On Rereading R. A. Fisher [Fisher Memorial Lecture, with Comments]
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Sequential Analysis Tests and Confidence Intervals
D. Siegmund Sequential Analysis Tests and Confidence Intervals Series: Springer Series in Statistics The modern theory of Sequential Analysis came into existence simultaneously in the United States and Great Britain in response to demands for more efficient sampling inspection procedures during World War II. The develop ments were admirably summarized by their principal architect, A. Wald, in his book Sequential Analysis (1947). In spite of the extraordinary accomplishments of this period, there remained some dissatisfaction with the sequential probability ratio test and Wald's analysis of it. (i) The open-ended continuation region with the concomitant possibility of taking an arbitrarily large number of observations seems intol erable in practice. (ii) Wald's elegant approximations based on "neglecting the excess" of the log likelihood ratio over the stopping boundaries are not especially accurate and do not allow one to study the effect oftaking observa tions in groups rather than one at a time. (iii) The beautiful optimality property of the sequential probability ratio test applies only to the artificial problem of 1985, XI, 274 p. testing a simple hypothesis against a simple alternative. In response to these issues and to new motivation from the direction of controlled clinical trials numerous modifications of the sequential probability ratio test were proposed and their properties studied-often Printed book by simulation or lengthy numerical computation. (A notable exception is Anderson, 1960; see III.7.) In the past decade it has become possible to give a more complete theoretical Hardcover analysis of many of the proposals and hence to understand them better. ▶ 119,99 € | £109.99 | $149.99 ▶ *128,39 € (D) | 131,99 € (A) | CHF 141.50 eBook Available from your bookstore or ▶ springer.com/shop MyCopy Printed eBook for just ▶ € | $ 24.99 ▶ springer.com/mycopy Order online at springer.com ▶ or for the Americas call (toll free) 1-800-SPRINGER ▶ or email us at: [email protected]. -
Trial Sequential Analysis: Novel Approach for Meta-Analysis
Anesth Pain Med 2021;16:138-150 https://doi.org/10.17085/apm.21038 Review pISSN 1975-5171 • eISSN 2383-7977 Trial sequential analysis: novel approach for meta-analysis Hyun Kang Received April 19, 2021 Department of Anesthesiology and Pain Medicine, Chung-Ang University College of Accepted April 25, 2021 Medicine, Seoul, Korea Systematic reviews and meta-analyses rank the highest in the evidence hierarchy. However, they still have the risk of spurious results because they include too few studies and partici- pants. The use of trial sequential analysis (TSA) has increased recently, providing more infor- mation on the precision and uncertainty of meta-analysis results. This makes it a powerful tool for clinicians to assess the conclusiveness of meta-analysis. TSA provides monitoring boundaries or futility boundaries, helping clinicians prevent unnecessary trials. The use and Corresponding author Hyun Kang, M.D., Ph.D. interpretation of TSA should be based on an understanding of the principles and assump- Department of Anesthesiology and tions behind TSA, which may provide more accurate, precise, and unbiased information to Pain Medicine, Chung-Ang University clinicians, patients, and policymakers. In this article, the history, background, principles, and College of Medicine, 84 Heukseok-ro, assumptions behind TSA are described, which would lead to its better understanding, imple- Dongjak-gu, Seoul 06974, Korea mentation, and interpretation. Tel: 82-2-6299-2586 Fax: 82-2-6299-2585 E-mail: [email protected] Keywords: Interim analysis; Meta-analysis; Statistics; Trial sequential analysis. INTRODUCTION termined number of patients was reached [2]. A systematic review is a research method that attempts Sequential analysis is a statistical method in which the fi- to collect all empirical evidence according to predefined nal number of patients analyzed is not predetermined, but inclusion and exclusion criteria to answer specific and fo- sampling or enrollment of patients is decided by a prede- cused questions [3]. -
Detecting Reinforcement Patterns in the Stream of Naturalistic Observations of Social Interactions
Portland State University PDXScholar Dissertations and Theses Dissertations and Theses 7-14-2020 Detecting Reinforcement Patterns in the Stream of Naturalistic Observations of Social Interactions James Lamar DeLaney 3rd Portland State University Follow this and additional works at: https://pdxscholar.library.pdx.edu/open_access_etds Part of the Developmental Psychology Commons Let us know how access to this document benefits ou.y Recommended Citation DeLaney 3rd, James Lamar, "Detecting Reinforcement Patterns in the Stream of Naturalistic Observations of Social Interactions" (2020). Dissertations and Theses. Paper 5553. https://doi.org/10.15760/etd.7427 This Thesis is brought to you for free and open access. It has been accepted for inclusion in Dissertations and Theses by an authorized administrator of PDXScholar. Please contact us if we can make this document more accessible: [email protected]. Detecting Reinforcement Patterns in the Stream of Naturalistic Observations of Social Interactions by James Lamar DeLaney 3rd A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Psychology Thesis Committee: Thomas Kindermann, Chair Jason Newsom Ellen A. Skinner Portland State University i Abstract How do consequences affect future behaviors in real-world social interactions? The term positive reinforcer refers to those consequences that are associated with an increase in probability of an antecedent behavior (Skinner, 1938). To explore whether reinforcement occurs under naturally occuring conditions, many studies use sequential analysis methods to detect contingency patterns (see Quera & Bakeman, 1998). This study argues that these methods do not look at behavior change following putative reinforcers, and thus, are not sufficient for declaring reinforcement effects arising in naturally occuring interactions, according to the Skinner’s (1938) operational definition of reinforcers. -
Jacob Wolfowitz 1910–1981
NATIONAL ACADEMY OF SCIENCES JACOB WOLFOWITZ 1910–1981 A Biographical Memoir by SHELEMYAHU ZACKS Any opinions expressed in this memoir are those of the author and do not necessarily reflect the views of the National Academy of Sciences. Biographical Memoirs, VOLUME 82 PUBLISHED 2002 BY THE NATIONAL ACADEMY PRESS WASHINGTON, D.C. JACOB WOLFOWITZ March 19, 1910–July 16, 1981 BY SHELEMYAHU ZACKS ACOB WOLFOWITZ, A GIANT among the founders of modern Jstatistics, will always be remembered for his originality, deep thinking, clear mind, excellence in teaching, and vast contributions to statistical and information sciences. I met Wolfowitz for the first time in 1957, when he spent a sab- batical year at the Technion, Israel Institute of Technology. I was at the time a graduate student and a statistician at the building research station of the Technion. I had read papers of Wald and Wolfowitz before, and for me the meeting with Wolfowitz was a great opportunity to associate with a great scholar who was very kind to me and most helpful. I took his class at the Technion on statistical decision theory. Outside the classroom we used to spend time together over a cup of coffee or in his office discussing statistical problems. He gave me a lot of his time, as though I was his student. His advice on the correct approach to the theory of statistics accompanied my development as statistician for many years to come. Later we kept in touch, mostly by correspondence and in meetings of the Institute of Mathematical Statistics. I saw him the last time in his office at the University of Southern Florida in Tampa, where he spent the last years 3 4 BIOGRAPHICAL MEMOIRS of his life. -
Sequential Analysis Exercises
Sequential analysis Exercises : 1. What is the practical idea at origin of sequential analysis ? 2. In which cases sequential analysis is appropriate ? 3. What are the supposed advantages of sequential analysis ? 4. What does the values from a three steps sequential analysis in the following table are for ? nb accept refuse grains e xamined 20 0 5 40 2 7 60 6 7 5. Do I have alpha and beta risks when I use sequential analysis ? ______________________________________________ 1 5th ISTA seminar on statistics S Grégoire August 1999 SEQUENTIAL ANALYSIS.DOC PRINCIPLE OF THE SEQUENTIAL ANALYSIS METHOD The method originates from a practical observation. Some objects are so good or so bad that with a quick look we are able to accept or reject them. For other objects we need more information to know if they are good enough or not. In other words, we do not need to put the same effort on all the objects we control. As a result of this we may save time or money if we decide for some objects at an early stage. The general basis of sequential analysis is to define, -before the studies-, sampling schemes which permit to take consistent decisions at different stages of examination. The aim is to compare the results of examinations made on an object to control <-- to --> decisional limits in order to take a decision. At each stage of examination the decision is to accept the object if it is good enough with a chosen probability or to reject the object if it is too bad (at a chosen probability) or to continue the study if we need more information before to fall in one of the two above categories. -
Adalékok Wald Ábrahám Életrajzához Additions to Abraham Wald's Biography Adăugări La Biografia Lui Abraham Wald FERENC Márta, LŐRINCZ Annamária, OLÁH-GÁL Róbert
Adalékok Wald Ábrahám életrajzához Additions to Abraham Wald's biography Adăugări la biografia lui Abraham Wald FERENC Márta, LŐRINCZ Annamária, OLÁH-GÁL Róbert Sapientia Erdélyi Magyar Tudományegyetem, Csíkszeredai Kar, [email protected], [email protected], [email protected] Összefoglaló Száz évvel Bolyai János születése után Kolozsváron született egy másik nagy, de sajnos ke- vébé ismert matematikus, Wald Ábrahám. Wald Ábrahám ortodox-zsidó (hasszid) családban született. Sajnos nagyon kevés dokumentum maradt meg Wald életéről. A modern statisztikák- ban és az ökonometriában Wald nevét sok tétel őrzi. Ebben a cikkben eredeti dokumentumokat mutatunk be Wald Ábrahám életéről, nevezetesen: a kolozsvári piarista gimnázium matrikulai nyilvántartását, és 3 Wald Ábrahám által Alexits Györgynek magyarul írt levelet. A levéltári anyagok bemutatása forrásközlés. A cikk végén hangsúlyozzuk Wald Ábrahám szellemi és anyagi örökségének megőrzésének fontosságát. Wald örökségének megőrzéséhez emléktáblát kellene felállítani a Waldek szülői házának falán, Kolozsváron. Abstract One hundred years after the birth of János Bolyai, another well-known mathematician, Ábrahám Wald, was born in Cluj-Napoca. Ábrahám Wald was born into a Jewish-Orthodox family. Unfortu- nately, we don't have many documents about Wald's life. In modern statistics and econometrics many theorems of Wald's name are related. In this article we present original documents about the life of Ábrahám Wald, namely: the matriculation register from the Piarist High School in Cluj, and 3 letters of Ábrahám Wald written to György Alexits in Hungarian. Our presentation is an authentic first publi- cation. At the end of this article, we emphasize the importance of commemorating the intellectual and material heritage of Ábrahám Wald. -
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. -
Sequential and Multiple Testing for Dose-Response Analysis
Drug Information Journal, Vol. 34, pp. 591±597, 2000 0092-8615/2000 Printed in the USA. All rights reserved. Copyright 2000 Drug Information Association Inc. SEQUENTIAL AND MULTIPLE TESTING FOR DOSE-RESPONSE ANALYSIS WALTER LEHMACHER,PHD Professor, Institute for Medical Statistics, Informatics and Epidemiology, University of Cologne, Germany MEINHARD KIESER,PHD Head, Department of Biometry, Dr. Willmar Schwabe Pharmaceuticals, Karlsruhe, Germany LUDWIG HOTHORN,PHD Professor, Department of Bioinformatics, University of Hannover, Germany For the analysis of dose-response relationship under the assumption of ordered alterna- tives several global trend tests are available. Furthermore, there are multiple test proce- dures which can identify doses as effective or even as minimally effective. In this paper it is shown that the principles of multiple comparisons and interim analyses can be combined in flexible and adaptive strategies for dose-response analyses; these procedures control the experimentwise error rate. Key Words: Dose-response; Interim analysis; Multiple testing INTRODUCTION rate in a strong sense) are necessary. The global and multiplicity aspects are also re- THE FIRST QUESTION in dose-response flected by the actual International Confer- analysis is whether a monotone trend across ence for Harmonization Guidelines (1) for dose groups exists. This leads to global trend dose-response analysis. Appropriate order tests, where the global level (familywise er- restricted tests can be confirmatory for dem- ror rate in a weak sense) is controlled. In onstrating a global trend and suitable multi- clinical research, one is additionally inter- ple procedures can identify a recommended ested in which doses are effective, which dose. minimal dose is effective, or which dose For the global hypothesis, there are sev- steps are effective, in order to substantiate eral suitable trend tests. -
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 . -
Dataset Decay and the Problem of Sequential Analyses on Open Datasets
FEATURE ARTICLE META-RESEARCH Dataset decay and the problem of sequential analyses on open datasets Abstract Open data allows researchers to explore pre-existing datasets in new ways. However, if many researchers reuse the same dataset, multiple statistical testing may increase false positives. Here we demonstrate that sequential hypothesis testing on the same dataset by multiple researchers can inflate error rates. We go on to discuss a number of correction procedures that can reduce the number of false positives, and the challenges associated with these correction procedures. WILLIAM HEDLEY THOMPSON*, JESSEY WRIGHT, PATRICK G BISSETT AND RUSSELL A POLDRACK Introduction sequential procedures correct for the latest in a In recent years, there has been a push to make non-simultaneous series of tests. There are sev- the scientific datasets associated with published eral proposed solutions to address multiple papers openly available to other researchers sequential analyses, namely a-spending and a- (Nosek et al., 2015). Making data open will investing procedures (Aharoni and Rosset, allow other researchers to both reproduce pub- 2014; Foster and Stine, 2008), which strictly lished analyses and ask new questions of existing control false positive rate. Here we will also pro- datasets (Molloy, 2011; Pisani et al., 2016). pose a third, a-debt, which does not maintain a The ability to explore pre-existing datasets in constant false positive rate but allows it to grow new ways should make research more efficient controllably. and has the potential to yield new discoveries Sequential correction procedures are harder *For correspondence: william. (Weston et al., 2019). to implement than simultaneous procedures as [email protected] The availability of open datasets will increase they require keeping track of the total number over time as funders mandate and reward data Competing interests: The of tests that have been performed by others. -
Fisher's Fiducial Argument and Bayes Theorem
R. A. Fisher's Fiducial Argument and Bayes' Theorem Teddy Seidenfeld Statistical Science, Vol. 7, No. 3. (Aug., 1992), pp. 358-368. Stable URL: http://links.jstor.org/sici?sici=0883-4237%28199208%297%3A3%3C358%3ARAFFAA%3E2.0.CO%3B2-G Statistical Science is currently published by Institute of Mathematical Statistics. Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://www.jstor.org/journals/ims.html. Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission. The JSTOR Archive is a trusted digital repository providing for long-term preservation and access to leading academic journals and scholarly literature from around the world. The Archive is supported by libraries, scholarly societies, publishers, and foundations. It is an initiative of JSTOR, a not-for-profit organization with a mission to help the scholarly community take advantage of advances in technology. For more information regarding JSTOR, please contact [email protected]. http://www.jstor.org Tue Mar 4 10:35:54 2008 Statistical Science 1992, Vol. -
Improper Priors & Fiducial Inference
IMPROPER PRIORS & FIDUCIAL INFERENCE Gunnar Taraldsen & Bo Henry Lindqvist Norwegian University of Science and Technology The 4th Bayesian, Fiducial and Frequentist Workshop Harvard University May 2017 1 / 22 Abstract The use of improper priors flourish in applications and is as sucha central part of contemporary statistics. Unfortunately, this is most often presented without a theoretical basis: “Improper priors are just limits of proper priors ... ” We present ingredients in a mathematical theory for statistics which generalize the axioms of Kolmogorov so that improper priors are included. A particular by-product is an elimination of the famous marginalization paradoxes in Bayesian and structural inference. Secondly, we demonstrate that structural and fiducial inference can be formulated naturally in this theory of conditional probability spaces. A particular by-product is then a proof of conditions which ensure coincidence between a Bayesian posterior and the fiducial distribution. The concept of a conditional fiducial model is introduced, and the interpretation of the fiducial distribution is discussed. It isin particular explained that the information given by the prior distribution in Bayesian analysis is replaced by the information given by the fiducial relation in fiducial inference. 2 / 22 Table of Contents Introduction Statistics with improper priors Fiducial inference 3 / 22 The Large Picture 4 / 22 A motivating problem gives it all I Initial problem: Generate data X = χ(U; θ) conditionally given a sufficient statistic T = τ(U; θ) = t. I Tentative solution: Adjust parameter value θ for simulated data so that the sufficient statistic is kept fixed equal to t (Trotter-Tukey, 1956; Engen-Lillegård, Biometrika 1997).