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The Best Time to Play the Lottery Am. Stat. (IF 1.8) Pub Date : 2024-05-07 Christopher M. Rump
The best time to play the lottery is when the jackpot has rolled over several times and grown large, but not so large that you must share the prize if you win. We examine maximizing the expected va...
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Tests for large-dimensional shape matrices via Tyler’s M estimators J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-05-03 Runze Li, Weiming Li, Qinwen Wang
Tyler’s M estimator, as a robust alternative to the sample covariance matrix, has been widely applied in robust statistics. However, classical theory on Tyler’s M estimator is mainly developed in t...
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An efficient solver for large-scale onshore wind farm siting including cable routing Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-05-03 Jaap Pedersen, Jann Michael Weinand, Chloi Syranidou, Daniel Rehfeldt
Existing planning approaches for onshore wind farm siting and grid integration often do not meet minimum cost solutions or social and environmental considerations. In this paper, we develop an exact approach for the integrated layout and cable routing problem of onshore wind farm planning using the Quota Steiner tree problem. Applying a novel transformation on a known directed cut formulation, reduction
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Analyzing Matched 2 × 2 Tables from all Corners Am. Stat. (IF 1.8) Pub Date : 2024-05-02 Marc Aerts, Geert Molenberghs
Squared 2 × 2 tables with binary data from matched pairs are typically analysed using Cochran-Mantel-Haenszel methodology, conditional logistic regression, or random intercepts logistic regression....
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A Causal Framework for the Comparability of Latent Variables Struct. Equ. Model. (IF 6.0) Pub Date : 2024-04-30 Philipp Sterner, Florian Pargent, Dominik Deffner, David Goretzko
Measurement invariance (MI) describes the equivalence of measurement models of a construct across groups or time. When comparing latent means, MI is often stated as a prerequisite of meaningful gro...
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Augmented Bifactor Models and Bifactor-(S-1) Models are Identical. A Comment on Zhang, Luo, Zhang, Sun & Zhang (2023) Struct. Equ. Model. (IF 6.0) Pub Date : 2024-04-30 Tobias Koch, Michael Eid
In a recent study, Zhang et al. (2023) proposed the augmented oblique bifactor model as a new methodological contribution, in which an oblique bifactor model is augmented by one or more indicator(s...
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Benders decomposition for the discrete ordered median problem Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-29 Ivana Ljubić, Miguel A. Pozo, Justo Puerto, Alberto Torrejón
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Railway crew planning with fairness over time Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-29 B.T.C. van Rossum, T. Dollevoet, D. Huisman
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Determinism versus uncertainty: Examining the worst-case expected performance of data-driven policies Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-27 Xuecheng Tian, Shuaian Wang, Gilbert Laporte, Ying Yang
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Large-scale robust regression with truncated loss via majorization-minimization algorithm Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-27 Ling-Wei Huang, Yuan-Hai Shao, Xiao-Jing Lv, Chun-Na Li
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Repositioning to sink: The pricing and quality decisions for product line considering the sinking market Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-27 Yusheng Wang, Yongjian Li, Shuangshuang Xu
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Optimal energy collection with rotational movement constraints in concentrated solar power plants Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-27 José-Miguel Díaz-Báñez, José-Manuel Higes-López, Miguel-Angel Pérez-Cutiño, Juan Valverde
In concentrated solar power (CSP) plants based on parabolic trough collectors (PTC), the sun is tracked at discrete time intervals, with each interval representing a movement of the collector system. The act of moving heavy mechanical structures can lead to the development of cracks, bending, and/or displacement of components from their optimal optical positions. This, in turn, diminishes the overall
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Controlled Discovery and Localization of Signals via Bayesian Linear Programming J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-26 Asher Spector, Lucas Janson
Scientists often must simultaneously localize and discover signals. For instance, in genetic fine-mapping, high correlations between nearby genetic variants make it hard to identify the exact locat...
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Telling Stories with Data: With Applications in R Am. Stat. (IF 1.8) Pub Date : 2024-04-23 Piotr Fryzlewicz
Published in The American Statistician (Ahead of Print, 2024)
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The Role of the Bayes Factor in the Evaluation of Evidence Annu. Rev. Stat. Appl. (IF 7.9) Pub Date : 2024-04-24 Colin Aitken, Franco Taroni, Silvia Bozza
The use of the Bayes factor as a metric for the assessment of the probative value of forensic scientific evidence is largely supported by recommended standards in different disciplines. The application of Bayesian networks enables the consideration of problems of increasing complexity. The lack of a widespread consensus concerning key aspects of evidence evaluation and interpretation, such as the adequacy
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Convergence Diagnostics for Entity Resolution Annu. Rev. Stat. Appl. (IF 7.9) Pub Date : 2024-04-24 Serge Aleshin-Guendel, Rebecca C. Steorts
Entity resolution is the process of merging and removing duplicate records from multiple data sources, often in the absence of unique identifiers. Bayesian models for entity resolution allow one to include a priori information, quantify uncertainty in important applications, and directly estimate a partition of the records. Markov chain Monte Carlo (MCMC) sampling is the primary computational method
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Explainable Analytics for Operational Research Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-24 Koen W. De Bock, Kristof Coussement, Arno De Caigny
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Rank-1 transition uncertainties in constrained Markov decision processes Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-24 V Varagapriya, Vikas Vikram Singh, Abdel Lisser
We consider an infinite-horizon discounted constrained Markov decision process (CMDP) with uncertain transition probabilities. We assume that the uncertainty in transition probabilities has a rank-1 matrix structure and the underlying uncertain parameters belong to a polytope. We formulate the uncertain CMDP problem using a robust optimization framework. To derive reformulation of the robust CMDP problem
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Electric vehicle supply equipment location and capacity allocation for fixed-route networks Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-23 Amir Davatgari, Taner Cokyasar, Anirudh Subramanyam, Jeffrey Larson, Abolfazl (Kouros) Mohammadian
Electric vehicle (EV) supply equipment location and allocation (EVSELCA) problems for freight vehicles are becoming more important because of the trending electrification shift. Some previous works address EV charger location and vehicle routing problems simultaneously by generating vehicle routes from scratch. Although such routes can be efficient, introducing new routes may violate practical constraints
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A robust optimization approach for a two-player force-design game Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-23 Jeffrey Christiansen, Andreas T. Ernst, Janosch Rieger
We present a new approach to force design that relies on robust decision making with a min–max objective, rather than assumptions about the goals and strategy of an opponent. This idea is explored mathematically in the framework of a round-based two-player Stackelberg game representing an arms race, which features the acquisition of assets by both players and an evaluation of the defensive capability
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Robust two-stage optimization consensus models with uncertain costs Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-22 Huanhuan Li, Ying Ji, Jieyu Ding, Shaojian Qu, Huijie Zhang, Yuanming Li, Yubing Liu
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Testing the number of common factors by bootstrapped sample covariance matrix in high-dimensional factor models J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-22 Long Yu, Peng Zhao, Wang Zhou
This paper studies the impact of bootstrap procedure on the eigenvalue distributions of the sample covariance matrix under a high-dimensional factor structure. We provide asymptotic distributions f...
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Inventory reallocation in a fashion retail network: A matheuristic approach Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-21 Paolo Brandimarte, Giuseppe Craparotta, Elena Marocco
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Constructing order-2 information granules of linguistic expressions with the aid of the principle of justifiable granularity Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-21 Ting Huang, Witold Pedrycz, Qiang Zhang, Xiaoan Tang, Shanlin Yang
To capture collective opinions/evaluations in a collection of individual linguistic expressions, this study proposes an approach to construct order-2 information granules by extending the numerical data-based principles of justifiable granularity to a linguistic data-based one. First, the two key criteria of the principle of justifiable granularity, namely coverage and specificity, are formally defined
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A survey on the Traveling Salesman Problem and its variants in a warehousing context Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-21 Stefan Bock, Stefan Bomsdorf, Nils Boysen, Michael Schneider
With the advent of e-commerce and its fast-delivery expectations, efficiently routing pickers in warehouses and distribution centers has received renewed interest. The processes and the resulting routing problems in this environment are diverse. For instance, not only human pickers have to be routed but also autonomous picking robots or mobile robots that accompany human pickers. Traditional picker
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On cone-based decompositions of proper Pareto-optimality in multi-objective optimization Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-20 Marlon Braun, Pradyumn Shukla
In recent years, research focus in multi-objective optimization has shifted from approximating the Pareto optimal front in its entirety to identifying solutions that are well-balanced among their objectives. Proper Pareto optimality is an established concept for eliminating Pareto optimal solutions that exhibit unbounded tradeoffs. Imposing a strict tradeoff bound in a classical definition of proper
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Ranking voting systems and surrogate weights: Explicit formulas for centroid weights Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-20 Bonifacio Llamazares
One of the most important issues in the field of ranking voting systems is the choice of the weighting vector. This issue has been addressed in the literature from different approaches, and one of them has been to obtain the weighting vector as a solution to a linear programming problem. In this paper we analyze some models proposed in the literature and show that one of their main shortcomings is
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Estimating trans-ancestry genetic correlation with unbalanced data resources J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-19 Bingxin Zhao, Xiaochen Yang, Hongtu Zhu
The aim of this paper is to propose a novel method for estimating trans-ancestry genetic correlations in genome-wide association studies (GWAS) using genetically-predicted observations. These corre...
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Modeling and Learning on High-Dimensional Matrix-Variate Sequences J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-19 Xu Zhang, Catherine C. Liu, Jianhua Guo, K. C. Yuen, A. H. Welsh
We propose a new matrix factor model, named RaDFaM, which is strictly derived based on the general rank decomposition and assumes a structure of a high-dimensional vector factor model for each basi...
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A hybrid genetic search and dynamic programming-based split algorithm for the multi-trip time-dependent vehicle routing problem Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-19 Jingyi Zhao, Mark Poon, Vincent Y.F. Tan, Zhenzhen Zhang
We design a hybrid algorithm for the multi-trip time-dependent vehicle routing problem (MT-TD-VRP). One of its components is the Time-Dependent SPlit Algorithm (TD-SPA), which is a dynamic programming-based algorithm specifically designed to handle both the per vehicle and the aspects of the problem. The hybrid algorithm combines the proposed TD-SPA, designed to efficiently split a giant tour into
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Selection and Aggregation of Conformal Prediction Sets J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-17 Yachong Yang, Arun Kumar Kuchibhotla
Conformal prediction is a generic methodology for finite-sample valid distribution-free prediction. This technique has garnered a lot of attention in the literature partly because it can be applied...
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Statistical Methods in Health Disparity Research. J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-17 Susan M. Paddock
Published in Journal of the American Statistical Association (Just accepted, 2024)
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Deep Learning and Scientific Computing with R torch Am. Stat. (IF 1.8) Pub Date : 2024-04-17 Yang Ni
Published in The American Statistician (Vol. 78, No. 2, 2024)
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An Introduction to R and Python for Data Analysis: A Side-by-Side Approach. Am. Stat. (IF 1.8) Pub Date : 2024-04-17 Gabriel Wallin
Published in The American Statistician (Vol. 78, No. 2, 2024)
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On Point Estimators for Gamma and Beta Distributions Am. Stat. (IF 1.8) Pub Date : 2024-04-17 Nickos D. Papadatos
Let X1,…,Xn be a random sample from the gamma distribution with density f(x)=λαxα−1e−λx/Γ(α), x > 0, where both α>0 (the shape parameter) and λ>0 (the reciprocal scale parameter) are unknown. The m...
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Introduction to Environmental Data Science J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-16 Timothée Poisot
Published in Journal of the American Statistical Association (Just accepted, 2024)
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Introduction to Statistical Modelling and Inference Am. Stat. (IF 1.8) Pub Date : 2024-04-17 Nianpin Cheng, Beth Chance
Published in The American Statistician (Ahead of Print, 2024)
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Statistical Theory: A Concise Introduction, 2nd ed. Am. Stat. (IF 1.8) Pub Date : 2024-04-17 Juan Sosa
Published in The American Statistician (Ahead of Print, 2024)
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Review of Quantitative Methods for the Social Sciences: A Practical Introduction with Examples in R (2nd ed.) Struct. Equ. Model. (IF 6.0) Pub Date : 2024-04-16 Virginia Rosa da Silva
Published in Structural Equation Modeling: A Multidisciplinary Journal (Ahead of Print, 2024)
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Optimizing service networks to support freight rail decarbonization: Flow selection, facility location, and energy sourcing Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-16 Adrian Hernandez, Max Ng, Pablo L. Durango-Cohen, Hani S. Mahmassani
We present a framework to support decarbonization of energy intensive transportation systems offering periodic service on expansive networks (e.g., freight rail, trucking, and intercity bus services). The framework consists of two optimization problems that respectivelyaddress (i) flow selection and facility location, and (ii) energy sourcing/procurement at the service facilities to enable the selected
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Dissecting gene expression heterogeneity: generalized Pearson correlation squares and the K-lines clustering algorithm J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-15 Jingyi Jessica Li, Heather J. Zhou, Peter J. Bickel, Xin Tong
Motivated by the pressing needs for dissecting heterogeneous relationships in gene expression data, here we generalize the squared Pearson correlation to capture a mixture of linear dependences bet...
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Sharp-SSL: Selective high-dimensional axis-aligned random projections for semi-supervised learning J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-12 Tengyao Wang, Edgar Dobriban, Milana Gataric, Richard J. Samworth
We propose a new method for high-dimensional semi-supervised learning problems based on the careful aggregation of the results of a low-dimensional procedure applied to many axis-aligned random pro...
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Skill development in the field of scheduling: A structured literature review Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-12 Patricia Heuser, Peter Letmathe, Thomas Vossen
Employee skills are seen as a main driver of competitive advantages of enterprises. This article provides a state-of-the-art overview of research related to skill management in the field of operational research. For this purpose, ‘skill management’ is used as an umbrella term to integrate the different quantitative approaches found in this field. The structured literature review is based on six keywords
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Problem-based scenario generation by decomposing output distributions Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-12 Benjamin S. Narum, Jamie Fairbrother, Stein W. Wallace
Scenario generation is required for most applications of stochastic programming to evaluate the expected effect of decisions made under uncertainty. We propose a novel and effective problem-based scenario generation method for two-stage stochastic programming that is agnostic to the specific stochastic program and kind of distribution. Our contribution lies in studying how an output distribution may
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Coexchangeable process modelling for uncertainty quantification in joint climate reconstruction J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-11 Lachlan Astfalck, Daniel Williamson, Niall Gandy, Lauren Gregoire, Ruza Ivanovic
Any experiment with climate models relies on a potentially large set of spatio-temporal boundary conditions. These can represent both the initial state of the system and/or forcings driving the mod...
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Handling Measurement Error and Omitted Confounders Considering Informativeness of the Confounding Effect under Mediation Modeling Struct. Equ. Model. (IF 6.0) Pub Date : 2024-04-10 Qian Zhang, Qi Wang
In the article, we focused on the issues of measurement error and omitted confounders while conducting mediation analysis under experimental studies. Depending on informativeness of the confounders...
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Forecasting and planning for a critical infrastructure sector during a pandemic: Empirical evidence from a food supply chain Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-10 Tariq Aljuneidi, Sushil Punia, Aida Jebali, Konstantinos Nikolopoulos
The meat supply chain (MSC) – a key constituent of the ‘Food & Agriculture’ CISA critical infrastructure sector, was among the most impacted by the COVID-19 pandemic. The witnessed successive demand and supply shocks uncovered the fragility of the MSC and revealed that more attention should be given by researchers and practitioners to ensure effective planning of such a critical infrastructure sector
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Individualized second stage corrections in data envelopment analysis Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-10 Mohsen Afsharian, Sara Kamali, Heinz Ahn, Peter Bogetoft
In the context of two-stage data envelopment analysis (DEA) for efficiency correction, we shift the focus from the common central tendency orientation in its second stage to an individually oriented procedure. We propose to evaluate the influence of contextual variables on each unit's performance relative to the other operating units. This results in an alternative approach in which the second stage
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An exact method for a last-mile delivery routing problem with multiple deliverymen Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-10 Fernando Senna, Leandro C. Coelho, Reinaldo Morabito, Pedro Munari
The demand for efficient last-mile delivery systems in large cities creates an opportunity to develop innovative logistics schemes. In this paper, we study a problem in which each vehicle may travel with more than one deliveryman to serve multiple customers with a single stop of the vehicle, increasing the delivery efficiency. We extend the vehicle routing problem with time windows and multiple deliverymen
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Sobolev Calibration of Imperfect Computer Models J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-09 Qingwen Zhang, Wenjia Wang
Calibration refers to the statistical estimation of unknown model parameters in computer experiments, such that computer experiments can match underlying physical systems. This work develops a new ...
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Efficient Nonparametric Estimation of Stochastic Policy Effects with Clustered Interference J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-09 Chanhwa Lee, Donglin Zeng, Michael G. Hudgens
Interference occurs when a unit’s treatment (or exposure) affects another unit’s outcome. In some settings, units may be grouped into clusters such that it is reasonable to assume that interference...
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50 years of metaheuristics Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-09 Rafael Martí, Marc Sevaux, Kenneth Sörensen
In this paper, we review the milestones in the development of heuristic methods for optimization over the last 50 years. We propose a critical analysis of the main findings and contributions, mainly from a European perspective. Starting with the roots of the area that can be traced back to the classical philosophers, we follow the historical path of heuristics and metaheuristics in the field of operations
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Partnering with Authors to Enhance Reproducibility at JASA J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-08 Julia Wrobel, Emily C. Hector, Lorin Crawford, Lucy D’Agostino McGowan, Natalia da Silva, Jeff Goldsmith, Stephanie Hicks, Michael Kane, Youjin Lee, Vinicius Mayrink, Christopher J. Paciorek, Therri Usher, Julian Wolfson
Published in Journal of the American Statistical Association (Just accepted, 2024)
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Multilevel Factor Mixture Modeling: A Tutorial for Multilevel Constructs Struct. Equ. Model. (IF 6.0) Pub Date : 2024-04-05 Chunhua Cao, Yan Wang, Eunsook Kim
Multilevel factor mixture modeling (FMM) is a hybrid of multilevel confirmatory factor analysis (CFA) and multilevel latent class analysis (LCA). It allows researchers to examine population heterog...
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A generic approach to conference scheduling with integer programming Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-06 Yaroslav Pylyavskyy, Peter Jacko, Ahmed Kheiri
Conferences are a key aspect of communicating knowledge, and their schedule plays a vital role in meeting the expectations of participants. Given that many conferences have different constraints and objectives, different mathematical models and heuristic methods have been designed to address rather specific requirements of the conferences being studied per se. We present a penalty system that allows
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Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacy J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-05 Antonio R. Linero
Bayesian additive regression trees have seen increased interest in recent years due to their ability to combine machine learning techniques with principled uncertainty quantification. The Bayesian ...
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Inferring independent sets of Gaussian variables after thresholding correlations J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-05 Arkajyoti Saha, Daniela Witten, Jacob Bien
We consider testing whether a set of Gaussian variables, selected from the data, is independent of the remaining variables. This set is selected via a very simple approach: these are the variables ...
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Boldness-Recalibration for Binary Event Predictions Am. Stat. (IF 1.8) Pub Date : 2024-04-04 Adeline P. Guthrie, Christopher T. Franck
Probability predictions are essential to inform decision making across many fields. Ideally, probability predictions are (i) well calibrated, (ii) accurate, and (iii) bold, i.e., spread out enough ...
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Supply chain coordination in a dual sourcing system under the Tailored Base-Surge policy Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-04 Kilani Ghoudi, Younes Hamdouch, Youssef Boulaksil, Sadeque Hamdan
In this paper, we study the coordination of a dual sourcing supply chain comprising a buyer and two suppliers: a regular and an expedited one. The suppliers differ in lead time and cost, with the expedited supplier offering a shorter lead time at a higher cost than the regular supplier. The buyer uses the Tailored Base-Surge inventory policy, ordering every period a fixed quantity from the regular
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Accelerated Double-Sketching Subspace Newton Eur. J. Oper. Res. (IF 6.4) Pub Date : 2024-04-03 Jun Shang, Haishan Ye, Xiangyu Chang
This paper proposes a second-order stochastic algorithm called Accelerated Double-Sketching Subspace Newton (ADSSN) to solve large-scale optimization problems with high dimensional feature spaces and substantial sample sizes. The proposed ADSSN has two computational superiority. First, ADSSN achieves a fast local convergence rate by exploiting Nesterov’s acceleration technique. Second, by taking full