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5,827 results on '"Xie, Min"'

59. Confidence Distribution and Distribution Estimation for Modern Statistical Inference

Author

Cui, Yifan and Xie, Min-ge

Subjects
Statistics - Methodology
Abstract

This paper introduces to readers the new concept and methodology of confidence distribution and the modern-day distributional inference in statistics. This discussion should be of interest to people who would like to go into the depth of the statistical inference methodology and to utilize distribution estimators in practice. We also include in the discussion the topic of generalized fiducial inference, a special type of modern distributional inference, and relate it to the concept of confidence distribution. Several real data examples are also provided for practitioners. We hope that the selected content covers the greater part of the developments on this subject., Comment: To appear as a chapter in Springer Handbook of Engineering Statistics, 2nd ed

Published
2021

75. Design and Multi-objective Optimization for a Novel 7 DOF Inchworm-Like Robot

Author

Zhu, Minglei, Du, Yaning, Xie, Min, Qi, Jun, Ceccarelli, Marco, Series Editor, Agrawal, Sunil K., Advisory Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, and Okada, Masafumi, editor

Published
2023
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79. Fault Location of Ground Faults in Distribution Network Based on Empirical Wavelet Transform and Convolutional Neural Networks

Author

Qi, Zhenxing, Zhang, Qian, Li, Guoli, Xie, Min, Ye, Yuanbo, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Yang, Qingxin, editor, Li, Jian, editor, Xie, Kaigui, editor, and Hu, Jianlin, editor

Published
2023
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80. Accelerated Algorithms for -Happiness Query

Author

Xie, Min, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Li, Bohan, editor, Yue, Lin, editor, Tao, Chuanqi, editor, Han, Xuming, editor, Calvanese, Diego, editor, and Amagasa, Toshiyuki, editor

Published
2023
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99. Divide-and-conquer methods for big data analysis

Author

Chen, Xueying, Cheng, Jerry Q., and Xie, Min-ge

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

In the context of big data analysis, the divide-and-conquer methodology refers to a multiple-step process: first splitting a data set into several smaller ones; then analyzing each set separately; finally combining results from each analysis together. This approach is effective in handling large data sets that are unsuitable to be analyzed entirely by a single computer due to limits either from memory storage or computational time. The combined results will provide a statistical inference which is similar to the one from analyzing the entire data set. This article reviews some recently developments of divide-and-conquer methods in a variety of settings, including combining based on parametric, semiparametric and nonparametric models, online sequential updating methods, among others. Theoretical development on the efficiency of the divide-and-conquer methods is also discussed.

Published
2021

100. Lesion Penetration and Activity Limit the Utility of Second-Line Injectable Agents in Pulmonary Tuberculosis

Author

Ernest, Jacqueline P, Sarathy, Jansy, Wang, Ning, Kaya, Firat, Zimmerman, Matthew D, Strydom, Natasha, Wang, Han, Xie, Min, Gengenbacher, Martin, Via, Laura E, Barry, Clifton E, Carter, Claire L, Savic, Radojka M, and Dartois, Véronique

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Biodefense, Antimicrobial Resistance, Infectious Diseases, Clinical Research, Emerging Infectious Diseases, Rare Diseases, Orphan Drug, Lung, Clinical Trials and Supportive Activities, Tuberculosis, 6.1 Pharmaceuticals, Infection, Good Health and Well Being, Animals, Antitubercular Agents, Humans, Kanamycin, Mycobacterium tuberculosis, Rabbits, Randomized Controlled Trials as Topic, Retrospective Studies, Tuberculosis, Multidrug-Resistant, Tuberculosis, Pulmonary, aminoglycoside, drug tolerance, multidrug resistant, pharmacokinetics, tissue penetration, tuberculosis, Microbiology, Medical Microbiology, Pharmacology and Pharmaceutical Sciences, Medical microbiology, Pharmacology and pharmaceutical sciences
Abstract

Amikacin and kanamycin are second-line injectables used in the treatment of multidrug-resistant tuberculosis (MDR-TB) based on the clinical utility of streptomycin, another aminoglycoside and first-line anti-TB drug. While streptomycin was tested as a single agent in the first controlled TB clinical trial, introduction of amikacin and kanamycin into MDR-TB regimens was not preceded by randomized controlled trials. A recent large retrospective meta-analysis revealed that compared with regimens without any injectable drug, amikacin provided modest benefits, and kanamycin was associated with worse outcomes. Although their long-term use can cause irreversible ototoxicity, they remain part of MDR-TB regimens because they have a role in preventing emergence of resistance to other drugs. To quantify the contribution of amikacin and kanamycin to second-line regimens, we applied two-dimensional matrix-assisted laser desorption ionization (MALDI) mass spectrometry imaging in large lung lesions, quantified drug exposure in lung and in lesions of rabbits with active TB, and measured the concentrations required to kill or inhibit growth of the resident bacterial populations. Using these metrics, we applied site-of-action pharmacokinetic and pharmacodynamic (PK-PD) concepts and simulated drug coverage in patients' lung lesions. The results provide a pharmacological explanation for the limited clinical utility of both agents and reveal better PK-PD lesion coverage for amikacin than kanamycin, consistent with retrospective data of contribution to treatment success. Together with recent mechanistic studies dissecting antibacterial activity from aminoglycoside ototoxicity, the limited but rapid penetration of streptomycin, amikacin, and kanamycin to the sites of TB disease supports the development of analogs with improved efficacy and tolerability.

Published
2021

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