Celebrating Statistics : Papers in Honour of Sir David Cox on The
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Royal Statistical Scandal
Royal Statistical Scandal False and misleading claims by the Royal Statistical Society Including on human poverty and UN global goals Documentary evidence Matt Berkley Draft 27 June 2019 1 "The Code also requires us to be competent. ... We must also know our limits and not go beyond what we know.... John Pullinger RSS President" https://www.statslife.org.uk/news/3338-rss-publishes-revised-code-of- conduct "If the Royal Statistical Society cannot provide reasonable evidence on inflation faced by poor people, changing needs, assets or debts from 2008 to 2018, I propose that it retract the honour and that the President makes a statement while he holds office." Matt Berkley 27 Dec 2018 2 "a recent World Bank study showed that nearly half of low-and middle- income countries had insufficient data to monitor poverty rates (2002- 2011)." Royal Statistical Society news item 2015 1 "Max Roser from Oxford points out that newspapers could have legitimately run the headline ' Number of people in extreme poverty fell by 137,000 since yesterday' every single day for the past 25 years... Careless statistical reporting could cost lives." President of the Royal Statistical Society Lecture to the Independent Press Standards Organisation April 2018 2 1 https://www.statslife.org.uk/news/2495-global-partnership-for- sustainable-development-data-launches-at-un-summit 2 https://www.statslife.org.uk/features/3790-risk-statistics-and-the-media 3 "Mistaken or malicious misinformation can change your world... When the government is wrong about you it will hurt you too but you may never know how. -
Two Principles of Evidence and Their Implications for the Philosophy of Scientific Method
TWO PRINCIPLES OF EVIDENCE AND THEIR IMPLICATIONS FOR THE PHILOSOPHY OF SCIENTIFIC METHOD by Gregory Stephen Gandenberger BA, Philosophy, Washington University in St. Louis, 2009 MA, Statistics, University of Pittsburgh, 2014 Submitted to the Graduate Faculty of the Kenneth P. Dietrich School of Arts and Sciences in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Pittsburgh 2015 UNIVERSITY OF PITTSBURGH KENNETH P. DIETRICH SCHOOL OF ARTS AND SCIENCES This dissertation was presented by Gregory Stephen Gandenberger It was defended on April 14, 2015 and approved by Edouard Machery, Pittsburgh, Dietrich School of Arts and Sciences Satish Iyengar, Pittsburgh, Dietrich School of Arts and Sciences John Norton, Pittsburgh, Dietrich School of Arts and Sciences Teddy Seidenfeld, Carnegie Mellon University, Dietrich College of Humanities & Social Sciences James Woodward, Pittsburgh, Dietrich School of Arts and Sciences Dissertation Director: Edouard Machery, Pittsburgh, Dietrich School of Arts and Sciences ii Copyright © by Gregory Stephen Gandenberger 2015 iii TWO PRINCIPLES OF EVIDENCE AND THEIR IMPLICATIONS FOR THE PHILOSOPHY OF SCIENTIFIC METHOD Gregory Stephen Gandenberger, PhD University of Pittsburgh, 2015 The notion of evidence is of great importance, but there are substantial disagreements about how it should be understood. One major locus of disagreement is the Likelihood Principle, which says roughly that an observation supports a hypothesis to the extent that the hy- pothesis predicts it. The Likelihood Principle is supported by axiomatic arguments, but the frequentist methods that are most commonly used in science violate it. This dissertation advances debates about the Likelihood Principle, its near-corollary the Law of Likelihood, and related questions about statistical practice. -
JSM 2017 in Baltimore the 2017 Joint Statistical Meetings in Baltimore, Maryland, Which Included the CONTENTS IMS Annual Meeting, Took Place from July 29 to August 3
Volume 46 • Issue 6 IMS Bulletin September 2017 JSM 2017 in Baltimore The 2017 Joint Statistical Meetings in Baltimore, Maryland, which included the CONTENTS IMS Annual Meeting, took place from July 29 to August 3. There were over 6,000 1 JSM round-up participants from 52 countries, and more than 600 sessions. Among the IMS program highlights were the three Wald Lectures given by Emmanuel Candès, and the Blackwell 2–3 Members’ News: ASA Fellows; ICM speakers; David Allison; Lecture by Martin Wainwright—Xiao-Li Meng writes about how inspirational these Mike Cohen; David Cox lectures (among others) were, on page 10. There were also five Medallion lectures, from Edoardo Airoldi, Emery Brown, Subhashis Ghoshal, Mark Girolami and Judith 4 COPSS Awards winners and nominations Rousseau. Next year’s IMS lectures 6 JSM photos At the IMS Presidential Address and Awards session (you can read Jon Wellner’s 8 Anirban’s Angle: The State of address in the next issue), the IMS lecturers for 2018 were announced. The Wald the World, in a few lines lecturer will be Luc Devroye, the Le Cam lecturer will be Ruth Williams, the Neyman Peter Bühlmann Yuval Peres 10 Obituary: Joseph Hilbe lecture will be given by , and the Schramm lecture by . The Medallion lecturers are: Jean Bertoin, Anthony Davison, Anna De Masi, Svante Student Puzzle Corner; 11 Janson, Davar Khoshnevisan, Thomas Mikosch, Sonia Petrone, Richard Samworth Loève Prize and Ming Yuan. 12 XL-Files: The IMS Style— Next year’s JSM invited sessions Inspirational, Mathematical If you’re feeling inspired by what you heard at JSM, you can help to create the 2018 and Statistical invited program for the meeting in Vancouver (July 28–August 2, 2018). -
Sir Richard Doll, Epidemiologist – a Personal Reminiscence with a Selected Bibliography
British Journal of Cancer (2005) 93, 963 – 966 & 2005 Cancer Research UK All rights reserved 0007 – 0920/05 $30.00 www.bjcancer.com Obituary Sir Richard Doll, epidemiologist – a personal reminiscence with a selected bibliography The death of Richard Doll on 24 July 2005 at the age of 92 after a short illness ended an extraordinarily productive life in science for which he received widespread recognition, including Fellowship of The Royal Society (1966), Knighthood (1971), Companionship of Honour (1996), and many honorary degrees and prizes. He is unique, however, in having seen both universal acceptance of his work demonstrating smoking as the main cause of the most common fatal cancer in the world and the relative success of strategies to reduce the prevalence of the habit. In 1950, 80% of the men in Britain smoked but this has now declined to less than 30%. Richard Doll qualified in medicine at St Thomas’ Hospital in 1937, but his epidemiological career began after service in the Second World War when he worked with Francis Avery Jones at the Central Middlesex Hospital on occupational factors in the aetiology of peptic ulceration. The completeness of Doll’s tracing of previously surveyed men so impressed Tony Bradford Hill that he offered him a post in the MRC Statistical Research Unit to investigate the causes of lung cancer. For the representations of Percy Stocks (Chief Medical Officer to the Registrar General) and Sir Ernest Kennaway had prevailed against the then commonly held view that the marked rise in lung cancer deaths in Britain since 1900 was due only to improved diagnosis. -
You May Be (Stuck) Here! and Here Are Some Potential Reasons Why
You may be (stuck) here! And here are some potential reasons why. As published in Benchmarks RSS Matters, May 2015 http://web3.unt.edu/benchmarks/issues/2015/05/rss-matters Jon Starkweather, PhD 1 Jon Starkweather, PhD [email protected] Consultant Research and Statistical Support http://www.unt.edu http://www.unt.edu/rss RSS hosts a number of “Short Courses”. A list of them is available at: http://www.unt.edu/rss/Instructional.htm Those interested in learning more about R, or how to use it, can find information here: http://www.unt.edu/rss/class/Jon/R_SC 2 You may be (stuck) here! And here are some potential reasons why. I often read R-bloggers (Galili, 2015) to see new and exciting things users are doing in the wonderful world of R. Recently I came across Norm Matloff’s (2014) blog post with the title “Why are we still teaching t-tests?” To be honest, many RSS personnel have echoed Norm’s sentiments over the years. There do seem to be some fields which are perpetually stuck in decades long past — in terms of the statistical methods they teach and use. Reading Norm’s post got me thinking it might be good to offer some explanations, or at least opinions, on why some fields tend to be stubbornly behind the analytic times. This month’s article will offer some of my own thoughts on the matter. I offer these opinions having been academically raised in one such Rip Van Winkle (Washington, 1819) field and subsequently realized how much of what I was taught has very little practical utility with real world research problems and data. -
Access Pdf of Lancet Paper on Global Burden of Disease Here
Articles A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010 Stephen S Lim‡, Theo Vos, Abraham D Flaxman, Goodarz Danaei, Kenji Shibuya, Heather Adair-Rohani*, Markus Amann*, H Ross Anderson*, Kathryn G Andrews*, Martin Aryee*, Charles Atkinson*, Loraine J Bacchus*, Adil N Bahalim*, Kalpana Balakrishnan*, John Balmes*, Suzanne Barker-Collo*, Amanda Baxter*, Michelle L Bell*, Jed D Blore*, Fiona Blyth*, Carissa Bonner*, Guilherme Borges*, Rupert Bourne*, Michel Boussinesq*, Michael Brauer*, Peter Brooks*, Nigel G Bruce*, Bert Brunekreef*, Claire Bryan-Hancock*, Chiara Bucello*, Rachelle Buchbinder*, Fiona Bull*, Richard T Burnett*, Tim E Byers*, Bianca Calabria*, Jonathan Carapetis*, Emily Carnahan*, Zoe Chafe*, Fiona Charlson*, Honglei Chen*, Jian Shen Chen*, Andrew Tai-Ann Cheng*, Jennifer Christine Child*, Aaron Cohen*, K Ellicott Colson*, Benjamin C Cowie*, Sarah Darby*, Susan Darling*, Adrian Davis*, Louisa Degenhardt*, Frank Dentener*, Don C Des Jarlais*, Karen Devries*, Mukesh Dherani*, Eric L Ding*, E Ray Dorsey*, Tim Driscoll*, Karen Edmond*, Suad Eltahir Ali*, Rebecca E Engell*, Patricia J Erwin*, Saman Fahimi*, Gail Falder*, Farshad Farzadfar*, Alize Ferrari*, Mariel M Finucane*, Seth Flaxman*, Francis Gerry R Fowkes*, Greg Freedman*, Michael K Freeman*, Emmanuela Gakidou*, Santu Ghosh*, Edward Giovannucci*, Gerhard Gmel*, Kathryn Graham*, Rebecca Grainger*, Bridget Grant*, -
Statistics Making an Impact
John Pullinger J. R. Statist. Soc. A (2013) 176, Part 4, pp. 819–839 Statistics making an impact John Pullinger House of Commons Library, London, UK [The address of the President, delivered to The Royal Statistical Society on Wednesday, June 26th, 2013] Summary. Statistics provides a special kind of understanding that enables well-informed deci- sions. As citizens and consumers we are faced with an array of choices. Statistics can help us to choose well. Our statistical brains need to be nurtured: we can all learn and practise some simple rules of statistical thinking. To understand how statistics can play a bigger part in our lives today we can draw inspiration from the founders of the Royal Statistical Society. Although in today’s world the information landscape is confused, there is an opportunity for statistics that is there to be seized.This calls for us to celebrate the discipline of statistics, to show confidence in our profession, to use statistics in the public interest and to champion statistical education. The Royal Statistical Society has a vital role to play. Keywords: Chartered Statistician; Citizenship; Economic growth; Evidence; ‘getstats’; Justice; Open data; Public good; The state; Wise choices 1. Introduction Dictionaries trace the source of the word statistics from the Latin ‘status’, the state, to the Italian ‘statista’, one skilled in statecraft, and on to the German ‘Statistik’, the science dealing with data about the condition of a state or community. The Oxford English Dictionary brings ‘statistics’ into English in 1787. Florence Nightingale held that ‘the thoughts and purpose of the Deity are only to be discovered by the statistical study of natural phenomena:::the application of the results of such study [is] the religious duty of man’ (Pearson, 1924). -
Spatio-Temporal Point Processes: Methods and Applications Peter J
Johns Hopkins University, Dept. of Biostatistics Working Papers 6-27-2005 Spatio-temporal Point Processes: Methods and Applications Peter J. Diggle Medical Statistics Unit, Lancaster University, UK & Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, [email protected] Suggested Citation Diggle, Peter J., "Spatio-temporal Point Processes: Methods and Applications" (June 2005). Johns Hopkins University, Dept. of Biostatistics Working Papers. Working Paper 78. http://biostats.bepress.com/jhubiostat/paper78 This working paper is hosted by The Berkeley Electronic Press (bepress) and may not be commercially reproduced without the permission of the copyright holder. Copyright © 2011 by the authors Spatio-temporal Point Processes: Methods and Applications Peter J Diggle (Department of Mathematics and Statistics, Lancaster University and Department of Biostatistics, Johns Hopkins University School of Public Health) June 27, 2005 1 Introduction This chapter is concerned with the analysis of data whose basic format is (xi; ti) : i = 1; :::; n where each xi denotes the location and ti the corresponding time of occurrence of an event of interest. We shall assume that the data form a complete record of all events which occur within a pre-specified spatial region A and a pre-specified time- interval, (0; T ). We call a data-set of this kind a spatio-temporal point pattern, and the underlying stochastic model for the data a spatio-temporal point process. 1.1 Motivating examples 1.1.1 Amacrine cells in the retina of a rabbit One general approach to analysing spatio-temporal point process data is to extend existing methods for purely spatial data by considering the time of occurrence as a distinguishing feature, or mark, attached to each event. -
Piotr Fryzlewicz
Piotr Fryzlewicz [email protected] https://stats.lse.ac.uk/fryzlewicz https://scholar.google.co.uk/citations?user=i846MWEAAAAJ Department of Statistics, London School of Economics Houghton Street London WC2A 2AE UK (no telephone number – please email to set up a Zoom call) 2011– Professor of Statistics, Department of Statistics, London School of Economics, UK. (Deputy Head of Department 2019–20, 2021–22.) 2009–2011 Reader in Statistics, Department of Statistics, London School of Economics, UK. 2005–2009 Lecturer, then part-time Lecturer, then part-time Senior Lecturer in Statistics, Depart- ment of Mathematics, University of Bristol, UK. 2003–2005 Chapman Research Fellow, Department of Mathematics, Imperial College London, UK. 2000–2003 PhD in Statistics, Department of Mathematics, University of Bristol, UK. Thesis title: Wavelet techniques for time series and Poisson data. Adviser: Prof. Guy Nason. 1995–2000 MSci in Mathematics, Faculty of Fundamental Problems of Technology, Wroclaw Uni- versity of Science and Technology, Poland. 1st class degree (with distinction). Associate (unremunerated) academic affiliations 2019– Associate Member, Department of Statistics, University of Oxford, UK. Professional accreditations 2018– Chartered Statistician; the Royal Statistical Society. Non-academic work experience 2008–2009 Researcher (on a 90% part-time basis), Winton Capital Management, London, UK. Awards and honours 2016 Distinguished Alumnus, Wroclaw University of Science and Technology, Poland. 2014–2019 Fellowship; Engineering and Physical Sciences Research Council. 2013 Guy Medal in Bronze; the Royal Statistical Society. 2007–2008 University Research Fellowship; University of Bristol. 2005–2007 Award to Newly Appointed Lecturers in Science, Engineering and Mathematics; Nuffield Foundation. 2000–2003 Overseas Research Student Award; Universities UK. -
Sequential Monte Carlo Methods for Inference and Prediction of Latent Time-Series
SSStttooonnnyyy BBBrrrooooookkk UUUnnniiivvveeerrrsssiiitttyyy The official electronic file of this thesis or dissertation is maintained by the University Libraries on behalf of The Graduate School at Stony Brook University. ©©© AAAllllll RRRiiiggghhhtttsss RRReeessseeerrrvvveeeddd bbbyyy AAAuuuttthhhooorrr... Sequential Monte Carlo Methods for Inference and Prediction of Latent Time-series A Dissertation presented by Iñigo Urteaga to The Graduate School in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Electrical Engineering Stony Brook University August 2016 Stony Brook University The Graduate School Iñigo Urteaga We, the dissertation committee for the above candidate for the Doctor of Philosophy degree, hereby recommend acceptance of this dissertation. Petar M. Djuri´c,Dissertation Advisor Professor, Department of Electrical and Computer Engineering Mónica F. Bugallo, Chairperson of Defense Associate Professor, Department of Electrical and Computer Engineering Yue Zhao Assistant Professor, Department of Electrical and Computer Engineering Svetlozar Rachev Professor, Department of Applied Math and Statistics This dissertation is accepted by the Graduate School. Nancy Goroff Interim Dean of the Graduate School ii Abstract of the Dissertation Sequential Monte Carlo Methods for Inference and Prediction of Latent Time-series by Iñigo Urteaga Doctor of Philosophy in Electrical Engineering Stony Brook University 2016 In the era of information-sensing mobile devices, the Internet- of-Things and Big Data, research on advanced methods for extracting information from data has become extremely critical. One important task in this area of work is the analysis of time-varying phenomena, observed sequentially in time. This endeavor is relevant in many applications, where the goal is to infer the dynamics of events of interest described by the data, as soon as new data-samples are acquired. -
Letter from the RSS Covid-19 Task Force To
Letter from the Royal Statistical Society Covid-19 Task Force to the Guardian regarding data transparency 7 August 2020 The Covid-19 public health crisis has placed a sharp emphasis on the role of data in government decision-making. During the current phase – where the emphasis is on Test and Trace and local lockdowns – data is playing an increasingly central role in informing policy. We recognise that the stakes are high and that decisions need to be made quickly. However, this makes it even more important that data is used in a responsible and effective manner. Transparency around the data that are being used to inform decisions is central to this. Over the past week, there have been two major data-led government announcements where the supporting data were not made available at the time. First, the announcement that home and garden visits would be made illegal in parts of northern England. The Prime Minister cited unpublished data which suggested that these visits were the main setting for transmission. Second, the purchase of two new tests for the virus that claim to deliver results within 90 minutes, without data regarding the tests’ effectiveness being published. We are concerned about the lack of transparency in these two cases – these are important decisions and the data upon which they are based should be publicly available for scrutiny, as Paul Nurse pointed out in this paper at the weekend. Government rhetoric often treats data as a managerial tool for informing decisions. But beyond this, transparency and well sign-posted data builds public trust and encourages compliance: the daily provision of statistical information was an integral part of full lockdown and was both expected and valued by the public. -
Identification of Spikes in Time Series Arxiv:1801.08061V1 [Stat.AP]
Identification of Spikes in Time Series Dana E. Goin1 and Jennifer Ahern1 1 Division of Epidemiology, School of Public Health, University of California, Berkeley, California January 25, 2018 arXiv:1801.08061v1 [stat.AP] 24 Jan 2018 1 Abstract Identification of unexpectedly high values in a time series is useful for epi- demiologists, economists, and other social scientists interested in the effect of an exposure spike on an outcome variable. However, the best method to identify spikes in time series is not known. This paper aims to fill this gap by testing the performance of several spike detection methods in a sim- ulation setting. We created simulations parameterized by monthly violence rates in nine California cities that represented different series features, and randomly inserted spikes into the series. We then compared the ability to detect spikes of the following methods: ARIMA modeling, Kalman filtering and smoothing, wavelet modeling with soft thresholding, and an iterative outlier detection method. We varied the magnitude of spikes from 10-50% of the mean rate over the study period and varied the number of spikes inserted from 1 to 10. We assessed performance of each method using sensitivity and specificity. The Kalman filtering and smoothing procedure had the best over- all performance. We applied Kalman filtering and smoothing to the monthly violence rates in nine California cities and identified spikes in the rate over the 2005-2012 period. 2 List of Tables 1 ARIMA models and parameters by city . 21 2 Average sensitivity of spike identification methods for spikes of magnitudes ranging from 10-50% increase over series mean .