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ORCIDTechnometrics, Volume 67
Volume 67, Number 1, January 2025
- Bradley A. Jones, Ryan Lekivetz, Dibyen Majumdar, Christopher J. Nachtsheim:
Screening Designs for Continuous and Categorical Factors. 1-10 - Guanzhou Wei, Feng Qiu, Xiao Liu
:
Convolutional Non-Homogeneous Poisson Process and its Application to Wildfire Ignition Risk Quantification for Power Delivery Networks. 11-22 - Haoxuan Wu, Toryn L. J. Schafer, Sean E. Ryan, David S. Matteson:
Drift versus Shift: Decoupling Trends and Changepoint Analysis. 23-31 - Qiyuan Chen, Liangkui Jiang, Hantang Qin, Raed Al Kontar:
Multi-Agent Collaborative Bayesian Optimization via Constrained Gaussian Processes. 32-45 - Henrik Imberg, Xiaomi Yang, Carol A. C. Flannagan, Jonas Bärgman:
Active Sampling: A Machine-Learning-Assisted Framework for Finite Population Inference with Optimal Subsamples. 46-57 - Junoh Heo, Chih-Li Sung:
Active Learning for a Recursive Non-Additive Emulator for Multi-Fidelity Computer Experiments. 58-72 - Jordan Bryan, Peter D. Hoff, Christopher L. Osburn:
Linear Source Apportionment Using Generalized Least Squares. 73-81 - Lei Jin, Bo Li:
Scalable Methods for Multiple Time Series Comparison in Second Order Dynamics. 82-96 - Xuanjie Shao, Arnab Hazra, Jordan Richards, Raphaël Huser:
Flexible Modeling of Nonstationary Extremal Dependence using Spatially Fused LASSO and Ridge Penalties. 97-111 - Romain Ait Abdelmalek-Lomenech, Julien Bect, Vincent Chabridon, Emmanuel Vázquez:
Bayesian Sequential Design of Computer Experiments for Quantile Set Inversion. 112-121 - Jun Yu, HaiYing Wang, Mingyao Ai:
A Subsampling Strategy for AIC-based Model Averaging with Generalized Linear Models. 122-132 - Kellin N. Rumsey, Zachary K. Hardy, Cory Ahrens, Scott A. Vander Wiel:
Co-Active Subspace Methods for the Joint Analysis of Adjacent Computer Models. 133-146 - Eloísa Díaz-Francés:
Practical and Optimal Likelihood Intervals and Regions for Weibull and Gumbel Distributions. 147-156 - Toryn L. J. Schafer, David S. Matteson:
Locally Adaptive Shrinkage Priors for Trends and Breaks in Count Time Series. 157-167 - Yiran Huang, Jian-Feng Yang:
Robust Design for Order-of-Addition Experiments. 168-176 - Stan Lipovetsky:
Correlation in Engineering and the Applied Sciences: Applications in R: Rajan Chattamvelli, Cham, Switzerland: Springer/Springer Nature Switzerland AG, 2024, xvi + 178 pp., $44.99 (hbk), ISBN 978-3-031-51014-4. 177-178 - Stan Lipovetsky:
Advanced Statistical Methods: Sahana Prasad, Singapore: Springer Nature, 2024, xxiii + 218 pp., 63 b/w + 11 color illustrations, $119.99 (hbk), ISBN 978-981-99-7256-2. 178-179 - Stan Lipovetsky:
Statistics Today: Everyday Applications, Research Questions, Insights, and Challenges: Edited by Claus Weihs, Walter Krämer, and Sarah Buschfeld, Berlin, Heidelberg, Germany: Springer Nature, 2024, xxi + 252 pp., 53 b/w + 46 color illustrations, $44.99 (hbk), ISBN 978-3-662-68906-6. 179-181 - Zulfaidil, Shelma Maharani Pelu:
Intelligent Fatigue Statistics: By Zhentong Gao and Jiajin Xu, Boca Raton: CRC Press, 2024, 258 pp., £65.44, ISBN 9781003488477 (ebook). 181-182 - Siti Mutiah, Indra Rivaldi Siregar:
Models for Multi-State Survival Data Rates, Risks, and Pseudo-Values, 1st ed.: Per Kragh Andersen and Henrik Ravn, CRC Press Taylor & Francis Group, 2024, XIV + 292 pp., 94 B/W Illustrations, £61.59, ISBN: 978-0-367-14002-1. Boca Raton and Oxon. Scope: Texts in Statistical Science Series. Level: academics, graduate students, practitioners, and professionals. 182-184 - Fransiskus Serfian Jogo:
Statistical Prediction and Machine Learning: John Tuhao Chen, Clement Lee, Lincy Y. Chen, CRC Press, 2024, 314 pp., $74.99, ISBN 9780429318689. 184-185 - James M. Lucas:
OMARS Letter for Technometrics. 186-188 - Peter Goos, José Núñez Ares:
Response to Letter to the Editor. 189-191
Volume 67, Number 2, April 2025
- Cristian Oliva-Aviles, Paloma Hauser:
Tolerance Intervals Under a Class of Unbalanced Linear Mixed Models. 193-202 - Jaeshin Park, Eunshin Byon, Young Myoung Ko, Sara Shashaani:
Strata Design for Variance Reduction in Stochastic Simulation. 203-214 - Conor Murphy, Jonathan A. Tawn, Zak Varty:
Automated Threshold Selection and Associated Inference Uncertainty for Univariate Extremes. 215-224 - Benjamin K. Dahl, Matthew J. Heaton, Richard L. Warr, Jared D. Fisher, Grant G. Schultz:
Modeling Crash Risk on Roadway Networks Using Bayesian Regression Trees. 225-237 - Peihong Xiao, Yudong Wang, Wenting Liu, Zhisheng Ye:
Remaining Useful Life Prediction Based on Forward Intensity. 238-253 - Jun Jin, Chao Ying, Zhou Yu:
Distributed Estimation of Principal Support Vector Machines for Sufficient Dimension Reduction. 254-266 - James Yang, Trevor Hastie:
Note on the Equivalence of Orthogonalizing EM and Proximal Gradient Descent. 267-269 - Gildas Mazo, Laurent Tournier:
An Inference Method for Global Sensitivity Analysis. 270-282 - Takayuki Iguchi, Andrés F. Barrientos, Eric Chicken, Debajyoti Sinha:
Profile Monitoring via Eigenvector Perturbation. 283-292 - Di Wang, Xiaochen Xian, Haidong Li:
Spatiotemporal Interactive Modeling of Event-Based Dynamic Networks. 293-310 - Abdul-Nasah Soale:
Detecting Influential Observations in Single-Index Fréchet Regression. 311-322 - Xingcai Zhou, Shengxian Ding, Jiangyan Wang, Rongjie Liu, Linglong Kong, Chao Huang:
Distribution-on-Scalar Single-Index Quantile Regression Model for Handling Tumor Heterogeneity. 323-332 - Youngdeok Hwang, Hang Joon Kim, Won Chang, Christian Hong, Steven N. MacEachern:
Bayesian Model Calibration and Sensitivity Analysis for Oscillating Biological Experiments. 333-343 - Arnab Chakraborty, Soumendra Nath Lahiri:
On Statistical Properties of a Veracity Scoring Method for Spatial Data. 344-355 - Stan Lipovetsky:
Modeling Reality with Mathematics: Alfio Quarteroni, Cham, Switzerland: Springer/Springer Nature Switzerland AG, 2022, xix + 123 pp., 20 illustrations in color, $24.99 (hbk), ISBN 978-3-030-96161-9. 356-358 - Sangjin Kim, Jai Woo Lee:
Text as Data: A New Framework for Machine Learning and the Social Sciences: Justin Grimmer, Margaret E. Roberts, and Brandon M. Stewart, Princeton, NJ: Princeton University Press, 2022, 360 pp. $99.95 (Hardcover), ISBN: 9780691207544, $43 (Paperback), ISBN: 9780691207551. 358-359 - Shuangzhe Liu, Yonghui Liu:
Skew-Normal Model Theories and Their Applications: Rendao Ye, Kun Luo, Wenyan Zhu, Yue Qi, Boca Raton, FL: CRC Press, 2024, 272, with 19 B/W illustrations, ISBN 9781032694696 (Hardcover). 359-360 - Cacu Cacu, Dadan Hermawan:
Robust and Multivariate Statistical Methods: Festschrift in Honor of David E. Tyler: edited by Mengxi Yi and Klaus Nordhausen, Cham: Springer, 2023, XVIII + 495 pp., €149.79 (eBook), €179.79 (Softcover and Hardcover), ISBN 978-3-031-22687-8 (eBook), ISBN 978-3-031-22689-2 (Softcover), ISBN 978-3-031-22686-1 (Hardcover). 360-362 - Johanes Robert Kera, Agri Satrio Adi Nugroho:
Machine Learning For Transportation Research and Application: Yinhai Wang, Zhiyong Cui, and Ruimin Ke, Amsterdam, Netherlands: Elsevier, 2023, 231 pp., ISBN 978-0-323-96126-4. 362-363 - Oktaviyani Daswati, Yunia Hasnataeni:
Applied Microbiome Statistics Correlation, Association, Interaction and Composition, 1st ed: Yinglin Xia and Jun Sun, Boca Raton and Oxon: CRC Press Taylor & Francis Group, 2024, 456 pp., 46 Color & 5 B/W Illustrations, £140.00 ISBN 978-0-367-63970-9. Scope: Text in Biostatistics Series. Level: academics, students, researcher, and practitioners. 364-365
Volume 67, Number 3, July 2025
- Russell R. Barton, Max D. Morris:
Experimental Design and Modeling for Forward-Inverse Maps. 367-381 - David M. Steinberg:
Thoughts on Forward-Inverse Maps. 382-383 - Shangkun Wang, V. Roshan Joseph:
Comment: A Model-Free Method for Input-Output Space-Filling Design. 384-387 - Christine M. Anderson-Cook, Lu Lu:
Discussion of "Experimental Design and Modeling for Forward-Inverse Maps" by R.R. Barton and M.D. Morris. 388-390 - Youssef Marzouk:
Discussion of "Experimental Design and Modeling for Forward-Inverse Maps" by R. Barton & M. Morris, appearing in Technometrics. 391-393 - Russell R. Barton, Max D. Morris:
Rejoinder. 394-396 - Pratik Nag, Ying Sun, Brian J. Reich:
Bivariate DeepKriging for Large-Scale Spatial Interpolation of Wind Fields. 397-408 - Jiuyun Hu, Naichen Shi, Raed Al Kontar, Hao Yan:
Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data. 409-425 - Maryclare Griffin, Gennady Samorodnitsky, David S. Matteson:
Likelihood Inference for Possibly Nonstationary Processes via Adaptive Overdifferencing. 426-439 - Jiannan Yang:
Derivative Based Global Sensitivity Analysis and Its Entropic Link. 440-450 - Yongxiang Li, Yunji Zhang, Qian Xiao, Jianguo Wu:
Noise Resistant Control Charts for Detecting Periodicity from Correlation. 451-463 - Di Wang, Andi Wang, Xiaochen Xian, Yongxiang Li:
Partially Observable Online Nonparametric Monitoring of Spatiotemporally Correlated Data Streams. 464-480 - Christopher Qian, Daniel Ries, Feng Liang, Jason Adams:
Regression Recalibration by Learning PIT Map Values. 481-492 - Jiaxiang Cai, William Q. Meeker, Zhi-Sheng Ye:
Optimal Planning of Destructive Degradation Tests. 493-504 - Marcus Mayrhofer, Una Radojicic, Peter Filzmoser:
Robust Covariance Estimation and Explainable Outlier Detection for Matrix-Valued Data. 516-530 - Jie Min, Yili Hong, William Q. Meeker, George Ostrouchov:
A Spatially Correlated Competing Risks Time-to-Event Model for Supercomputer GPU Failure Data. 531-545
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