Locfit(Noxäe, Data=Ethanol, Language

Total Page:16

File Type:pdf, Size:1020Kb

Locfit(Noxäe, Data=Ethanol, Language A joint newsletter of the Statistical Computing & Statistical Graphics Sections of the American Statistical Association. April 97 Vol.8 No.1 A WORD FROM OUR CHAIRS TOPICS IN MEDICAL IMAGING Statistical Computing Functional Magnetic Resonance Imaging is a Daryl Pregibon is the 1997 Chair Team Sport of the Statistical Computing Sec- tion. While this is his ®rst column By William F. Eddy for the Newsletter, he is familiar It began like this. It was a cold, dreary day in Novem- with the role of Section Chair. ber 1993. I was sitting in my of®ce studying some box- plots when someone knocked. I looked up. It was a This is my ®rst column as Chair of the Statistical Com- rather wild-lookingman, long gray hair ¯ying about. He puting Section, but alas, it is not my ®rst tour of duty. was very animated, his intensity almost scary. ªI'm a Imagine if you will, a time when a well-groomed statis- psychologist; I need some help,º he said. I smiled; it tician could be sighted wearing bell-bottom trousers and was usually the other way around. He showed me a pic- a zipper-front shirt, perched atop 3 inch platform shoes. ture, a slice of a human brain, he said. I commented that These fashion statements are so old that they are now it didn't seem like very good resolution; ªI thought X- back ± so it goes with my tenure as Chair. And as be- rays were a lot clearer than this fuzzy thing.º He said fore, I look forward to the responsibilities that the posi- it was done with magnetic resonance not x-rays and he tion brings ± including the opportunity to write this col- was studying brain function not brain structure. I tried umn! CONTINUED ON PAGE 2 to absorb the idea. He said the picture was not actually a picture of a brain but rather a visual display of a col- Statistical Graphics lection of t-tests, one for each pixel in the slice. I smiled again thinking how silly it was to do thousands of t-tests. He noticed my smile and said ªI don't know any statis- Sally Morton is the 1997 Chair tics but someone told me about `multiple comparisons.' of the Statistical Graphics Section. I've read up on it and I did the Bonferroni corrections.º She welcomes your feedback and My smile turned into outright laughter. ªNothing is sig- comments on any issue of interest ni®cant,º he said; ªplease help me.º By now I was ®gu- to your section. ratively rolling on the ¯oor. We talked a bit more; I de- cided this was not a problem I wanted to work on. I gave Let me begin my ®rst column by thanking Bill him the names of a few colleagues who, I thought, might DuMouchel for his leadership as our Statistical Graph- straighten him out; I imagined a few Bayesian thoughts ics Chair during the past year. I'd also like to thank might thoroughly discombobulate him. I sent him away Deborah Donnell and Jane Gentleman for their service and promptly forgot all about the incident. as Publications Of®cer and Council of Sections Repre- Aside: Just so you have some idea what this is about, I sentative respectively over the past three years. have included a couple of ®gures (page 17). The ®rst is In 1997, Michael Minnotte will be our new Publications a functional image of a brain; it takes about 32 millisec- Of®cer and Bob Newcomb continues as our Secretary/ onds to acquire the data for it (although the duty cycle of Treasurer. Lorraine Denby and Colin Goodall continue the machine and FDA regulations limit the acquisition CONTINUED ON PAGE 3 CONTINUED ON PAGE 17 EDITORIAL Data visualization in the context of large-scale gene expression analysis is the topic of a paper by Daniel Carr, Roland Somogyi, and George Michaels. The tools On-Line PDF Version they propose for looking at gene expression data include This Newsletter marks our ®rst full year as editors. As stereo plots, parallel coordinate (time series) plots and we commented in our maiden issue, we inherited a qual- conditioned parallel coordinate plots. ity publication from Mike Meyer and Jim Rosenberger. Also, make sure you don't miss the article by Clive During the past year, one of our goals has been to main- Loader describing Loc®t, a software package designed tain the standards they set during their three year tenure to be used in S/Splus that performs local regression, as editors. So, you are probably asking yourself, why is likelihood and related smoothing procedures. the quality of the paper not as nice this time around? The Among the several important notices and announce- answer is simple: because we wish to keep offering you ments that we hope you will ®nd useful, let us point out a ®rst-rate product. For many of the articles we publish, the report on the results of the Computing Section's Stu- color ®gures constitute an essential ingredient, but, alas, dent Paper Competition and the announcements for next they are expensive to print. With the money we save on year's competition and it's companion to be sponsored the paper we can offset the increasing cost of color print- for the ®rst time by the Graphics Section. ing and still balance the budget. This issue should reach your mailboxes before the Joint There are, in fact, other avenues that could be ex- Statistical Meetings in Anaheim and contains a timely plored in order to cut printing costs. One possi- article by our Program Chairs, James Rosenberger and bility would be to stop printing the newsletter al- Dianne Cook, outlining our sections' activities at the together. This might be a drastic measure, but we Meetings. The plans are very exciting and we hope to are now offering a much improved on-line version see all of you there. at http://cm.bell-labs.com/who/cocteau/ newsletter/. You can conveniently browse through Mark Hansen the new PDF (Portable Document Format) version us- Editor, Statistical Computing Section ing the Acrobat reader available free from Adobe. The Bell Laboratories old PostScript version is still available, but we strongly [email protected] encourage you to rely on the new PDF version, a much more interactive document due to the addition of arti- cle listings and various links. As we are contemplat- Mario Peruggia ing polling our readers on the issue of phasing out the Editor, Statistical Graphics Section printed version, we would greatly appreciate receiving The Ohio State University your suggestions on this matter. Please, let us know [email protected] what you think and what questions we should ask. In this issue, we feature an article by William Eddy on functional MRI. Bill tells us, in a lively and informal style, how he became interested in the study of brain FROM OUR CHAIRS (Cont.). function, what statistical issues are involved, and why this scienti®c work would be impossible without the co- Statistical Computing operation of researchers from ®elds as varied as psy- chology, radiology and (of course!) statistics. CONTINUED FROM PAGE 1 Since my previous term, important changes have oc- In another interesting article, John Salch and David curred on the statistical computing landscape and I'll try Scott describe a novel exploratory tool that they devel- to highlight some of these. Perhaps the most important oped to visualize high dimensional data. The method change to the Section is this Newsletter. While all of us they propose, called the Density Grand Tour, is based now take it for granted as one of the bene®ts as Section on the combination of data exploration along continu- members, it is a very recent innovation. In my opinion, ous paths through lower dimensional projections and ef- it has evolved into the premier newsletter in the Associa- ®cient density estimation techniques. John and David tion, combining interesting (and readable!) technical ar- present examples of univariate and bivariate tours and ticles with timely announcements concerning activities discuss the dif®culties associated with the implementa- relevant to the Section membership. tion of trivariate tours. 2 Statistical Computing & Statistical Graphics Newsletter April 97 Many of us remember the good old days of the sity; our Program Chair-Elect, Mark Hansen from Bell ARPANET, a world-wide web free of banners and Labs; and our new Secretary/Treasurer, Merlise Clyde junk email. But with all the bad comes a lot from Duke University. Complete election results will be of good, namely the Section's web presence at available shortly from the ASA home page. I especially http://www-stat.montclair.edu/asascs.This want to thank all of you who were candidates and all of is the best place to look for information on Section ac- you who took the time to send in your ballots. tivities, including online versions of the Newsletter. On behalf of all the current and newly elected Section The ®nal bit of nostalgia concerns a subject near and Of®cers, we look forward to seeing you in Anaheim in dear to many of our hearts Ð namely data analysis. This August ± have a great summer! subject gained respect in statistical circles in the early 70's. But this pales in comparison to the visibility it Daryl Pregibon is now receiving in computer science circles under the Statistics Research AT&T Labs name ªdata mining.º Is it good or bad? In my view it [email protected] is very good as it provides a tremendous opportunity for statistics to illustrate its central role in science and tech- nology.
Recommended publications
  • Causal Diagrams for Interference Elizabeth L
    STATISTICAL SCIENCE Volume 29, Number 4 November 2014 Special Issue on Semiparametrics and Causal Inference Causal Etiology of the Research of James M. Robins ..............................................Thomas S. Richardson and Andrea Rotnitzky 459 Doubly Robust Policy Evaluation and Optimization ..........................Miroslav Dudík, Dumitru Erhan, John Langford and Lihong Li 485 Statistics,CausalityandBell’sTheorem.....................................Richard D. Gill 512 Standardization and Control for Confounding in Observational Studies: A Historical Perspective..............................................Niels Keiding and David Clayton 529 CausalDiagramsforInterference............Elizabeth L. Ogburn and Tyler J. VanderWeele 559 External Validity: From do-calculus to Transportability Across Populations .........................................................Judea Pearl and Elias Bareinboim 579 Nonparametric Bounds and Sensitivity Analysis of Treatment Effects .................Amy Richardson, Michael G. Hudgens, Peter B. Gilbert and Jason P. Fine 596 The Bayesian Analysis of Complex, High-Dimensional Models: Can It Be CODA? .......................................Y.Ritov,P.J.Bickel,A.C.GamstandB.J.K.Kleijn 619 Q-andA-learning Methods for Estimating Optimal Dynamic Treatment Regimes .............. Phillip J. Schulte, Anastasios A. Tsiatis, Eric B. Laber and Marie Davidian 640 A Uniformly Consistent Estimator of Causal Effects under the k-Triangle-Faithfulness Assumption...................................................Peter Spirtes
    [Show full text]
  • Design and Analysis Issues in Family-Based Association
    Emerging Challenges in Statistical Genetics Duncan Thomas University of Southern California Human Genetics in the Big Science Era • “Big Data” – large n and large p and complexity e.g., NIH Biomedical Big Data Initiative (RFA-HG-14-020) • Large n: challenge for computation and data storage, but not conceptual • Large p: many data mining approaches, few grounded in statistical principles • Sparse penalized regression & hierarchical modeling from Bayesian and frequentist perspectives • Emerging –omics challenges Genetics: from Fisher to GWAS • Population genetics & heritability – Mendel / Fisher / Haldane / Wright • Segregation analysis – Likelihoods on complex pedigrees by peeling: Elston & Stewart • Linkage analysis (PCR / microsats / SNPs) – Multipoint: Lander & Green – MCMC: Thompson • Association – TDT, FBATs, etc: Spielman, Laird – GWAS: Risch & Merikangas – Post-GWAS: pathway mining, next-gen sequencing Association: From hypothesis-driven to agnostic research Candidate pathways Candidate Hierarchical GWAS genes models (ht-SNPs) Ontologies Pathway mining MRC BSU SGX Plans Objectives: – Integrating structural and prior information for sparse regression analysis of high dimensional data – Clustering models for exposure-disease associations – Integrating network information – Penalised regression and Bayesian variable selection – Mechanistic models of cellular processes – Statistical computing for large scale genomics data Targeted areas of impact : – gene regulation and immunological response – biomarker based signatures – targeting
    [Show full text]
  • A Joint Spatio-Temporal Model of Opioid Associated Deaths and Treatment Admissions in Ohio
    A JOINT SPATIO-TEMPORAL MODEL OF OPIOID ASSOCIATED DEATHS AND TREATMENT ADMISSIONS IN OHIO BY YIXUAN JI A Thesis Submitted to the Graduate Faculty of WAKE FOREST UNIVERSITY GRADUATE SCHOOL OF ARTS AND SCIENCES in Partial Fulfillment of the Requirements for the Degree of MASTER OF ARTS Mathematics and Statistics May 2019 Winston-Salem, North Carolina Approved By: Staci Hepler, Ph.D., Advisor Miaohua Jiang, Ph.D., Chair Robert Erhardt, Ph.D. Acknowledgments First and foremost, I would like to express my sincere gratitude and appreciation to my advisor, Dr. Staci A. Hepler. Without your continuous support and inspiration, I could not have achieved this thesis research. Thank you so much for your patience, motivation, enthusiasm, and immense knowledge, which made this research even more enjoyable. In addition to my advisor, I would also like to thank Dr. Miaohua Jiang and Dr. Robert Erhardt, for the constant encouragement and advice, and for serving on my thesis committee. Furthermore, this thesis benefited greatly from various courses that I took, including Bayesian statistics from Dr. Hepler, factor analysis and Markov Chain from Dr. Berenhaut, poisson linear model from Dr. Erhardt's Generalized Linear Model class, R programming skills from Dr. Nicole Dalzell and also reasoning skills from Dr. John Gemmer's Real Analysis class. Additionally, I would like to extend my thanks to Dr. Ellen Kirkman, Dr. Stephen Robinson and Dr. Sarah Raynor for their special attention and support to me, as an international student. Last but not least, I would like to thank my family for their sincere mental support throughout my life.
    [Show full text]
  • Alan Agresti
    Historical Highlights in the Development of Categorical Data Analysis Alan Agresti Department of Statistics, University of Florida UF Winter Workshop 2010 CDA History – p. 1/39 Karl Pearson (1857-1936) CDA History – p. 2/39 Karl Pearson (1900) Philos. Mag. Introduces chi-squared statistic (observed − expected)2 X2 = X expected df = no. categories − 1 • testing values for multinomial probabilities (Monte Carlo roulette runs) • testing fit of Pearson curves • testing statistical independence in r × c contingency table (df = rc − 1) CDA History – p. 3/39 Karl Pearson (1904) Advocates measuring association in contingency tables by approximating the correlation for an assumed underlying continuous distribution • tetrachoric correlation (2 × 2, assuming bivariate normality) X2 2 • contingency coefficient 2 based on for testing q X +n X independence in r × c contingency table • introduces term “contingency” as a “measure of the total deviation of the classification from independent probability.” CDA History – p. 4/39 George Udny Yule (1871-1951) (1900) Philos. Trans. Royal Soc. London (1912) JRSS Advocates measuring association using odds ratio n11 n12 n21 n22 n n n n − n n θˆ = 11 22 Q = 11 22 12 21 = (θˆ − 1)/(θˆ + 1) n12n21 n11n22 + n12n21 “At best the normal coefficient can only be said to give us. a hypothetical correlation between supposititious variables. The introduction of needless and unverifiable hypotheses does not appear to me a desirable proceeding in scientific work.” (1911) An Introduction to the Theory of Statistics (14 editions) CDA History – p. 5/39 K. Pearson, with D. Heron (1913) Biometrika “Unthinking praise has been bestowed on a textbook which can only lead statistical students hopelessly astray.” .
    [Show full text]
  • The Stata Journal
    The Stata Journal Editor Editor H. Joseph Newton Nicholas J. Cox Department of Statistics Department of Geography Texas A & M University Durham University College Station, Texas 77843 South Road 979-845-3142; FAX 979-845-3144 Durham City DH1 3LE UK [email protected] [email protected] Associate Editors Christopher F. Baum Jens Lauritsen Boston College Odense University Hospital Rino Bellocco Stanley Lemeshow Karolinska Institutet, Sweden and Ohio State University Univ. degli Studi di Milano-Bicocca, Italy A. Colin Cameron J. Scott Long University of California–Davis Indiana University David Clayton Thomas Lumley Cambridge Inst. for Medical Research University of Washington–Seattle Mario A. Cleves Roger Newson Univ. of Arkansas for Medical Sciences Imperial College, London William D. Dupont Marcello Pagano Vanderbilt University Harvard School of Public Health Charles Franklin Sophia Rabe-Hesketh University of Wisconsin–Madison University of California–Berkeley Joanne M. Garrett J. Patrick Royston University of North Carolina MRC Clinical Trials Unit, London Allan Gregory Philip Ryan Queen’s University University of Adelaide James Hardin Mark E. Schaffer University of South Carolina Heriot-Watt University, Edinburgh Ben Jann Jeroen Weesie ETH Z¨urich, Switzerland Utrecht University Stephen Jenkins Nicholas J. G. Winter University of Essex University of Virginia Ulrich Kohler Jeffrey Wooldridge WZB, Berlin Michigan State University Stata Press Production Manager Lisa Gilmore Stata Press Copy Editor Gabe Waggoner Copyright Statement: The Stata Journal and the contents of the supporting files (programs, datasets, and help files) are copyright c by StataCorp LP. The contents of the supporting files (programs, datasets, and help files) may be copied or reproduced by any means whatsoever, in whole or in part, as long as any copy or reproduction includes attribution to both (1) the author and (2) the Stata Journal.
    [Show full text]
  • GREEN FILM C2B 14/3/03 10:54 Page 1 Richardson
    GREEN FILM C2B 14/3/03 10:54 Page 1 Richardson Hjort and Hjort oxford statistical science series • 27 Green, Highly Structured Stochastic Systems (HSSS) is a modern strategy for building statistical models for real-world problems, for computing with them, and for interpreting the resulting inferences. Complexity is handled by working up from simple local assumptions in a coherent way, and that is the key to modelling, computation, inference and interpretation; the unifying framework is that of Bayesian hierarchical models. The aim of this book is to make recent developments in HSSS Highly Structured Stochastic Systems Stochastic Structured Highly accessible to a general statistical audience. Graphical modelling and Markov chain Monte Carlo (MCMC) methodology are central to the field, and in this text they are treated in depth. These underlying methodologies are balanced by substantive areas of application: to spatial statistics in epidemiology, ecology and image analysis; to genetics; and to infectious disease modelling. The book Highly Structured concludes with two topics (model criticism and Bayesian nonparametrics) that seek to challenge the parametric assumptions that otherwise underlie most HSSS models. Stochastic Systems For each of the 15 topics in the book, there is a substantial article by a leading author in the field, and two invited commentaries that comple- ment, extend, and discuss the topic, and should be read in parallel. All authors are distinguished researchers in the field, and were active participants in an international research programme on HSSS. ALSO AVAILABLE FROM OXFORD UNIVERSITY PRESS Graphical models Steffen L. Lauritzen Neural networks for pattern recognition Christopher M. Bishop Statistical models in epidemiology David Clayton and Michael Hills Analysis of longitudinal data: Second Edition Peter Diggle, Patrick Heagerty, Kung-Yee Liang, and Scott Zeger Models for repeated measurements: Second Edition J.
    [Show full text]
  • Statistical Methods for the Analysis of the Genetics of Gene Expression
    Statistical methods for the analysis of the genetics of gene expression Matthias Alexander Heinig April 13, 2011 Dissertation zur Erlangung des Grades eines Doktors der Naturwissenschaften (Dr. rer. nat.) am Fachbereich Mathematik und Informatik der Freien Universit¨at Berlin Gutachter: Prof. Dr. Martin Vingron Prof. Dr. Norbert H¨ubner 1. Referent: Prof. Dr. Martin Vingron 2. Referent: Prof. Dr. Norbert H¨ubner Tag der Promotion: 13. Dezember 2010 Preface The work that led to this thesis is part of several collaborative projects in which I participated. For the sake of clarity this thesis presents all results that led to the conclusions drawn and individual contributions will be detailed here. For each project I mention publications, highlight my contributions and acknowledge the work of my collaborators. Star project The work presented in section 3.3.1 was published in Nature Genetics [1] by the Star Consortium. It was a large EU funded project initiated by Norbert H¨ubner and led by Kathrin Saar aiming to assess genetic variability among rat strains and to provide haplotype and genetic maps. My contribution was the design of the strategy for the construction of the maps and its implementation and application to three different populations of rats. Moreover I performed the comparative analysis of these maps. I would like to acknowledge Herbert Schulz, Kathrin Saar, Norbert H¨ubner, Edwin Cuppen, Richard Mott, Domenique Gaugier and Mari´e-Th´er`ese Bihoreau for discussions and guidance and Denise Brocklebank for discussion and contributions to the implementation of the analysis. I would like to thank Oliver Hummel and Herbert Schulz for the preprocessing of genotype data.
    [Show full text]
  • A Comparison of Bayesian and Likelihood-Based Methods for fitting Multilevel Models
    Bayesian and likelihood-based methods in multilevel modeling 1 A comparison of Bayesian and likelihood-based methods for fitting multilevel models William J. Browne University of Nottingham, UK and David Drapery University of California, Santa Cruz, USA Summary. We use simulation studies, whose design is realistic for educational and medical re- search (as well as other fields of inquiry), to compare Bayesian and likelihood-based methods for fitting variance-components (VC) and random-effects logistic regression (RELR) models. The like- lihood (and approximate likelihood) approaches we examine are based on the methods most widely used in current applied multilevel (hierarchical) analyses: maximum likelihood (ML) and restricted ML (REML) for Gaussian outcomes, and marginal and penalized quasi-likelihood (MQL and PQL) for Bernoulli outcomes. Our Bayesian methods use Markov chain Monte Carlo (MCMC) estimation, with adaptive hybrid Metropolis-Gibbs sampling for RELR models, and several diffuse prior distri- 1 1 butions (Γ− (, ) and U(0; ) priors for variance components). For evaluation criteria we consider bias of point estimates and nominal versus actual coverage of interval estimates in repeated sam- pling. In two-level VC models we find that (a) both likelihood-based and Bayesian approaches can be made to produce approximately unbiased estimates, although the automatic manner in which REML achieves this is an advantage, but (b) both approaches had difficulty achieving nominal coverage in small samples and with small values of the intraclass correlation. With the three-level RELR models we examine we find that (c) quasi-likelihood methods for estimating random-effects variances perform badly with respect to bias and coverage in the example we simulated, and (d) Bayesian diffuse-prior methods lead to well-calibrated point and interval RELR estimates.
    [Show full text]
  • Equivalence of Random-Effects and Conditional Likelihoods for Matched Case-Control Studies Ken Rice
    Equivalence of random-effects and conditional likelihoods for matched case-control studies Ken Rice MRC Biostatistics Unit, Cambridge, UK January 8th 2004 Random effects derivations of conditional likelihoods Motivation Study of genetic c-erbB-2 ‘exposure’ and breast cancer (Rohan et al, JNCI, 1998) Number of cases exposed 0 1 0 1 0 1 0 1 0 1 0 1 0 0 2 1 0 8 2 0 15 1 0 12 0 Number of 1 0 0 1 3 0 1 1 0 1 6 1 1 6 1 controls 2 0 0 2 1 1 2 3 1 2 3 0 exposed 3 0 0 3 1 0 3 0 0 4 0 0 4 0 0 5 0 0 Each exposure measured imperfectly; Rate of False Positive Exposure ≈ 0.49 Rate of False Negative Exposure ≈ 0.00 (External validation study, 187 subjects) Is any useful inference possible? 1/24 Random effects derivations of conditional likelihoods Matched case-control studies Some disease of interest, want to find if a binary ‘exposure’ is associated For each diseased case, find a control matched for other covariates; age, sex, etc Then measure exposure of interest The exposures are outcomes of interest, not the disease status - must build a model for Pr(exposure), not Pr(disease) Common study design, efficient, simple, popular 2/24 Random effects derivations of conditional likelihoods Formal description Control exposure (1 or 0) is Z1k, Case exposure is Z2k, for pair k Z1k ~Bern( p1k ), Z2k ~Bern( p2k ) Assume odds ratio identical in all pairs k; − ψ = p2k 1 p1k − 1 p2k p1k ψ i.e.
    [Show full text]
  • Regional Newsletter, June 2013
    Regional Newsletter, June 2013 Regional Newsletter, June 2013 France, Netherlands and the BIR. This year the meeting In this issue will take place from 3rd-5th July at the University of St DANIEL FAREWELL & MICHAEL SWEETING Andrews. The keynote speaker for the meeting is Geert Welcome to the June 2013 edition of the BIR newsletter! Verbeke with further invited sessions on Statistical Ecol- As incoming editors we would like to thank Sara Genelet- ogy (David Borchers, Eleni Matechou and Jerome Dupuis), ti for all her efforts in producing the newsletter for the Advances in Genomics (Mark van de Wiel, Hae Won Uh last few years, and for helping us hit the ground running. and Hongzhe Li) and Mixed Modelling (Simon Wood, Ja- We would welcome any comments on the structure and nine Illian and Adeline Samson). The meeting will also content of the current newsletter, and contributions for host the Fisher Memorial Lecture given by Prof. David the next issue! Names in blue (such as ours at the top of Spiegelhalter in the evening of the 3rd July, which will this section) provide email links. be followed by a poster and buffet session. Prior to the In this issue we hear for the first time from our new lecture a presentation will be made to the winner of the regional president, Simon Thompson, and learn about Young Biometrician Prize 2013, which is awarded jointly upcoming regional society events including meetings by the BIR and the Fisher Memorial Trust. There will be around Species Distribution Modelling and Statistical a further 60 contributed talks within 12 contributed ses- Software Interoperability.
    [Show full text]
  • IBC Dublin Is Here!
    The International Biometric Society British & Irish Region Newsletter — July 2008 IBC Dublin is here! With around 800 participants from all over the world coming to savour the delights of Dublin, including many from special circumstance countries, this promises to be a truly international meeting. The excellent programme represents well the broad scope of biometry today. Thanks in particular to those BIR members who have worked hard to make the meeting a success. On the local organising committee, John Hinde (chair), An- drew Mead, Gabrielle Kelly, Adele Marshall, Patrick Murphy, John Whittaker and Angela Wood. On the scientific committee, David Balding and Andrew Mead. It's particularly appropriate that in Andrew we have a BIR international president, and we look forward to South County, Dublin. Kevin McGarry, used Andrew's presidential address. with permission (www.flickr.com/photos/mcgarry) British and Irish Region in Dublin INDEX On Monday 14th July David Bald- drop by the BIR/IBS stand on Mon- ing is inviting all BIR members and day morning/lunchtime. First Regional Seminar p. 3 friends to a half-hour meeting at 6.30pm on the UCD campus, moving This stand will be staffed at break on to a pub later in the evening. and lunchtimes throughout the con- David will talk briefly about some ference. A big thanks to those who In brief p. 4 future plans, and is keen to hear from have volunteered to staff this. Please members about what they would like drop by and say hello, and encour- the BIR to be doing. age potential new members to do the For details of times and places, same.
    [Show full text]
  • Bayesian Demography 250 Years After Bayes
    Bayesian Demography 250 Years after Bayes Short title: Bayesian Demography Jakub Bijak1* and John Bryant2 1 University of Southampton, Southampton, United Kingdom 2 Statistics New Zealand, Christchurch, New Zealand, and the University of Waikato, Hamilton, New Zealand * Address for correspondence: Department of Social Statistics and Demography, University of Southampton, Southampton, SO17 1BJ, United Kingdom, email: [email protected] Abstract Bayesian statistics offers an alternative to classical (frequentist) statistics. It is distinguished by its use of probability distributions to describe uncertain quantities, what leads to elegant solutions to many difficult statistical problems. Although Bayesian demography, like Bayesian statistics more generally, is around 250 years old, only recently has it begun to flourish. The aim of this paper is to review the achievements of Bayesian demography, address some misconceptions, and make the case for wider use of Bayesian methods in population studies. We focus on three applications: demographic forecasts, limited data, and highly-structured or complex models. In general, the ability to integrate information from multiple sources, and to describe uncertainty coherently, is the key advantage of Bayesian methods. Bayesian methods also allow for including additional (prior) information next to the data sample. As such, Bayesian approaches are complementary to many traditional methods, which can be productively re- expressed in Bayesian terms. 1 Bayesian Demography 250 Years after Bayes Keywords: Bayesian demography, Bayesian statistics, demographic methodology, population estimates and forecasts, statistical methods 1. Introduction The original paper of Thomas Bayes (1763) establishing the theorem that bears his name was presented to the Royal Society just over 250 years ago, two years after Bayes’s death, by his friend Richard Price (Courgeau 2012).
    [Show full text]