Econometrics and Statistics (Ecosta 2018)
Total Page：16
File Type：pdf, Size：1020Kb
Load more
Recommended publications
-
Mixture Models with Grouping Structure: Retail Analytics Applications Haidar Almohri Wayne State University
Wayne State University Wayne State University Dissertations 1-1-2018 Mixture Models With Grouping Structure: Retail Analytics Applications Haidar Almohri Wayne State University, Follow this and additional works at: https://digitalcommons.wayne.edu/oa_dissertations Part of the Business Administration, Management, and Operations Commons, Engineering Commons, and the Statistics and Probability Commons Recommended Citation Almohri, Haidar, "Mixture Models With Grouping Structure: Retail Analytics Applications" (2018). Wayne State University Dissertations. 1911. https://digitalcommons.wayne.edu/oa_dissertations/1911 This Open Access Dissertation is brought to you for free and open access by DigitalCommons@WayneState. It has been accepted for inclusion in Wayne State University Dissertations by an authorized administrator of DigitalCommons@WayneState. MIXTURE MODELS WITH GROUPING STRUCTURE: RETAIL ANALYTICS APLICATIONS by HAIDAR ALMOHRI DISSERTATION Submitted to the Graduate School of Wayne State University, Detroit, Michigan in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY 2018 MAJOR: INDUSTRIAL ENGINEERING Approved By: Advisor Date © COPYRIGHT BY HAIDAR ALMOHRI 2018 All Rights Reserved DEDICATION To my lovely daughter Nour. ii ACKNOWLEDGMENTS I would like to thank my advisor Dr. Ratna Babu Chinnam for introducing me to this fascinating research topic and for his guidance, technical advise, and support throughout this research. I am also grateful to my committee members, Dr. Alper Murat, Dr. Evrim Dalkiran for their time and interest in this thesis and for providing helpful comments for my research. Special thanks go to Dr. Arash Ali Amini for providing valuable suggestions to improve this work. I also thank my family and friends for their continuous support. iii TABLE OF CONTENTS DEDICATION . -
Methods to Calculate Uncertainty in the Estimated Overall Effect Size from a Random-Effects Meta-Analysis
Methods to calculate uncertainty in the estimated overall effect size from a random-effects meta-analysis Areti Angeliki Veroniki1,2*; Email: [email protected] Dan Jackson3; Email: [email protected] Ralf Bender4; Email: [email protected] Oliver Kuss5,6; Email: [email protected] Dean Langan7; Email: [email protected] Julian PT Higgins8; Email: [email protected] Guido Knapp9; Email: [email protected] Georgia Salanti10; Email: [email protected] 1 Li Ka Shing Knowledge Institute, St. Michael’s Hospital, 209 Victoria Street, East Building. Toronto, Ontario, M5B 1T8, Canada 2 Department of Primary Education, School of Education, University of Ioannina, Ioannina, Greece 3 MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 0SR, U.K | downloaded: 28.9.2021 4 Department of Medical Biometry, Institute for Quality and Efficiency in Health Care (IQWiG), Im Mediapark 8, 50670 Cologne, Germany 5 Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Institute for Diabetes Research at Heinrich Heine University, 40225 Düsseldorf, Germany 6 Institute of Medical Statistics, Heinrich-Heine-University, Medical Faculty, Düsseldorf, Germany This article has been accepted for publication and undergone full peer review but has not https://doi.org/10.7892/boris.119524 been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/jrsm.1319 source: This article is protected by copyright. All rights reserved. 7 Institute of Child Health, UCL, London, WC1E 6BT, UK 8 Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, U.K. -
Mini-Batch Learning of Exponential Family Finite Mixture Models Hien D Nguyen, Florence Forbes, Geoffrey Mclachlan
Mini-batch learning of exponential family finite mixture models Hien D Nguyen, Florence Forbes, Geoffrey Mclachlan To cite this version: Hien D Nguyen, Florence Forbes, Geoffrey Mclachlan. Mini-batch learning of exponential family finite mixture models. Statistics and Computing, Springer Verlag (Germany), 2020, 30, pp.731-748. 10.1007/s11222-019-09919-4. hal-02415068v2 HAL Id: hal-02415068 https://hal.archives-ouvertes.fr/hal-02415068v2 Submitted on 26 Mar 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Mini-batch learning of exponential family finite mixture models Hien D. Nguyen1∗, Florence Forbes2, and Geoffrey J. McLachlan3 September 6, 2019 1Department of Mathematics and Statistics, La Trobe University, Melbourne, Victoria, Australia. 2Univ. Grenoble Alpes, Inria, CNRS, Grenoble INPy, LJK, 38000 Grenoble, France. yInstitute of Engineering Univ. Grenoble Alpes. 3School of Mathematics and Physics, University of Queensland, St. Lucia, Brisbane, Australia. ∗Corresponding author: Hien Nguyen (Email: [email protected]). Abstract Mini-batch algorithms have become increasingly popular due to the requirement for solving optimization problems, based on large-scale data sets. Using an existing online expectation-- maximization (EM) algorithm framework, we demonstrate how mini-batch (MB) algorithms may be constructed, and propose a scheme for the stochastic stabilization of the constructed mini-batch algorithms. -
Principles of Statistical Inference
Principles of Statistical Inference In this important book, D. R. Cox develops the key concepts of the theory of statistical inference, in particular describing and comparing the main ideas and controversies over foundational issues that have rumbled on for more than 200 years. Continuing a 60-year career of contribution to statistical thought, Professor Cox is ideally placed to give the comprehensive, balanced account of the field that is now needed. The careful comparison of frequentist and Bayesian approaches to inference allows readers to form their own opinion of the advantages and disadvantages. Two appendices give a brief historical overview and the author’s more personal assessment of the merits of different ideas. The content ranges from the traditional to the contemporary. While specific applications are not treated, the book is strongly motivated by applications across the sciences and associated technologies. The underlying mathematics is kept as elementary as feasible, though some previous knowledge of statistics is assumed. This book is for every serious user or student of statistics – in particular, for anyone wanting to understand the uncertainty inherent in conclusions from statistical analyses. Principles of Statistical Inference D.R. COX Nuffield College, Oxford CAMBRIDGE UNIVERSITY PRESS Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore, São Paulo Cambridge University Press The Edinburgh Building, Cambridge CB2 8RU, UK Published in the United States of America by Cambridge University Press, New York www.cambridge.org Information on this title: www.cambridge.org/9780521866736 © D. R. Cox 2006 This publication is in copyright. Subject to statutory exception and to the provision of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press. -
Confidence Distribution
Confidence Distribution Xie and Singh (2013): Confidence distribution, the frequentist distribution estimator of a parameter: A Review Céline Cunen, 15/09/2014 Outline of Article ● Introduction ● The concept of Confidence Distribution (CD) ● A classical Definition and the History of the CD Concept ● A modern definition and interpretation ● Illustrative examples ● Basic parametric examples ● Significant (p-value) functions ● Bootstrap distributions ● Likelihood functions ● Asymptotically third-order accurate confidence distributions ● CD, Bootstrap, Fiducial and Bayesian approaches ● CD-random variable, Bootstrap estimator and fiducial-less interpretation ● CD, fiducial distribution and Belief function ● CD and Bayesian inference ● Inferences using a CD ● Confidence Interval ● Point estimation ● Hypothesis testing ● Optimality (comparison) of CDs ● Combining CDs from independent sources ● Combination of CDs and a unified framework for Meta-Analysis ● Incorporation of Expert opinions in clinical trials ● CD-based new methodologies, examples and applications ● CD-based likelihood caluculations ● Confidence curve ● CD-based simulation methods ● Additional examples and applications of CD-developments ● Summary CD: a sample-dependent distribution that can represent confidence intervals of all levels for a parameter of interest ● CD: a broad concept = covers all approaches that can build confidence intervals at all levels Interpretation ● A distribution on the parameter space ● A Distribution estimator = contains information for many types of -
An Introduction to MM Algorithms for Machine Learning and Statistical
An Introduction to MM Algorithms for Machine Learning and Statistical Estimation Hien D. Nguyen November 12, 2016 School of Mathematics and Physics, University of Queensland, St. Lucia. Centre for Advanced Imaging, University of Queensland, St. Lucia. Abstract MM (majorization–minimization) algorithms are an increasingly pop- ular tool for solving optimization problems in machine learning and sta- tistical estimation. This article introduces the MM algorithm framework in general and via three popular example applications: Gaussian mix- ture regressions, multinomial logistic regressions, and support vector ma- chines. Specific algorithms for the three examples are derived and numer- ical demonstrations are presented. Theoretical and practical aspects of MM algorithm design are discussed. arXiv:1611.03969v1 [stat.CO] 12 Nov 2016 1 Introduction Let X X Rp and Y Y Rq be random variables, which we shall refer to ∈ ⊂ ∈ ⊂ as the input and target variables, respectively. We shall denote a sample of n in- n dependent and identically distributed (IID) pairs of variables D = (Xi, Yi) { }i=1 1 ¯ n as the data, and D = (xi, yi) as an observed realization of the data. Under { }i=1 the empirical risk minimization (ERM) framework of Vapnik (1998, Ch. 1) or the extremum estimation (EE) framework of Amemiya (1985, Ch. 4), a large number of machine learning and statistical estimation problems can be phrased as the computation of min θ; D¯ or θˆ = arg min θ; D¯ , (1) θ Θ R θ Θ R ∈ ∈ where θ; D¯ is a risk function defined over the observed data D¯ and is de- R pendent on some parameter θ Θ. -
The P-Value Function and Statistical Inference
The American Statistician ISSN: 0003-1305 (Print) 1537-2731 (Online) Journal homepage: https://www.tandfonline.com/loi/utas20 The p-value Function and Statistical Inference D. A. S. Fraser To cite this article: D. A. S. Fraser (2019) The p-value Function and Statistical Inference, The American Statistician, 73:sup1, 135-147, DOI: 10.1080/00031305.2018.1556735 To link to this article: https://doi.org/10.1080/00031305.2018.1556735 © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Published online: 20 Mar 2019. Submit your article to this journal Article views: 1983 View Crossmark data Citing articles: 1 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=utas20 THE AMERICAN STATISTICIAN 2019, VOL. 73, NO. S1, 135–147: Statistical Inference in the 21st Century https://doi.org/10.1080/00031305.2019.1556735 The p-value Function and Statistical Inference D. A. S. Fraser Department of Statistical Sciences, University of Toronto, Toronto, Canada ABSTRACT ARTICLE HISTORY This article has two objectives. The first and narrower is to formalize the p-value function, which records Received March 2018 all possible p-values, each corresponding to a value for whatever the scalar parameter of interest is for the Revised November 2018 problem at hand, and to show how this p-value function directly provides full inference information for any corresponding user or scientist. The p-value function provides familiar inference objects: significance KEYWORDS Accept–Reject; Ancillarity; levels, confidence intervals, critical values for fixed-level tests, and the power function at all values ofthe Box–Cox; Conditioning; parameter of interest. -
Discussions on Professor Fraser's Article on “Is Bayes Posterior Just Quick and Dirty Confidence?”
Discussions on Professor Fraser’s article on “Is Bayes posterior just quick and dirty confidence?” Kesar Singh and Minge Xie Rutgers University We congratulate Professor Fraser for this very engaging article. It gives us an opportunity to gaze at the past and future of Bayes and confidence. It is well known that a Bayes posterior can only provide credible intervals and has no assurance of frequentist coverage (known as confidence). Professor Fraser’s article provides a detailed and insightful exploration into the root of this issue. It turns out that the Bayes posterior is exactly a confidence in the linear case (a mathematics coincidence), and Professor Fraser’s insightful and far-reaching examples demonstrate how the departure from linearity induces the departure of a posterior, in a proportionate way, from being a confidence. Of course, Bayesian inference is not bounded by frequestist criteria or geared to provide confidence statements, even though in some applications researchers have treated the Bayes credible intervals as confidence intervals on asymptotic grounds. It is debatable whether this departure of Bayesian inference from confidence should be a concern or not. But, nevertheless, the article provides us a powerful exploration and demonstration which can help us better comprehend the two statistical philosophies and the 250-year debate between Bayesians and frequentists. In the midst of the 250-year debate, Fisher’s “fiducial distribution” played a prominent role, which however is now referred to as the “biggest blunder” of the father of modern statistical inference [1]. Both developments of the confidence distribution and Fisher’s fiducial distribution share the common goal of providing distribution estimation for parameters without using priors, and their performances are often judged by the (asymptotic or exact) probability coverage of their corresponding intervals. -
Swedish Translation for the ISI Multilingual Glossary of Statistical Terms, Prepared by Jan Enger, Bernhard Huitfeldt, Ulf Jorner, and Jan Wretman
Swedish translation for the ISI Multilingual Glossary of Statistical Terms, prepared by Jan Enger, Bernhard Huitfeldt, Ulf Jorner, and Jan Wretman. Finally revised version, January 2008. For principles, see the appendix. -
Mixture Model Clustering in the Analysis of Complex Diseases
CORE Metadata, citation and similar papers at core.ac.uk Provided by Helsingin yliopiston digitaalinen arkisto Department of Computer Science Series of Publications A Report A-2012-2 Mixture Model Clustering in the Analysis of Complex Diseases Jaana Wessman To be presented, with the permission of the Faculty of Science of the University of Helsinki, for public criticism in Auditorium XIV, University of Helsinki Main Building, on 13 April 2012 at noon. University of Helsinki Finland Supervisor Heikki Mannila, Department of Information and Computer Science and Helsinki Institute of Information Technology, Aalto University, Finland and Leena Peltonen (died 11th March 2010), University of Helsinki and National Public Health Institute, Finland Pre-examiners Sampsa Hautaniemi, Docent, Institute of Biomedicine, University of Helsinki, Finland Martti Juhola, Professor, Department of Computer Science, University of Tampere, Finland Opponent Tapio Elomaa, Professor, Department of Software Systems, Tampere University of Technology, Finland Custos Hannu Toivonen, Professor, Department of Computer Science, University of Helsinki, Finland Contact information Department of Computer Science P.O. Box 68 (Gustaf Hällströmin katu 2b) FI-00014 University of Helsinki Finland Email address: [email protected].fi URL: http://www.cs.Helsinki.fi/ Telephone: +358 9 1911, telefax: +358 9 191 51120 Copyright © 2012 Jaana Wessman ISSN 1238-8645 ISBN 978-952-10-7897-2 (paperback) ISBN 978-952-10-7898-9 (PDF) Computing Reviews (1998) Classification: I.5.3, J.2 Helsinki 2012 Unigrafia Mixture Model Clustering in the Analysis of Complex Diseases Jaana Wessman Department of Computer Science P.O. Box 68, FI-00014 University of Helsinki, Finland Jaana.Wessman@iki.fi PhD Thesis, Series of Publications A, Report A-2012-2 Helsinki, Mar 2012, 119+11 pages ISSN 1238-8645 ISBN 978-952-10-7897-2 (paperback) ISBN 978-952-10-7898-9 (PDF) Abstract The topic of this thesis is the analysis of complex diseases, and specifically the use of k-means and mixture modeling based clustering methods to do it. -
Game-Theoretic Safety Assurance for Human-Centered Robotic Systems by Jaime Fernández Fisac a Dissertation Submitted in Partial
Game-Theoretic Safety Assurance for Human-Centered Robotic Systems by Jaime Fern´andezFisac A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Engineering - Electrical Engineering and Computer Sciences in the Graduate Division of the University of California, Berkeley Committee in charge: Professor S. Shankar Sastry, Co-chair Professor Claire J. Tomlin, Co-chair Professor Anca D. Dragan Professor Thomas L. Griffiths Professor Ruzena Bajcsy Fall 2019 Game-Theoretic Safety Assurance for Human-Centered Robotic Systems Copyright 2019 by Jaime Fern´andezFisac 1 Abstract Game-Theoretic Safety Assurance for Human-Centered Robotic Systems by Jaime Fern´andezFisac Doctor of Philosophy in Engineering - Electrical Engineering and Computer Sciences University of California, Berkeley Professor S. Shankar Sastry, Co-chair Professor Claire J. Tomlin, Co-chair In order for autonomous systems like robots, drones, and self-driving cars to be reliably intro- duced into our society, they must have the ability to actively account for safety during their operation. While safety analysis has traditionally been conducted offline for controlled envi- ronments like cages on factory floors, the much higher complexity of open, human-populated spaces like our homes, cities, and roads makes it unviable to rely on common design-time assumptions, since these may be violated once the system is deployed. Instead, the next generation of robotic technologies will need to reason about safety online, constructing high- confidence assurances informed by ongoing observations of the environment and other agents, in spite of models of them being necessarily fallible. This dissertation aims to lay down the necessary foundations to enable autonomous systems to ensure their own safety in complex, changing, and uncertain environments, by explicitly reasoning about the gap between their models and the real world. -
Sponsored by the International Biometric Society
Sponsored by the International Biometric Society The International Biometric Society is devoted to the development and application of statistical and mathematical theory and methods in the biosiciences. The Conference is grateful for the support of the following organisations: The Federation of Pharmaceutical Manufacturers' Associations of JAPAN Organised by the Biometric Society of Japan, Japanese Region of the International Biometric Society Contents List of Sponsors Inside Front Cover Welcome from Presidents Page 2 Welcome from Chairs Page 3 Programme at a Glance Page 4 - 6 Opening Session Page 7 IBC2012 Governance Meetings Schedule Page 8 Organising Committees Page 9 Awards at IBC Kobe 2012 Page 10 General Information Page 11 Venue Information Page 12 Presentation Instruction Page 13 Social Programme Page 15 Mid-Conference Tours Page 16 Scientifi c Programme Page 21 Satellite Symposium Page 46 Map of KICC Page 60 Access Map Page 61 Floorplan Page 62 1 XXVIth International Biometric Conference Message from the IBS President and the IBC 2012 Organising President It is with great pleasure that we welcome all delegates, their families and friends to the XXVIth International Biometric Conference (IBC 2012) being hosted by the Japanese Region in Kobe. It is nearly thirty years since they hosted the one previous IBC held in Japan in Tokyo in 1984. Toshiro Tango and his Local Organising Committee (LOC) have done a wonderful job in providing a socially and scientifi cally inviting program at an outstanding venue. The Kobe International Convention Centre provides excellent spaces for formal meetings/scientifi c sessions and informal conversations/ networking activities which are an essential part of every IBC.