Gordon–Bressoud identities and -regular partitions. In particular, we prove several conjectures of Merca and Krattenthaler–Merca–Radu on truncated pentagonal number series.
-
Global inference and test for eigensystems of imaging data over complicated domains J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-07-01 Leheng Cai, Qirui Hu
A nonparametric approach for analyzing eigensystems of image data over a complex domain is novelly developed. The proposed estimators, which are based on bivariate splines, have both oracle efficie...
-
Bootstrapped Edge Count Tests for Nonparametric Two-Sample Inference Under Heterogeneity J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-07-01 Trambak Banerjee, Bhaswar B. Bhattacharya, Gourab Mukherjee
Nonparametric two-sample testing is a classical problem in inferential statistics. While modern two-sample tests, such as the edge count test and its variants, can handle multivariate and non-Eucli...
-
Dynamic prediction using landmark historical functional Cox regression J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-07-02 Andrew Leroux, Ciprian Crainiceanu
Dynamic prediction of survival data in the presence of time-varying covariates is an area of active research. Two common analytic approaches for this type of data are joint modeling of the longitud...
-
On the Wasserstein Median of Probability Measures J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-07-01 Kisung You, Dennis Shung, Mauro Giuffrè
The primary choice to summarize a finite collection of random objects is by using measures of central tendency, such as mean and median. In the field of optimal transport, the Wasserstein barycente...
-
Bayesian L12 regression J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-07-01 Xiongwen Ke, Yanan Fan
It is well known that Bridge regression Knight et al. (2000) enjoys superior theoretical properties when compared to traditional LASSO. However, the current latent variable representation of its Ba...
-
On ABC spectral radius of uniform hypergraphs J. Comb. Optim. (IF 0.9) Pub Date : 2024-06-28 Hongying Lin, Bo Zhou
Let G be a k-uniform hypergraph with vertex set [n] and edge set E(G), where \(k\ge 2\). For \(i\in [n]\), \(d_i\) denotes the degree of vertex i in G. The ABC spectral radius of G is $$\begin{aligned} \max \left\{ k\sum _{e\in E(G)}\root k \of {\dfrac{\sum _{i\in e}d_{i} -k}{\prod _{i\in e}d_{i}}}\prod _{i\in e}x_i: \textbf{x}\in {\mathbb {R}}_+^n, \sum _{i=1}^nx_i^k=1\right\} . \end{aligned}$$ We
-
A neural network accelerated optimization method for FPGA J. Comb. Optim. (IF 0.9) Pub Date : 2024-06-25 Zhengwei Hu, Sijie Zhu, Leilei Wang, Wangbin Cao, Zhiyuan Xie
-
Testing Model Specification in Approximate Bayesian Computation Using Asymptotic Properties J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-06-24 Andrés Ramírez-Hassan, David T. Frazier
We present a novel procedure to diagnose model misspecification in situations where inference is performed using approximate Bayesian computation (ABC). Unlike previous procedures, our proposal is ...
-
On the difference of the enhanced power graph and the power graph of a finite group J. Comb. Theory A (IF 0.9) Pub Date : 2024-06-21 Sucharita Biswas, Peter J. Cameron, Angsuman Das, Hiranya Kishore Dey
The difference graph of a finite group is the difference of the enhanced power graph of and the power graph of , where all isolated vertices are removed. In this paper we study the connectedness and perfectness of with respect to various properties of the underlying group . We also find several connections between the difference graph of and the Gruenberg-Kegel graph of . We also examine the operation
-
Flag-transitive automorphism groups of 2-designs with λ ≥ (r,λ)2 are not product type J. Comb. Theory A (IF 0.9) Pub Date : 2024-06-19 Huiling Li, Zhilin Zhang, Shenglin Zhou
In this note we show that a flag-transitive automorphism group of a non-trivial 2- design with is not of product action type. In conclusion, is a primitive group of affine or almost simple type.
-
A distribution-free method for change point detection in non-sparse high dimensional data J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-06-12 Reza Drikvandi, Reza Modarres
We propose a distribution-free distance-based method for high dimensional change points that can address challenging situations when the sample size is very small compared to the dimension as in th...
-
New efficient algorithms for the two-machine no-wait chain-reentrant shop problem J. Comb. Optim. (IF 0.9) Pub Date : 2024-06-16 Nazim Sami, Karim Amrouche, Mourad Boudhar
-
Principal variables analysis for non-Gaussian data J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-06-13 Dylan Clark-Boucher, Jeffrey W. Miller
Principal variables analysis (PVA) is a technique for selecting a subset of variables that capture as much of the information in a dataset as possible. Existing approaches for PVA are based on the ...
-
Interval-censored linear quantile regression J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-06-11 Taehwa Choi, Seohyeon Park, Hunyong Cho, Sangbum Choi
Censored quantile regression has emerged as a prominent alternative to classical Cox’s proportional hazards model or accelerated failure time model in both theoretical and applied statistics. While...
-
Generating Independent Replicates Directly from the Posterior Distribution for a Class of Spatial Hierarchical Models J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-06-11 Jonathan R. Bradley, Madelyn Clinch
Markov chain Monte Carlo (MCMC) allows one to generate dependent replicates from a posterior distribution for effectively any Bayesian hierarchical model. However, MCMC can produce a significant co...
-
Ultra-efficient MCMC for Bayesian longitudinal functional data analysis J. Comput. Graph. Stat. (IF 1.4) Pub Date : 2024-06-07 Thomas Y. Sun, Daniel R. Kowal
Functional mixed models are widely useful for regression analysis with dependent functional data, including longitudinal functional data with scalar predictors. However, existing algorithms for Bay...