Three Contributions to the History of Statistics
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THE HISTORY and DEVELOPMENT of STATISTICS in BELGIUM by Dr
THE HISTORY AND DEVELOPMENT OF STATISTICS IN BELGIUM By Dr. Armand Julin Director-General of the Belgian Labor Bureau, Member of the International Statistical Institute Chapter I. Historical Survey A vigorous interest in statistical researches has been both created and facilitated in Belgium by her restricted terri- tory, very dense population, prosperous agriculture, and the variety and vitality of her manufacturing interests. Nor need it surprise us that the successive governments of Bel- gium have given statistics a prominent place in their affairs. Baron de Reiffenberg, who published a bibliography of the ancient statistics of Belgium,* has given a long list of docu- ments relating to the population, agriculture, industry, commerce, transportation facilities, finance, army, etc. It was, however, chiefly the Austrian government which in- creased the number of such investigations and reports. The royal archives are filled to overflowing with documents from that period of our history and their very over-abun- dance forms even for the historian a most diflScult task.f With the French domination (1794-1814), the interest for statistics did not diminish. Lucien Bonaparte, Minister of the Interior from 1799-1800, organized in France the first Bureau of Statistics, while his successor, Chaptal, undertook to compile the statistics of the departments. As far as Belgium is concerned, there were published in Paris seven statistical memoirs prepared under the direction of the prefects. An eighth issue was not finished and a ninth one * Nouveaux mimoires de I'Acadimie royale des sciences et belles lettres de Bruxelles, t. VII. t The Archives of the kingdom and the catalogue of the van Hulthem library, preserved in the Biblioth^que Royale at Brussells, offer valuable information on this head. -
This History of Modern Mathematical Statistics Retraces Their Development
BOOK REVIEWS GORROOCHURN Prakash, 2016, Classic Topics on the History of Modern Mathematical Statistics: From Laplace to More Recent Times, Hoboken, NJ, John Wiley & Sons, Inc., 754 p. This history of modern mathematical statistics retraces their development from the “Laplacean revolution,” as the author so rightly calls it (though the beginnings are to be found in Bayes’ 1763 essay(1)), through the mid-twentieth century and Fisher’s major contribution. Up to the nineteenth century the book covers the same ground as Stigler’s history of statistics(2), though with notable differences (see infra). It then discusses developments through the first half of the twentieth century: Fisher’s synthesis but also the renewal of Bayesian methods, which implied a return to Laplace. Part I offers an in-depth, chronological account of Laplace’s approach to probability, with all the mathematical detail and deductions he drew from it. It begins with his first innovative articles and concludes with his philosophical synthesis showing that all fields of human knowledge are connected to the theory of probabilities. Here Gorrouchurn raises a problem that Stigler does not, that of induction (pp. 102-113), a notion that gives us a better understanding of probability according to Laplace. The term induction has two meanings, the first put forward by Bacon(3) in 1620, the second by Hume(4) in 1748. Gorroochurn discusses only the second. For Bacon, induction meant discovering the principles of a system by studying its properties through observation and experimentation. For Hume, induction was mere enumeration and could not lead to certainty. Laplace followed Bacon: “The surest method which can guide us in the search for truth, consists in rising by induction from phenomena to laws and from laws to forces”(5). -
The Theory of the Design of Experiments
The Theory of the Design of Experiments D.R. COX Honorary Fellow Nuffield College Oxford, UK AND N. REID Professor of Statistics University of Toronto, Canada CHAPMAN & HALL/CRC Boca Raton London New York Washington, D.C. C195X/disclaimer Page 1 Friday, April 28, 2000 10:59 AM Library of Congress Cataloging-in-Publication Data Cox, D. R. (David Roxbee) The theory of the design of experiments / D. R. Cox, N. Reid. p. cm. — (Monographs on statistics and applied probability ; 86) Includes bibliographical references and index. ISBN 1-58488-195-X (alk. paper) 1. Experimental design. I. Reid, N. II.Title. III. Series. QA279 .C73 2000 001.4 '34 —dc21 00-029529 CIP This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. A wide variety of references are listed. Reasonable efforts have been made to publish reliable data and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the consequences of their use. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming, and recording, or by any information storage or retrieval system, without prior permission in writing from the publisher. The consent of CRC Press LLC does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific permission must be obtained in writing from CRC Press LLC for such copying. Direct all inquiries to CRC Press LLC, 2000 N.W. -
Statistical Inference: Paradigms and Controversies in Historic Perspective
Jostein Lillestøl, NHH 2014 Statistical inference: Paradigms and controversies in historic perspective 1. Five paradigms We will cover the following five lines of thought: 1. Early Bayesian inference and its revival Inverse probability – Non-informative priors – “Objective” Bayes (1763), Laplace (1774), Jeffreys (1931), Bernardo (1975) 2. Fisherian inference Evidence oriented – Likelihood – Fisher information - Necessity Fisher (1921 and later) 3. Neyman- Pearson inference Action oriented – Frequentist/Sample space – Objective Neyman (1933, 1937), Pearson (1933), Wald (1939), Lehmann (1950 and later) 4. Neo - Bayesian inference Coherent decisions - Subjective/personal De Finetti (1937), Savage (1951), Lindley (1953) 5. Likelihood inference Evidence based – likelihood profiles – likelihood ratios Barnard (1949), Birnbaum (1962), Edwards (1972) Classical inference as it has been practiced since the 1950’s is really none of these in its pure form. It is more like a pragmatic mix of 2 and 3, in particular with respect to testing of significance, pretending to be both action and evidence oriented, which is hard to fulfill in a consistent manner. To keep our minds on track we do not single out this as a separate paradigm, but will discuss this at the end. A main concern through the history of statistical inference has been to establish a sound scientific framework for the analysis of sampled data. Concepts were initially often vague and disputed, but even after their clarification, various schools of thought have at times been in strong opposition to each other. When we try to describe the approaches here, we will use the notions of today. All five paradigms of statistical inference are based on modeling the observed data x given some parameter or “state of the world” , which essentially corresponds to stating the conditional distribution f(x|(or making some assumptions about it). -
Front Matter
Cambridge University Press 978-1-107-61967-8 - Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction Bradley Efron Frontmatter More information Large-Scale Inference We live in a new age for statistical inference, where modern scientific technology such as microarrays and fMRI machines routinely produce thousands and sometimes millions of parallel data sets, each with its own estimation or testing problem. Doing thousands of problems at once involves more than repeated application of classical methods. Taking an empirical Bayes approach, Bradley Efron, inventor of the bootstrap, shows how information accrues across problems in a way that combines Bayesian and frequentist ideas. Estimation, testing, and prediction blend in this framework, producing opportunities for new methodologies of increased power. New difficulties also arise, easily leading to flawed inferences. This book takes a careful look at both the promise and pitfalls of large-scale statistical inference, with particular attention to false discovery rates, the most successful of the new statistical techniques. Emphasis is on the inferential ideas underlying technical developments, illustrated using a large number of real examples. bradley efron is Max H. Stein Professor of Statistics and Biostatistics at the Stanford University School of Humanities and Sciences, and the Department of Health Research and Policy at the School of Medicine. © in this web service Cambridge University Press www.cambridge.org Cambridge University Press 978-1-107-61967-8 - Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction Bradley Efron Frontmatter More information INSTITUTE OF MATHEMATICAL STATISTICS MONOGRAPHS Editorial Board D. R. Cox (University of Oxford) B. Hambly (University of Oxford) S. -
The Enigma of Karl Pearson and Bayesian Inference
The Enigma of Karl Pearson and Bayesian Inference John Aldrich 1 Introduction “An enigmatic position in the history of the theory of probability is occupied by Karl Pearson” wrote Harold Jeffreys (1939, p. 313). The enigma was that Pearson based his philosophy of science on Bayesian principles but violated them when he used the method of moments and probability-values in statistics. It is not uncommon to see a divorce of practice from principles but Pearson also used Bayesian methods in some of his statistical work. The more one looks at his writings the more puzzling they seem. In 1939 Jeffreys was probably alone in regretting that Pearson had not been a bottom to top Bayesian. Bayesian methods had been in retreat in English statistics since Fisher began attacking them in the 1920s and only Jeffreys ever looked for a synthesis of logic, statistical methods and everything in between. In the 1890s, when Pearson started, everything was looser: statisticians used inverse arguments based on Bayesian principles and direct arguments based on sampling theory and a philosopher-physicist-statistician did not have to tie everything together. Pearson did less than his early guide Edgeworth or his students Yule and Gosset (Student) to tie up inverse and direct arguments and I will be look- ing to their work for clues to his position and for points of comparison. Of Pearson’s first course on the theory of statistics Yule (1938, p. 199) wrote, “A straightforward, organized, logically developed course could hardly then [in 1894] exist when the very elements of the subject were being developed.” But Pearson never produced such a course or published an exposition as integrated as Yule’s Introduction, not to mention the nonpareil Probability of Jeffreys. -
The Future of Statistics
THE FUTURE OF STATISTICS Bradley Efron The 1990 sesquicentennial celebration of the ASA included a set of eight articles on the future of statistics, published in the May issue of the American Statistician. Not all of the savants took their prediction duties seriously, but looking back from 2007 I would assign a respectable major league batting average of .333 to those who did. Of course it's only been 17 years and their average could still improve. The statistical world seems to be evolving even more rapidly today than in 1990, so I'd be wise not to sneeze at .333 as I begin my own prediction efforts. There are at least two futures to consider: statistics as a profession, both academic and industrial, and statistics as an intellectual discipline. (And maybe a third -- demographic – with the center of mass of our field moving toward Asia, and perhaps toward female, too.) We tend to think of statistics as a small profession, but a century of steady growth, accelerating in the past twenty years, now makes "middling" a better descriptor. Together, statistics and biostatistics departments graduated some 600 Ph.D.s last year, with no shortage of good jobs awaiting them in industry or academia. The current money-making powerhouses of industry, the hedge funds, have become probability/statistics preserves. Perhaps we can hope for a phalanx of new statistics billionaires, gratefully recalling their early years in the humble ranks of the ASA. Statistics departments have gotten bigger and more numerous around the U.S., with many universities now having two. -
Fiducial Inference: an Approach Based on Bootstrap Techniques
U.P.B. Sci. Bull., Series A, Vol. 69, No. 1, 2007 ISSN 1223-7027 FIDUCIAL INFERENCE: AN APPROACH BASED ON BOOTSTRAP TECHNIQUES H.-D. HEIKE1, C-tin TÂRCOLEA2, Adina I. TARCOLEA3, M. DEMETRESCU4 În prima parte a acestei lucrări sunt prezentate conceptele de bază ale inferenţei fiduciale. La aplicarea acestui principiu inferenţial apar dificultăţi când distribuţia variabilei pivotale folosite nu este cunoscută. În partea a doua este propusă o soluţie pentru această problemă constând în folosirea de metode bootstrap. The basic concepts of fiducial inference are presented in the first part of this paper. Problems arise if the form of the distribution of the pivot used by the fiducial argument is not known. Our main result consists in showing how bootstrap techniques can be used to handle this kind of situation. Keywords: Fiducial distribution, estimation of parameters, pivotal methods, approximation of distributions, approximate pivot. AMS 2000 Classification: 62F99 1. Introduction Commenting on Fisher’s work, [3] stated at the beginning of his paper: ’The history of fiducial probability dates back over thirty years and so is long by statistical standards; however, thirty years have not proved long enough for agreement to be reached among statisticians as to the derivation, manipulation and interpretation of fiducial probability’. Forty years later, the situation is very much the same. What is actually the fiducial theory? The fiducial model designed by Fisher leads us to objective probability statements about values of unknown parameters solely on the basis of given data, without resorting to any prior distribution. The idea that the initially assumed randomness may be preserved was one of Fisher’s great contributions to the theory of statistical estimation; the lack of acceptance arose from the fact that 1 Prof., Statistics and Econometrics, Technical University Darmstadt, Residenzschloss, D-64289 Darmstadt, GERMANY. -
The Significance Test Controversy Revisited: the Fiducial Bayesian
Bruno LECOUTRE Jacques POITEVINEAU The Significance Test Controversy Revisited: The fiducial Bayesian Alternative July 26, 2014 Springer Contents 1 Introduction ................................................... 3 1.1 The fiducial Bayesian Inference. .4 1.2 The Stranglehold of Significance Tests . .5 1.3 Beyond the Significance Test Controversy . .6 1.4 The Feasibility of Fiducial Bayesian Methods . .6 1.5 Plan of the Book . .7 2 Preamble - Frequentist and Bayesian Inference .................... 9 2.1 Two Different Approaches to Statistical Inference . .9 2.2 The Frequentist Approach: From Unknown to Known . 10 2.2.1 Sampling Probabilities . 10 2.2.2 Null Hypothesis Significance Testing in Practice . 11 2.2.3 Confidence Interval . 12 2.3 The Bayesian Approach: From Known to Unknown . 12 2.3.1 The Likelihood Function and the Bayesian Probabilities . 12 2.3.2 An Opinion-Based Analysis . 14 2.3.3 A “No Information Initially” Analysis . 16 3 The Fisher, Neyman-Pearson and Jeffreys views of Statistical Tests . 21 3.1 The Fisher Test of Significance . 21 3.2 The Neyman-Pearson Hypothesis Test . 23 3.3 The Jeffreys Bayesian Approach to Testing . 25 3.4 Different Views of Statistical Inference . 28 3.4.1 Different Scopes of Applications: The Aim of Statistical Inference . 28 3.4.2 The Role of Bayesian Probabilities . 30 3.4.3 Statistical Tests: Judgment, Action or Decision? . 32 3.5 Is It possible to Unify the Fisher and Neyman-Pearson Approaches? 34 3.6 Concluding Remarks . 35 v vi Contents 4 GHOST: An officially Recommended Practice ..................... 37 4.1 Null Hypothesis Significance Testing . 37 4.1.1 An Amalgam . -
Statistics and Measurements
Volume 106, Number 1, January–February 2001 Journal of Research of the National Institute of Standards and Technology [J. Res. Natl. Inst. Stand. Technol. 106, 279–292 (2001)] Statistics and Measurements Volume 106 Number 1 January–February 2001 M. Carroll Croarkin For more than 50 years, the Statistical En- survey of several thematic areas. The ac- gineering Division (SED) has been in- companying examples illustrate the im- National Institute of Standards and strumental in the success of a broad spec- portance of SED in the history of statistics, Technology, trum of metrology projects at NBS/NIST. measurements and standards: calibration Gaithersburg, MD 20899-8980 This paper highlights fundamental contribu- and measurement assurance, interlaboratory tions of NBS/NIST statisticians to statis- tests, development of measurement meth- [email protected] tics and to measurement science and tech- ods, Standard Reference Materials, statisti- nology. Published methods developed by cal computing, and dissemination of SED staff, especially during the early years, measurement technology. A brief look for- endure as cornerstones of statistics not ward sketches the expanding opportunity only in metrology and standards applica- and demand for SED statisticians created tions, but as data-analytic resources used by current trends in research and devel- across all disciplines. The history of statis- opment at NIST. tics at NBS/NIST began with the forma- tion of what is now the SED. Examples from the first five decades of the SED il- Key words: calibration and measure- lustrate the critical role of the division in ment assurance; error analysis and uncer- the successful resolution of a few of the tainty; design of experiments; history of highly visible, and sometimes controversial, NBS; interlaboratory testing; measurement statistical studies of national importance. -
History of Health Statistics
certain gambling problems [e.g. Pacioli (1494), Car- Health Statistics, History dano (1539), and Forestani (1603)] which were first of solved definitively by Pierre de Fermat (1601–1665) and Blaise Pascal (1623–1662). These efforts gave rise to the mathematical basis of probability theory, The field of statistics in the twentieth century statistical distribution functions (see Sampling Dis- (see Statistics, Overview) encompasses four major tributions), and statistical inference. areas; (i) the theory of probability and mathematical The analysis of uncertainty and errors of mea- statistics; (ii) the analysis of uncertainty and errors surement had its foundations in the field of astron- of measurement (see Measurement Error in omy which, from antiquity until the eighteenth cen- Epidemiologic Studies); (iii) design of experiments tury, was the dominant area for use of numerical (see Experimental Design) and sample surveys; information based on the most precise measure- and (iv) the collection, summarization, display, ments that the technologies of the times permit- and interpretation of observational data (see ted. The fallibility of their observations was evident Observational Study). These four areas are clearly to early astronomers, who took the “best” obser- interrelated and have evolved interactively over the vation when several were taken, the “best” being centuries. The first two areas are well covered assessed by such criteria as the quality of obser- in many histories of mathematical statistics while vational conditions, the fame of the observer, etc. the third area, being essentially a twentieth- But, gradually an appreciation for averaging observa- century development, has not yet been adequately tions developed and various techniques for fitting the summarized. -
Statistical Tests, P-Values, Confidence Intervals, and Power
Statistical Tests, P-values, Confidence Intervals, and Power: A Guide to Misinterpretations Sander GREENLAND, Stephen J. SENN, Kenneth J. ROTHMAN, John B. CARLIN, Charles POOLE, Steven N. GOODMAN, and Douglas G. ALTMAN Misinterpretation and abuse of statistical tests, confidence in- Introduction tervals, and statistical power have been decried for decades, yet remain rampant. A key problem is that there are no interpreta- Misinterpretation and abuse of statistical tests has been de- tions of these concepts that are at once simple, intuitive, cor- cried for decades, yet remains so rampant that some scientific rect, and foolproof. Instead, correct use and interpretation of journals discourage use of “statistical significance” (classify- these statistics requires an attention to detail which seems to tax ing results as “significant” or not based on a P-value) (Lang et the patience of working scientists. This high cognitive demand al. 1998). One journal now bans all statistical tests and mathe- has led to an epidemic of shortcut definitions and interpreta- matically related procedures such as confidence intervals (Trafi- tions that are simply wrong, sometimes disastrously so—and mow and Marks 2015), which has led to considerable discussion yet these misinterpretations dominate much of the scientific lit- and debate about the merits of such bans (e.g., Ashworth 2015; erature. Flanagan 2015). In light of this problem, we provide definitions and a discus- Despite such bans, we expect that the statistical methods at sion of basic statistics that are more general and critical than issue will be with us for many years to come. We thus think it typically found in traditional introductory expositions.