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Augmenting a simulation campaign for hybrid computer model and field data experiments Technometrics (IF 2.5) Pub Date : 2024-04-19 Scott Koermer, Justin Loda, Aaron Noble, Robert B. Gramacy
The Kennedy and O’Hagan (KOH) calibration framework uses coupled Gaussian processes (GPs) to meta-model an expensive simulator (first GP), tune its “knobs” (calibration inputs) to best match observ...
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Data-driven Pathwise Sampling Approaches for Online Anomaly Detection Technometrics (IF 2.5) Pub Date : 2024-04-18 Dongmin Li, Miao Bai, Xiaochen Xian
Moving vehicle-based sensors (MVSs) have been increasingly used for real-time sensing and anomaly detection in various applications such as the detection of wildfires and oil spills. In this paper,...
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Detection of Emergent Anomalous Structure in Functional Data Technometrics (IF 2.5) Pub Date : 2024-04-16 Edward Austin, Idris A. Eckley, Lawrence Bardwell
Motivated by an example arising from digital networks, we propose a novel approach for detecting the emergence of anomalies in functional data. In contrast to classical functional data approaches, ...
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Building Trees for Probabilistic Prediction via Scoring Rules Technometrics (IF 2.5) Pub Date : 2024-04-15 Sara Shashaani, Özge Sürer, Matthew Plumlee, Seth Guikema
Decision trees built with data remain in widespread use for nonparametric prediction. Predicting probability distributions is preferred over point predictions when uncertainty plays a prominent rol...
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Kernel-based Sensitivity Analysis for (excursion) sets Technometrics (IF 2.5) Pub Date : 2024-03-28 N. Fellmann, C. Blanchet-Scalliet, C. Helbert, A. Spagnol, D. Sinoquet
In this paper, we aim to perform sensitivity analysis of set-valued models and, in particular, to quantify the impact of uncertain inputs on feasible sets, which are key elements in solving a robus...
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Constrained Bayesian Optimization with Lower Confidence Bound Technometrics (IF 2.5) Pub Date : 2024-03-28 Neelesh S Upadhye, Raju Chowdhury
In this article, we present a hybrid Bayesian optimization (BO) framework to solve constrained optimization problems by adopting a state-of-the-art acquisition function from the unconstrained BO li...
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Federated Multiple Tensor-on-Tensor Regression (FedMTOT) for Multimodal Data under Data-Sharing Constraints Technometrics (IF 2.5) Pub Date : 2024-03-26 Zihan Zhang, Shancong Mou, Mostafa Reisi Gahrooei, Massimo Pacella, Jianjun Shi
In recent years, diversified measurements reflect the system dynamics from a more comprehensive perspective in system modeling and analysis, such as scalars, waveform signals, images, and structure...
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Statistical Process Monitoring from Industry 2.0 to Industry 4.0: Insights into Research and Practice Technometrics (IF 2.5) Pub Date : 2024-03-13 Bianca M. Colosimo, L. Allison Jones-Farmer, Fadel M. Megahed, Kamran Paynabar, Chitta Ranjan, William H. Woodall
Industry 4.0 has emerged as an important era for process monitoring and improvement. Our expository paper provides a historical perspective on research and practice of statistical process monitorin...
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Robust Multivariate Functional Control Chart Technometrics (IF 2.5) Pub Date : 2024-03-07 Christian Capezza, Fabio Centofanti, Antonio Lepore, Biagio Palumbo
In modern Industry 4.0 applications, a huge amount of data is acquired during manufacturing processes and is often contaminated with outliers, which can seriously reduce the performance of control ...
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Covariate-Dependent Clustering of Undirected Networks with Brain-Imaging Data Technometrics (IF 2.5) Pub Date : 2024-03-04 Sharmistha Guha, Rajarshi Guhaniyogi
This article focuses on model-based clustering of subjects based on the shared relationships of subject-specific networks and covariates in scenarios when there are differences in the relationship ...
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Gaussian Process Emulation for High-Dimensional Coupled Systems Technometrics (IF 2.5) Pub Date : 2024-03-04 Tamara Dolski, Elaine T. Spiller, Susan E. Minkoff
Complex coupled multiphysics simulations are ubiquitous in science and engineering. Evaluating these numerical simulators is often costly which limits our ability to run them sufficiently often for...
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Image Comparison Based On Local Pixel Clustering Technometrics (IF 2.5) Pub Date : 2024-02-22 Anik Roy, Partha Sarathi Mukherjee
Image comparison is a fundamental step for monitoring images and has wide applications in many disciplines of sciences, including satellite imaging, medical research, quality control and so forth. ...
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Deep Latent Factor Model for Spatio-Temporal Forecasting Technometrics (IF 2.5) Pub Date : 2024-02-22 Wonmo Koo, Eun-Yeol Ma, Heeyoung Kim
Latent factor models can perform spatio-temporal forecasting (i.e., predicting future responses at unmeasured as well as measured locations) by modeling temporal dependence using latent factors and...
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An Adaptive Sampling Strategy for Real-time Anomaly Detection with Unmanned Sensing Vehicles Technometrics (IF 2.5) Pub Date : 2024-02-22 Yue Jiang, Ana María Estrada Gómez
Unmanned sensing vehicles (USVs) have been widely used for real-time anomaly detection in various applications, including environmental monitoring, precision agriculture, and military surveillance....
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Mesh-clustered Gaussian process emulator for partial differential equation boundary value problems Technometrics (IF 2.5) Pub Date : 2024-02-21 Chih-Li Sung, Wenjia Wang, Liang Ding, Xingjian Wang
Partial differential equations (PDEs) have become an essential tool for modeling complex physical systems. Such equations are typically solved numerically via mesh-based methods, such as finite ele...
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Screening the Discrepancy Function of a Computer Model Technometrics (IF 2.5) Pub Date : 2024-02-20 Pierre Barbillon, Anabel Forte, Rui Paulo
Traditionally, screening refers to the problem of detecting influential (active) inputs in the computer model. We develop methodology that applies to screening, but the main focus is on detecting a...
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Backseat Driver: The Role of Data in Great Car Safety Debates Technometrics (IF 2.5) Pub Date : 2024-02-09 Shuangzhe Liu
Published in Technometrics (Vol. 66, No. 1, 2024)
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Ethics in Information Technology: A Practical Guide Technometrics (IF 2.5) Pub Date : 2024-02-09 Firdous Ahmad Mala, Shahid Abdullah Dar
Published in Technometrics (Vol. 66, No. 1, 2024)
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Handbook of Statistical Methods for Randomized Controlled Trials, 1st ed.Edited by KyungMann Kim, Frank Bretz, Ying Kuen K. Cheung, Lisa V. Hampson, New York: Chapman & Hall, 2023, 654 pp., £47.99 (paperback), ISBN 9781032009100 Technometrics (IF 2.5) Pub Date : 2024-02-09 Tri Astaria, Yoppy Wahyu Purnomo, Fery Muhamad Firdaus
Published in Technometrics (Vol. 66, No. 1, 2024)
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Quantitative Investing: from Theory to Industry Technometrics (IF 2.5) Pub Date : 2024-02-09 Stan Lipovetsky
Published in Technometrics (Vol. 66, No. 1, 2024)
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The Science of Hockey: The Math, Technology, and Data Behind the Sport Technometrics (IF 2.5) Pub Date : 2024-02-09 Zulfaidil, Utari Akhir Gusti, Waliyyatu Azzahra, Gantina Rachmaputri
Published in Technometrics (Vol. 66, No. 1, 2024)
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Smart Grid and Enabling Technologies Technometrics (IF 2.5) Pub Date : 2024-02-09 Antony Ndolo
Published in Technometrics (Vol. 66, No. 1, 2024)
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(Almost) Impossible Integrals, Sums, and Series Technometrics (IF 2.5) Pub Date : 2024-02-09 Stan Lipovetsky
Published in Technometrics (Vol. 66, No. 1, 2024)
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Transfer Learning with Large-Scale Quantile Regression Technometrics (IF 2.5) Pub Date : 2024-02-09 Jun Jin, Jun Yan, Robert H. Aseltine, Kun Chen
Quantile regression is increasingly encountered in modern big data applications due to its robustness and flexibility. We consider the scenario of learning the conditional quantiles of a specific t...
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Bayesian Optimization via Exact Penalty Technometrics (IF 2.5) Pub Date : 2024-02-07 Jiangyan Zhao, Jin Xu
Constrained optimization problems pose challenges when the objective function and constraints are nonconvex and their evaluation requires expensive black-box simulations. Recently, hybrid optimizat...
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Technometrics 2023 Associate Editors Technometrics (IF 2.5) Pub Date : 2024-02-09
Published in Technometrics (Vol. 66, No. 1, 2024)
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Moving sum procedure for change point detection under piecewise linearity Technometrics (IF 2.5) Pub Date : 2024-01-22 Joonpyo Kim, Hee-Seok Oh, Haeran Cho
Abstract–We propose a computationally and statistically efficient procedure for segmenting univariate data under piecewise linearity. The proposed moving sum (MOSUM) methodology detects multiple ch...
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Discrepancy measures for global sensitivity analysis Technometrics (IF 2.5) Pub Date : 2024-01-18 Arnald Puy, Pamphile T. Roy, Andrea Saltelli
While sensitivity analysis improves the transparency and reliability of mathematical models, its uptake by modelers is still scarce. This is partially explained by its technical requirements, which...
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Partial Tail-Correlation Coefficient Applied to Extremal-Network Learning Technometrics (IF 2.5) Pub Date : 2024-01-10 Yan Gong, Peng Zhong, Thomas Opitz, Raphaël Huser
We propose a novel extremal dependence measure called the partial tail-correlation coefficient (PTCC), in analogy to the partial correlation coefficient in classical multivariate analysis. The cons...
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Bayesian Semiparametric Local Clustering of Multiple Time Series Data Technometrics (IF 2.5) Pub Date : 2023-12-21 Jingjing Fan, Abhra Sarkar
In multiple time series data, clustering the component profiles can identify meaningful latent groups while also detecting interesting change points in their trajectories. Conventional time series ...
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Assessing measurement system agreement in the presence of reproducibility and repeatability Technometrics (IF 2.5) Pub Date : 2023-12-18 Adel Ahmadi Nadi, Stefan H. Steiner, Nathaniel T. Stevens
Assessing the agreement between an established and a new measurement system is a practical and important challenge in many application areas. The probability of agreement (PoA) has recently been in...
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A Global-Local Approximation Framework for Large-Scale Gaussian Process Modeling Technometrics (IF 2.5) Pub Date : 2023-12-18 Akhil Vakayil, V. Roshan Joseph
In this work, we propose a novel framework for large-scale Gaussian process (GP) modeling. Contrary to the global, and local approximations proposed in the literature to address the computational b...
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A graphical multi-fidelity Gaussian process model, with application to emulation of heavy-ion collisions Technometrics (IF 2.5) Pub Date : 2023-11-09 Yi Ji, Simon Mak, Derek Soeder, J-F Paquet, Steffen A. Bass
With advances in scientific computing and mathematical modeling, complex scientific phenomena such as galaxy formations and rocket propulsion can now be reliably simulated. Such simulations can how...
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Computer Age Statistical Inference: Algorithms, Evidence, and Data Science, Student ed. Technometrics (IF 2.5) Pub Date : 2023-11-03 Stan Lipovetsky
Published in Technometrics (Vol. 65, No. 4, 2023)
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A Proportional Intensity Model with Frailty for Missing Recurrent Failure Data Technometrics (IF 2.5) Pub Date : 2023-11-02 Suk Joo Bae, Byeong Min Mun, Xiaoyan Zhu
In some practical circumstances, data are recorded after the systems have begun operations, and data collection is stopped at a predetermined time or after a predetermined number of failures. In su...
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A Criminologist’s Guide to R: Crime by the Numbers Technometrics (IF 2.5) Pub Date : 2023-11-03 Enrique Garcia-Ceja
Published in Technometrics (Vol. 65, No. 4, 2023)
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Mathematics of The Big Four Casino Table Games: Blackjack, Baccarat, Craps, & Roulette Technometrics (IF 2.5) Pub Date : 2023-11-03 Stan Lipovetsky
Published in Technometrics (Vol. 65, No. 4, 2023)
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Statistical Genomics Technometrics (IF 2.5) Pub Date : 2023-11-03 Irvanal Haq, Nila Lestari
Published in Technometrics (Vol. 65, No. 4, 2023)
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AI, Machine Learning and Deep Learning a Security Perspective Technometrics (IF 2.5) Pub Date : 2023-11-03 Fajar Pitarsi Dharma, Moses Laksono Singgih, Hamdan S. Bintang
Published in Technometrics (Vol. 65, No. 4, 2023)
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Luck, Logic, and White Lies: The Mathematics of Games; 2nd ed. Technometrics (IF 2.5) Pub Date : 2023-11-03 Stan Lipovetsky
Published in Technometrics (Vol. 65, No. 4, 2023)
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Machine Learning for Knowledge Discovery with R: Methodologies for Modeling, Inference, and Prediction Technometrics (IF 2.5) Pub Date : 2023-11-03 Aszani Aszani
Published in Technometrics (Vol. 65, No. 4, 2023)
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Post-Shrinkage Strategies in Statistical and Machine Learning for High Dimensional Data Technometrics (IF 2.5) Pub Date : 2023-11-03 Abdulkadir Hussein
Published in Technometrics (Vol. 65, No. 4, 2023)
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Towards Improved Heliosphere Sky Map Estimation with Theseus Technometrics (IF 2.5) Pub Date : 2023-10-24 Dave Osthus, Brian P. Weaver, Lauren J. Beesley, Kelly R. Moran, Madeline A. Stricklin, Eric J. Zirnstein, Paul H. Janzen, Daniel B. Reisenfeld
The Interstellar Boundary Explorer (IBEX) satellite has been in orbit since 2008 and detects energy-resolved energetic neutral atoms (ENAs) originating from the heliosphere. Different regions of th...
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Efficient Model-free Subsampling Method for Massive Data Technometrics (IF 2.5) Pub Date : 2023-10-18 Zheng Zhou, Zebin Yang, Aijun Zhang, Yongdao Zhou
Subsampling plays a crucial role in tackling problems associated with the storage and statistical learning of massive datasets. However, most existing subsampling methods are model-based, which mea...
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Tensor-based Temporal Control for Partially Observed High-dimensional Streaming Data Technometrics (IF 2.5) Pub Date : 2023-10-16 Zihan Zhang, Shancong Mou, Kamran Paynabar, Jianjun Shi
In advanced manufacturing processes, high-dimensional (HD) streaming data (e.g., sequential images or videos) are commonly used to provide online measurements of product quality. Although there exi...
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Statistical Process Monitoring of Artificial Neural Networks Technometrics (IF 2.5) Pub Date : 2023-09-22 Anna Malinovskaya, Pavlo Mozharovskyi, Philipp Otto
The rapid advancement of models based on artificial intelligence demands innovative monitoring techniques which can operate in real time with low computational costs. In machine learning, especiall...
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Model Mixing Using Bayesian Additive Regression Trees Technometrics (IF 2.5) Pub Date : 2023-09-13 John C. Yannotty, Thomas J. Santner, Richard J. Furnstahl, Matthew T. Pratola
Abstract In modern computer experiment applications, one often encounters the situation where various models of a physical system are considered, each implemented as a simulator on a computer. An important question in such a setting is determining the best simulator, or the best combination of simulators, to use for prediction and inference. Bayesian model averaging (BMA) and stacking are two statistical
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Signal Classification in Large-Scale Multi-Sequence Integrative Analysis Under the HMM Dependence Technometrics (IF 2.5) Pub Date : 2023-09-12 Wendong Li, Dongdong Xiang, Gongtao Chen, Peihua Qiu
Abstract The integrative analysis of multiple sequences of multiple tests has enjoyed increasing popularity in many applications, especially in large-scale genomics. In the context of large-scale multiple testing, the concept of signal classification has been developed recently for cases when the same features are involved in several independent studies, with the goal of classifying each feature into
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Inference for the Optimum using Linear Regression Models with Discrete Inputs Technometrics (IF 2.5) Pub Date : 2023-08-28 Robert W. Mee, Hui Li
Abstract We present a multiple-comparison-with-the-best procedure to provide inference for the optimum from regression models with discrete inputs. Two applications are given to illustrate the methodology: two-level factorial designs to identify the best drug combination and order-of-addition experiments where the primary objective is to identify the sequence with the largest mean response. The methods
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Federated Multi-Output Gaussian Processes Technometrics (IF 2.5) Pub Date : 2023-08-29 Seokhyun Chung, Raed Al Kontar
Multi-output Gaussian process (MGP) regression plays an important role in the integrative analysis of different but interrelated systems/units. Existing MGP approaches assume that data from all uni...
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The Energy of Data and Distance Correlation, Technometrics (IF 2.5) Pub Date : 2023-08-22 Stan Lipovetsky
Published in Technometrics (Vol. 65, No. 3, 2023)
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Mathematical Modeling in Biology: A Research Methods Approach, 1st ed. Technometrics (IF 2.5) Pub Date : 2023-08-22 Suprianto, Nur Alam
Published in Technometrics (Vol. 65, No. 3, 2023)
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Polynomial Methods and Incidence Theory Technometrics (IF 2.5) Pub Date : 2023-08-22 Firdous Ahmad Mala
Published in Technometrics (Vol. 65, No. 3, 2023)
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Mathletics: How Gamblers, Managers, and Fans Use Mathematics in Sports, 2nd ed. Technometrics (IF 2.5) Pub Date : 2023-08-22 Ahmad Mala Firdous
Published in Technometrics (Vol. 65, No. 3, 2023)
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Artificial Intelligence with Python Technometrics (IF 2.5) Pub Date : 2023-08-22 Aminatus Sa’adah
Published in Technometrics (Vol. 65, No. 3, 2023)
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Sequential Bayesian experimental design for calibration of expensive simulation models Technometrics (IF 2.5) Pub Date : 2023-08-09 Özge Sürer, Matthew Plumlee, Stefan M. Wild
Abstract Simulation models of critical systems often have parameters that need to be calibrated using observed data. For expensive simulation models, calibration is done using an emulator of the simulation model built on simulation output at different parameter settings. Using intelligent and adaptive selection of parameters to build the emulator can drastically improve the efficiency of the calibration
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A Class of Hierarchical Multivariate Wiener Processes for Modeling Dependent Degradation Data Technometrics (IF 2.5) Pub Date : 2023-07-31 Guanqi Fang, Rong Pan
Abstract In engineering practice, many products exhibit multiple and dependent degrading performance characteristics (PCs). It is common to observe that these PCs’ initial measurements are non-constant and sometimes correlated with the subsequent degradation rate, which typically varies from one unit to another. To accommodate the unit-wise heterogeneity, PC-wise dependency, and “initiation-growth”
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Statistical Modeling of the Effectiveness of Preventive Maintenance for Repairable Systems Technometrics (IF 2.5) Pub Date : 2023-07-26 Xin Ye, Jiaxiang Cai, Loon Ching Tang, Zhi-Sheng Ye
Preventive maintenance (PM) is commonly adopted in practice to improve a system’s health condition and reduce the risk of unexpected failures. When a PM action is poorly performed, however, it is l...
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Sequential Designs for Filling Output Spaces Technometrics (IF 2.5) Pub Date : 2023-07-24 Shangkun Wang, Adam P. Generale, Surya R. Kalidindi, V. Roshan Joseph
Space-filling designs are commonly used in computer experiments to fill the space of inputs so that the input–output relationship can be accurately estimated. However, in certain applications such ...
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Bayesian Modeling and Inference for One-Shot Experiments Technometrics (IF 2.5) Pub Date : 2023-07-24 Jonathan Rougier, Andrew Duncan
In one-shot experiments, units are subjected to varying levels of stimulus and their binary response (go/no-go) is recorded. Experimental data is used to estimate the “sensitivity function”, which ...