Article
Automation & Control Systems
Syeda Sakira Hassan, Simo Sarkka
Summary: This article proposes a novel computational method for solving nonlinear optimal control problems. The method uses Fourier-Hermite series to approximate the action-value function in dynamic programming and uses sigma-point methods to numerically compute the coefficients of the series. The method is proven to have quadratic convergence and its performance is tested experimentally.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Mario Becerra, Peter Goos
Summary: Discrete choice experiments are commonly used to quantify consumer preferences, but designing choice experiments involving mixtures of ingredients has been largely neglected. The I-optimality criterion is more suitable for precise predictions in experiments with mixtures, as it focuses on estimated statistical models. In this research, Bayesian I-optimal designs are shown to outperform their Bayesian D-optimal counterparts in terms of predicted utility variance.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2021)
Article
Engineering, Aerospace
Qing Li, Bashar Ahmad, Simon J. Godsill
Summary: This article introduces a framework for automatically identifying group structure and leadership from noisy sensory observations in tracked groups. A new leader-follower model is developed to assume the dynamics of the group, with a focus on designated leaders drifting to the destination and followers reverting to the leaders' state. The proposed techniques successfully determine leadership structures in challenging scenarios, enhancing tracking accuracy through consideration of leadership interactions within the group.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2021)
Review
Medicine, General & Internal
Kelley M. Kidwell, Daniel Almirall
Summary: This article explains sequential, multiple assignment, randomized trial (SMART) study designs, where participants are randomized at 2 or more decision points depending on their response to prior treatment.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
(2023)
Article
Automation & Control Systems
Emir Demirovic, Anna Lukina, Emmanuel Hebrard, Jeffrey Chan, James Bailey, Christopher Leckie, Kotagiri Ramamohanarao, Peter J. Stuckey
Summary: Decision tree learning is a widely used approach in machine learning, especially in applications that require concise and interpretable models. Traditional heuristic methods quickly produce models with high accuracy, but the resulting trees may not necessarily be the best representation of the data in terms of accuracy and size. To address this, optimal classification tree algorithms have been developed to globally optimize the decision tree. We present a novel algorithm based on dynamic programming and search, which supports constraints on the depth and number of nodes. Our approach improves upon traditional algorithms by utilizing specialized techniques unique to classification trees, resulting in significantly faster runtimes and improved scalability.
JOURNAL OF MACHINE LEARNING RESEARCH
(2022)
Article
Oncology
Emily C. Zabor, Alexander M. Kaizer, Elizabeth Garrett-Mayer, Brian P. Hobbs
Summary: The authors propose a method for the design of phase I expansion cohorts in early-phase dose-escalation designs based on sequential predictive probability monitoring. They introduce two optimization criteria to determine efficacy and allow for early termination of dose-expansion cohorts that show no promising results, while maintaining traditional control of type I and II errors.
JCO PRECISION ONCOLOGY
(2022)
Article
Mathematics
Amelia Badica, Costin Badica, Ion Buligiu, Liviu Ion Ciora, Doina Logofatu
Summary: The study focuses on hierarchically shaped single-elimination tournaments, proposing a dynamic programming algorithm for optimal tournaments and efficient sub-optimal algorithms. Experimental results with Python implementation confirm the effectiveness of the solutions and efficiency of running time.
Article
Management
Ken Seng Tan, Chengguo Weng, Jinggong Zhang
Summary: This paper proposes an optimal dynamic strategy for hedging longevity risk in a discrete-time setting using standardized mortality-linked securities to minimize the variance of the hedging error induced by population basis risk. Through a stylized pension plan and a specified trading strategy, the authors show how the hedging problem can be formulated as a stochastic optimal control framework and a semi-analytic solution can be derived using an extended Bellman equation. Extensive Monte Carlo studies highlight the effectiveness of the proposed hedging strategy and a scheme is considered to approximate the semi-analytic solution for practical implementation.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Automation & Control Systems
Zebin Yang, Aijun Zhang
Summary: This paper reformulates HPO as a computer experiment and proposes a novel SeqUD strategy with threefold advantages. Extensive experiments show that the proposed SeqUD strategy outperforms benchmark HPO methods, making it a promising and competitive alternative in the field of AutoML.
JOURNAL OF MACHINE LEARNING RESEARCH
(2021)
Article
Mathematics
Belmiro P. M. Duarte
Summary: We propose mixed-integer semidefinite programming formulations for finding exact optimal designs for linear models and locally optimal designs for nonlinear models. The strategy involves generating candidate treatments, formulating the optimal design problem as a mixed-integer semidefinite program, and solving it using appropriate solvers. We also use semidefinite programming-based formulations to find equivalent approximate optimal designs for comparison.
Article
Management
Denis Lebedev, Paul Goulart, Kostas Margellos
Summary: This study focuses on revenue management in attended home delivery using dynamic programming. The unique fixed point of the underlying Bellman operator is proven, with a closed-form expression and continuous extension provided. Monotonicity of prices with respect to the number of orders placed is shown, offering a pathway for scalable implementations.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Engineering, Electrical & Electronic
Xuan He, Kui Cai, Wentu Song, Zhen Mei
Summary: In this article, a dynamic programming method is proposed to obtain the optimal deterministic quantizer and improve performance with reduced complexity. Two techniques are applied to reduce complexity and generalize results for binary input channels. The method is further applied to practical pulse-amplitude modulation systems.
IEEE TRANSACTIONS ON COMMUNICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Ali Heydari
Summary: This study investigates optimal control of nonlinear impulsive systems with free impulse instants, developing a scheme based on adaptive dynamic programming. The scheme handles single and multiple impulsive actuators efficiently and is applied to challenging problems such as spacecraft orbital maneuver.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Sleiman Mhanna, Pierluigi Mancarella
Summary: Despite advancements in nonlinear programming and convex relaxations, linear programming is still widely used by system operators due to its reliability and computational efficiency. This paper proposes a sequential linear programming approach that combines the advantages of LP and NLP methods, providing feasible and high-quality solutions for the nonconvex AC OPF problem.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Automation & Control Systems
Anil V. T. Rao, John V. Betts
Summary: The review of this book focuses on the author's profound guidance in solving general optimal control problems numerically using non-linear programming. The book emphasizes the strong connection between optimal control and non-linear programming, highlighting the need for sophisticated techniques in solving these problems. It is divided into ten chapters, covering topics such as non-linear programming, large sparse non-linear programming, optimal control, and direct collocation methods.
IEEE CONTROL SYSTEMS MAGAZINE
(2023)
Article
Computer Science, Interdisciplinary Applications
Blair Robertson, Chris Price
Summary: Spatial sampling designs are crucial for accurate estimation of population parameters. This study proposes a new design method that generates samples with good spatial spread and performs favorably compared to existing designs.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Hiroya Yamazoe, Kanta Naito
Summary: This paper focuses on the simultaneous confidence region of a one-dimensional curve embedded in multi-dimensional space. An estimator of the curve is obtained through local linear regression on each variable in multi-dimensional data. A method to construct a simultaneous confidence region based on this estimator is proposed, and theoretical results for the estimator and the region are developed. The effectiveness of the region is demonstrated through simulation studies and applications to artificial and real datasets.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Cheng Peng, Drew P. Kouri, Stan Uryasev
Summary: This paper introduces a novel optimal experimental design method for quantifying the distribution tails of uncertain system responses. The method minimizes the variance or conditional value-at-risk of the upper bound of the predicted quantile, and estimates the data uncertainty using quantile regression. The optimal design problems are solved as linear programming problems, making the proposed methods efficient even for large datasets.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Xiaofei Wu, Hao Ming, Zhimin Zhang, Zhenyu Cui
Summary: This paper proposes a model that combines quantile regression and fused LASSO penalty, and introduces an iterative algorithm based on ADMM to solve high-dimensional datasets. The paper proves the global convergence and comparable convergence rates of the algorithm, and analyzes the theoretical properties of the model. Numerical experimental results support the superior performance of the model.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Xin He, Xiaojun Mao, Zhonglei Wang
Summary: This paper proposes a nonparametric imputation method with sparsity to estimate the finite population mean, using an efficient kernel method and sparse learning for estimation. An augmented inverse probability weighting framework is adopted to achieve a central limit theorem for the proposed estimator under regularity conditions.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Christian H. Weiss, Fukang Zhu
Summary: This study introduces a multiplicative error model (CMEMs) for discrete-valued count time series, which is closely related to the integer-valued generalized autoregressive conditional heteroscedasticity (INGARCH) models. It derives the stochastic properties and estimation approaches of different types of INGARCH-CMEMs, and demonstrates their performance and application through simulations and real-world data examples.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Ming-Hung Kao, Ping-Han Huang
Summary: Optimal designs for sparse functional data under the functional empirical component (FEC) settings are investigated. New computational methods and theoretical results are developed to efficiently obtain optimal exact and approximate designs. A hybrid exact-approximate design approach is proposed and demonstrated to be efficient through simulation studies and a real example.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Mateus Maia, Keefe Murphy, Andrew C. Parnell
Summary: The Bayesian additive regression trees (BART) model is a powerful ensemble method for regression tasks, but its lack of smoothness and explicit covariance structure can limit its performance. The Gaussian processes Bayesian additive regression trees (GP-BART) model addresses this limitation by incorporating Gaussian process priors, resulting in superior performance in various scenarios.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Xichen Mou, Dewei Wang
Summary: Human biomonitoring is a method of monitoring human health by measuring the accumulation of harmful chemicals in the body. To reduce the high cost of chemical analysis, researchers have adopted a cost-effective approach that combines specimens and analyzes the concentration of toxic substances in the pooled samples. To effectively interpret these aggregated measurements, a new regression framework is proposed by extending the additive partially linear model (APLM). The APLM is versatile in capturing the complex association between outcomes and covariates, making it valuable in assessing the complex interplay between chemical bioaccumulation and potential risk factors.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Lili Yu, Yichuan Zhao
Summary: The classical accelerated failure time model is a linear model commonly used for right censored survival data, but it cannot handle heteroscedastic survival data. This paper proposes a Laplace approximated quasi-likelihood method with a continuous estimating equation to address this issue, and provides estimation bias and confidence interval estimation formulas.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Shaobo Jin, Youngjo Lee
Summary: Hierarchical generalized linear models are widely used for fitting random effects models, but the standard error estimators receive less attention. Current standard error estimation methods are not necessarily accurate, and a sandwich estimator is proposed to improve the accuracy of standard error estimation.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Rebeca Pelaez, Ingrid Van Keilegom, Ricardo Cao, Juan M. Vilar
Summary: This article proposes an estimator for the probability of default (PD) in credit risk, derived from a nonparametric conditional survival function estimator based on cure models. The asymptotic expressions for bias, variance, and normality of the estimator are presented. Through simulation and empirical studies, the performance and practical behavior of the nonparametric estimator are compared with other methods.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
L. M. Andre, J. L. Wadsworth, A. O'Hagan
Summary: This paper proposes a dependence model that captures the entire data range in multi-variable cases. By blending two copulas with different characteristics and using a dynamic weighting function for smooth transition, the model is able to flexibly capture various dependence structures.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Niwen Zhou, Xu Guo, Lixing Zhu
Summary: The paper investigates hypothesis testing regarding the potential additional contributions of other covariates to the structural function, given the known covariates. The proposed distance-based test, based on Neyman's orthogonality condition, effectively detects local alternatives and is robust to the influence of nuisance functions. Numerical studies and real data analysis demonstrate the importance of this test in exploring covariates associated with AIDS treatment effects.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)
Article
Computer Science, Interdisciplinary Applications
Blake Moya, Stephen G. Walker
Summary: A full posterior analysis method for nonparametric mixture models using Gibbs-type prior distributions, including the well known Dirichlet process mixture (DPM) model, is presented. The method removes the random mixing distribution and enables a simple-to-implement Markov chain Monte Carlo (MCMC) algorithm. The removal procedure reduces some of the posterior uncertainty and introduces a novel replacement approach. The method only requires the probabilities of a new or an old value associated with the corresponding Gibbs-type exchangeable sequence, without the need for explicit representations of the prior or posterior distributions. This allows the implementation of mixture models with full posterior uncertainty, including one introduced by Gnedin. The paper also provides numerous illustrations and introduces an R-package called CopRe that implements the methodology.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2024)