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A Robust Quantitative Risk Screening for Subgroup Pursuit in Clinical Trials

Econometrics and Statistics 2023年

作者： Guo, Xinzhou Li, Ruosha Zhou, Jianjun He, Xuming Department of Mathematics Hong Kong University of Science and Technology Clear Water Bay Kowloon 999077 Hong Kong Department of Biostatistics and Data Science University of Texas Health Science Center at Houston 7000 Fannin Street Suite 2250 Houston 77030 United States Yunnan Provincial Key Laboratory of Statistical Modeling and Data Analysis Yunnan University 2 Cuihu North Road Kunming 650091 China Department of Statistics University of Michigan 1085 South University Ave Ann Arbor 48109 United States

In clinical studies, when to recommend or decide further pursuit of the most promising subgroup that has been observed from an existing trial is a very important question. It is well recognized that the working models in assessing subgroup effects might be misspecified and the observed treatment effect size of the best selected subgroup tends to be too optimistic. Therefore, a careful and robust statistical quantification of risk is useful before any decision of subgroup pursuit is made. Via the newly established bootstrap consistency for the misspecified proportional hazard model, the issue of selection bias and model misspecification in subgroup pursuit is addressed, and a robust risk quantitative measure directly based on the observed treatment effect of the selected subgroup that might be used in the decision-making of subgroup pursuit is provided. Two earlier studies are reviewed to demonstrate what can be learned from the proposed risk index. © 2023 EcoSta Econometrics and Statistics

关键词： Bias-correction Bootstrap Decision risk Misspecified proportional hazard model Model-free Subgroup analysis

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Possible hadronic origin of TeV photon emission from SNR G106.3+2.7

arXiv

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arXiv 2021年

作者： Yang, Chuyuan Zeng, Houdun Bao, Biwen Zhang, Li Yunnan Observatories Chinese Academy of Sciences Kunming650011 China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Nanjing210034 China Department of Astronomy Key Laboratory of Astroparticle Physics of Yunnan Province Yunnan University Kunming650091 China Key Laboratory of Statistical Modeling and Data Analysis of Yunnan Province Yunnan University Kunming650091 China

Context. Recently, HAWC, ASγ, and LHAASO experiments have reported the gamma-ray spectrum of supernova remnant (SNR) G106.3+2.7 above 40 TeV, indicating that SNR G106.3+2.7 is a promising PeVatron candidate. However, the origin of the gamma-ray spectrum is still debated. Thus, a dedicated theoretical model with self-consistent descriptions is required to decipher the properties of the gamma-ray spectrum for this specific source. Aims. We construct a theoretical model to explain the multiband photon emission from the PeVatron SNR G106.3+2.7. Methods. In our model, the acceleration and propagation of particles from the Bohm-like diffusion region inside the SNR to the Galactic diffusion region outside the SNR are described through nonlinear diffusive shock acceleration (NLDSA). The main content of our NLDSA model is solving the hydrodynamic equations numerically for gas density, gas velocity, and gas pressure and the equation for the quasi-isotropic particle momentum distribution. The consequent multiband nonthermal emission stems from two different regions, namely the acceleration region and the escaping region. Results. Our model is capable of explaining the multiband photon emission via the dominant synchrotron radiation of the electrons accelerated inside the SNR. The photons with energy of GeV are naturally produced by the protons inside and outside the SNR. Moreover, photons in the energy range of ∼ 1- ∼ 100 TeV are due to the interaction of escaped protons with dense molecular clouds. Conclusions. For photons with energy Eγ 1 GeV from SNR G106.3+2.7, our results here favor a hadronic origin, where the photons in the energy range of ∼ 1 GeV to ∼ 1 TeV are produced inside the SNR through proton-proton interaction, while photons with Eγ 1 TeV originate from the interaction of escaped protons with a dense molecular cloud. Copyright © 2021, The Authors. All rights reserved.

关键词： Ultraviolet spectroscopy

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Adjusted Empirical Likelihood Estimation of Distribution Function and Quantile with Nonignorable Missing data

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Journal of Systems Science & Complexity 2018年第3期31卷 820-840页

作者： DING Xianwen TANG Niansheng Key Laboratory of Statistical Modeling & Data Analysis of Yunnan Province Yunnan University Kunming 650500 China Department of Mathematics Jiangsu University of Technology Changzhou 213001 China

This paper considers the estimation problem of distribution functions and quantiles with nonignorable missing response data. Three approaches are developed to estimate distribution functions and quantiles, i.e., the Horvtiz-Thompson-type method, regression imputation method and augmented inverse probability weighted approach. The propensity score is specified by a semiparametric expo- nential tilting model. To estimate the tilting parameter in the propensity score, the authors propose an adjusted empirical likelihood method to deal with the over-identified system. Under some regular conditions, the authors investigate the asymptotic properties of the proposed three estimators for distri- bution functions and quantiles, and find that these estimators have the same asymptotic variance. The jackknife method is employed to consistently estimate the asymptotic variances. Simulation studies are conducted to investigate the finite sample performance of the proposed methodologies.

关键词： Adjusted empirical likelihood distribution estimation exponential tilting model nonig-norable missing data quantile.

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Consistency of the MLE under a two-parameter gamma mixture model with a structural shape parameter

arXiv

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arXiv 2020年

作者： He, Mingxing Chen, Jiahua Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University Kunming650091 China Research Institute of Big Data Yunnan University Kunming650221 China Department of Statistics University of British Columbia VancouverV7C 5K5 Canada

The finite Gamma mixture model is often used to describe randomness in income data, insurance data, and data from other applications. The popular likelihood approach, however, does not work for this model because the likelihood function is unbounded, and the maximum likelihood estimator is therefore not well defined. There has been much research into ways to ensure the consistent estimation of the mixing distribution, including placing an upper bound on the shape parameter or adding a penalty to the log-likelihood function. In this paper, we show that if the shape parameter in the finite Gamma mixture model is structural, then the maximum likelihood estimator of the mixing distribution is well defined and strongly consistent. We also present simulation results demonstrating the consistency of the estimator. We illustrate the application of the model with a structural scale parameter to household income data. The fitted mixture distribution leads to several possible subpopulation structures in terms of the level of disposable income. Copyright © 2020, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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Rejoinder: statistical inference for non-ignorable missing-data problems: a selective review

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statistical Theory and Related Fields 2018年第2期2卷 146-149页

作者： Niansheng Tang Yuanyuan Ju Key Lab of Statistical Modeling and Data Analysis of Yunnan Province Yunnan UniversityKunming 650091People’s Republic of China

We thank five discussants for their thoughtful *** have made significant contributions to the general theme raised in our *** will try our best to answer each of points that five discussers have made.

关键词： thank raised missing

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EDGE DIFFERENTIALLY PRIVATE ESTIMATION IN THE β-MODEL VIA JITTERING AND METHOD OF MOMENTS

arXiv

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arXiv 2021年

作者： Chang, Jinyuan Hu, Qiao Kolaczyk, Eric D. Yao, Qiwei Yi, Fengting Joint Laboratory of Data Science and Business Intelligence Southwestern University of Finance and Economics Chengdu611130 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Mathematics and Statistics McGill University MontrealQCH3A 0B8 Canada Department of Statistics London School of Economics and Political Science LondonWC2A 2AE United Kingdom Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University Kunming650500 China

A standing challenge in data privacy is the trade-off between the level of privacy and the efficiency of statistical inference. Here we conduct an in-depth study of this trade-off for parameter estimation in the β-model (Chatterjee, Diaconis and Sly, 2011) for edge differentially private network data released via jittering (Karwa, Krivitsky and Slavković, 2017). Unlike most previous approaches based on maximum likelihood estimation for this network model, we proceed via method-of-moments. This choice facilitates our exploration of a substantially broader range of privacy levels – corresponding to stricter privacy – than has been to date. Over this new range we discover our proposed estimator for the parameters exhibits an interesting phase transition, with both its convergence rate and asymptotic variance following one of three different regimes of behavior depending on the level of privacy. Because identification of the operable regime is difficult if not impossible in practice, we devise a novel adaptive bootstrap procedure to construct uniform inference across different phases. In fact, leveraging this bootstrap we are able to provide for simultaneous inference of all parameters in the β-model (i.e., equal to the number of nodes), which, to our best knowledge, is the first result of its kind. Numerical experiments confirm the competitive and reliable finite sample performance of the proposed inference methods, next to a comparable maximum likelihood method, as well as significant advantages in terms of computational speed and memory. Copyright © 2021, The Authors. All rights reserved.

关键词： Method of moments

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Correction to “Information Geometric Approach to Multisensor Estimation Fusion”

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IEEE Transactions on Signal Processing 2021年 69卷 4556-4556页

作者： Yao Rong Mengjiao Tang Xiangbing Chen Jie Zhou Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University Kunming Yunnan China Sichuan University Jinjiang College Pengshan Sichuan China College of Mathematics Sichuan University Chengdu Sichuan China

In this correspondence, we make a few corrections to the geodesic projection method of estimation fusion presented in [M. Tang, Y. Rong, J. Zhou, and X. R. Li, IEEE TSP, vol. 67, no. 2, pp. 279-292, 2019].

关键词： Linear programming Estimation Mathematical model Manifolds data models data analysis Analytical models

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statistical inference for nonignorable missing-data problems:a selective review

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statistical Theory and Related Fields 2018年第2期2卷 105-133页

作者： Niansheng Tang Yuanyuan Ju Key Lab of Statistical Modelling and Data Analysis of Yunnan Province Yunnan UniversityKunming650091People’s Republic of China

Nonignorable missing data are frequently encountered in various settings, such as economics,sociology and biomedicine. We review statistical inference for nonignorable missing-data problems, including estimation, influence analysis and model selection. For estimation of meanfunctionals, we review semiparametric method and empirical likelihood (EL) approach. For estimation of parameters in exponential family nonlinear structural equation models, we introduceexpectation-maximisation algorithm, Bayesian approach, and Bayesian EL method. For influenceanalysis, we investigate the case-deletion method and local influence analysis method fromthe frequentist and Bayesian viewpoints. For model selection, we present the modified Akaikeinformation criterion and penalised method.

关键词： Bayesian method empirical likelihood method expectation-maximisation algorithm local influence analysis missing data model selection

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Worldwide Soundscapes: A Synthesis of Passive Acoustic Monitoring Across Realms

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Global Ecology and Biogeography 2025年第5期34卷

作者： Kevin F. A. Darras Rodney A. Rountree Steven L. Van Wilgenburg Anna F. Cord Frederik Pitz Youfang Chen Lijun Dong Agnès Rocquencourt Camille Desjonquères Patrick Mauritz Diaz Tzu-Hao Lin Théophile Turco Louise Emmerson Tom Bradfer-Lawrence Amandine Gasc Sarah Marley Marcus Salton Laura Schillé Paul J. Wensveen Shih-Hung Wu Adriana C. Acero-Murcia Orlando Acevedo-Charry Matyáš Adam Jacopo Aguzzi Irmak Akoglu M. Clara P. Amorim Mina Anders Michel André Alexandre Antonelli Leandro Aparecido Do Nascimento Giulliana Appel Stephanie Archer Christos Astaras Andrey Atemasov Jamieson Atkinson Joël Attia Emanuel Baltag Luc Barbaro Fritjof Basan Carly Batist Julio Ernesto Baumgarten Just T. Bayle Sempere Kristen Bellisario Asaf Ben David Oded Berger-Tal Frédéric Bertucci Matthew G. Betts Iqbal S. Bhalla Thiago Bicudo Marta Bolgan Sara Bombaci Gerard Bota Martin Boullhesen Robert A. Briers Susannah Buchan Michal Budka Katie Burchard Giuseppa Buscaino Alice Calvente Marconi Campos-Cerqueira Maria Isabel Carvalho Gonçalves Maria Ceraulo Maite Cerezo-Araujo Gunnar Cerwén Adams A. Chaskda Maria Chistopolova Christopher W. Clark Kieran D. Cox Benjamin Cretois Chapin Czarnecki Luis P. da Silva Wigna da Silva Laurence H. De Clippele David de la Haye Ana Silvia de Oliveira Tissiani Devin de Zwaan M. Eugenia Degano Jessica Deichmann Joaquin del Rio Christian Devenish Ricardo Díaz-Delgado Pedro Diniz Dorgival Diógenes Oliveira-Júnior Thiago Dorigo Saskia Dröge Marina Duarte Adam Duarte Kerry Dunleavy Robert Dziak Simon Elise Hiroto Enari Haruka S. Enari Florence Erbs Britas Klemens Eriksson Pınar Ertör-Akyazi Nina C. Ferrari Luane Ferreira Abram B. Fleishman Paulo J. Fonseca Bárbara Freitas Nicholas R. Friedman Jérémy S. P. Froidevaux Svetlana Gogoleva Carolina Gonzaga José Miguel González Correa Eben Goodale Benjamin Gottesman Ingo Grass Jack Greenhalgh Jocelyn Gregoire Samuel Haché Jonas Hagge William Halliday Antonia Hammer Tara Hanf-Dressler Sylvain Haupert Samara Haver Becky Heath Daniel Hending Jose Hernandez-Blanco Dennis Higgs EFNO ECODIV INRAE Domaine des Barres Nogent-sur-Vernisson France Sustainable Agricultural Systems & Engineering Lab School of Engineering Westlake University Hangzhou Zhejiang China Chair of Computational Landscape Ecology Faculty of Environmental Sciences Dresden University of Technology Dresden Germany Biology Department University of Victoria Victoria British Columbia Canada The Fish Listener Waguoit Massachusetts USA Terrestrial Unit Prairie Region Canadian Wildlife Service Environment & Climate Change Canada Prairie & Northern Wildlife Research Centre Saskatoon Saskatchewan Canada Agro-ecological Modeling Group Faculty of Agriculture University of Bonn Bonn Germany Marine Mammal and Marine Bioacoustics Laboratory Department of Deep Sea Science Institute of Deep-sea Science and Engineering Chinese Academy of Sciences Sanya Hainan China Institut de Systématique Évolution Biodiversité Muséum National d’Histoire Naturelle Paris France Université Grenoble Alpes Université Savoie Mont Blanc CNRS LECA Grenoble France Climate Change and Biodiversity Hanns R. Neumann Stiftung Indonesia Muaradua Indonesia Biodiversity Research Center Academia Sinica Nankang Taipei Taiwan ENES Bioacoustics Research Laboratory University of Saint-Etienne CRNL CNRS UMR 5292 Inserm UMR_S 1028 Saint-Etienne France Australian Antarctic Division Science Branch Department of Climate Change Energy Environment and Water Channel Highway Kingston Tasmania Australia Biological and Environmental Sciences University of Stirling Stirling Scotland Centre for Conservation Science RSPB Edinburgh UK IMBE Aix Marseille Univ Avignon Univ CNRS IRD Aix-en-Provence France Scotland's Rural College Craibstone Estate Aberdeen UK BIOGECO INRAE University of Bordeaux Cestas France Westman Islands Research Centre University of Iceland Vestmannaeyjar Iceland Qigu Research Center Taiwan Biodiversity Research Institute Nantou County Taiwan Programa de Pós-graduação em Ecologia

Aim The urgency for remote, reliable and scalable biodiversity monitoring amidst mounting human pressures on ecosystems has sparked worldwide interest in Passive Acoustic Monitoring (PAM), which can track life underwater and on land. However, we lack a unified methodology to report this sampling effort and a comprehensive overview of PAM coverage to gauge its potential as a global research and monitoring tool. To address this gap, we created the Worldwide Soundscapes project, a collaborative network and growing database comprising metadata from 416 datasets across all realms (terrestrial, marine, freshwater and subterranean). Location Worldwide, 12,343 sites, all ecosystem types. Time Period 1991 to present. Major Taxa Studied All soniferous taxa. Methods We synthesise sampling coverage across spatial, temporal and ecological scales using metadata describing sampling locations, deployment schedules, focal taxa and audio recording parameters. We explore global trends in biological, anthropogenic and geophysical sounds based on 168 selected recordings from 12 ecosystems across all realms. Results Terrestrial sampling is spatially denser (46 sites per million square kilometre—Mkm 2 ) than aquatic sampling (0.3 and 1.8 sites/Mkm 2 in oceans and fresh water) with only two subterranean datasets. Although diel and lunar cycles are well sampled across realms, only marine datasets (55%) comprehensively sample all seasons. Across the 12 ecosystems selected for exploring global acoustic trends, biological sounds showed contrasting diel patterns across ecosystems, declined with distance from the Equator, and were negatively correlated with anthropogenic sounds. Main Conclusions PAM can inform macroecological studies as well as global conservation and phenology syntheses, but representation can be improved by expanding terrestrial taxonomic scope, sampling coverage in the high seas and subterranean ecosystems, and spatio-temporal replication in freshwater habitats. Overall, this

关键词： ARU automated sound recorder biodiversity conservation biology ecoacoustics IUCN GET realm Passive Acoustic Monitoring phenology soundscape ecology

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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