The Scientific Contributions of R
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Volume Quantification by Contrast-Enhanced Ultrasound
Herold et al. Cardiovascular Ultrasound 2013, 11:36 http://www.cardiovascularultrasound.com/content/11/1/36 CARDIOVASCULAR ULTRASOUND RESEARCH Open Access Volume quantification by contrast-enhanced ultrasound: an in-vitro comparison with true volumes and thermodilution Ingeborg HF Herold1*, Gianna Russo2, Massimo Mischi2, Patrick Houthuizen3, Tamerlan Saidov2, Marcel van het Veer3, Hans C van Assen2 and Hendrikus HM Korsten1,2 Abstract Background: Contrast-enhanced ultrasound (CEUS) has recently been proposed as a minimally- invasive, alternative method for blood volume measurement. This study aims at comparing the accuracy of CEUS and the classical thermodilution techniques for volume assessment in an in-vitro set-up. Methods: The in-vitro set-up consisted of a variable network between an inflow and outflow tube and a roller pump. The inflow and outflow tubes were insonified with an ultrasound array transducer and a thermistor was placed in each tube. Indicator dilution curves were made by injecting indicator which consisted of an ultrasound- contrast-agent diluted in ice-cold saline. Both acoustic intensity- and thermo-dilution curves were used to calculate the indicator mean transit time between the inflow and outflow tube. The volumes were derived by multiplying the estimated mean transit time by the flow rate. We compared the volumes measured by CEUS with the true volumes of the variable network and those measured by thermodilution by Bland-Altman and intraclass-correlation analysis. Results: The measurements by CEUS and thermodilution showed a very strong correlation (rs=0.94) with a modest volume underestimation by CEUS of −40 ± 28 mL and an overestimation of 84 ± 62 mL by thermodilution compared with the true volumes. -
On the Sampling Variance of Intraclass Correlations and Genetic Correlations
Copyright 1998 by the Genetics Society of America On the Sampling Variance of Intraclass Correlations and Genetic Correlations Peter M. Visscher University of Edinburgh, Institute of Ecology and Resource Management, Edinburgh EH9 3JG, Scotland Manuscript received February 3, 1997 Accepted for publication March 23, 1998 ABSTRACT Widely used standard expressions for the sampling variance of intraclass correlations and genetic correlation coef®cients were reviewed for small and large sample sizes. For the sampling variance of the intraclass correlation, it was shown by simulation that the commonly used expression, derived using a ®rst-order Taylor series performs better than alternative expressions found in the literature, when the between-sire degrees of freedom were small. The expressions for the sampling variance of the genetic correlation are signi®cantly biased for small sample sizes, in particular when the population values, or their estimates, are close to zero. It was shown, both analytically and by simulation, that this is because the estimate of the sampling variance becomes very large in these cases due to very small values of the denominator of the expressions. It was concluded, therefore, that for small samples, estimates of the heritabilities and genetic correlations should not be used in the expressions for the sampling variance of the genetic correlation. It was shown analytically that in cases where the population values of the heritabili- ties are known, using the estimated heritabilities rather than their true values to estimate the genetic correlation results in a lower sampling variance for the genetic correlation. Therefore, for large samples, estimates of heritabilities, and not their true values, should be used. -
National Academy Elects IMS Fellows Have You Voted Yet?
Volume 38 • Issue 5 IMS Bulletin June 2009 National Academy elects IMS Fellows CONTENTS The United States National Academy of Sciences has elected 72 new members and 1 National Academy elects 18 foreign associates from 15 countries in recognition of their distinguished and Raftery, Wong continuing achievements in original research. Among those elected are two IMS Adrian Raftery 2 Members’ News: Jianqing Fellows: , Blumstein-Jordan Professor of Statistics and Sociology, Center Fan; SIAM Fellows for Statistics and the Social Sciences, University of Washington, Seattle, and Wing Hung Wong, Professor 3 Laha Award recipients of Statistics and Professor of Health Research and Policy, 4 COPSS Fisher Lecturer: Department of Statistics, Stanford University, California. Noel Cressie The election was held April 28, during the business 5 Members’ Discoveries: session of the 146th annual meeting of the Academy. Nicolai Meinshausen Those elected bring the total number of active members 6 Medallion Lecture: Tony Cai to 2,150. Foreign associates are non-voting members of the Academy, with citizenship outside the United States. Meeting report: SSP Above: Adrian Raftery 7 This year’s election brings the total number of foreign 8 New IMS Fellows Below: Wing H. Wong associates to 404. The National Academy of Sciences is a private 10 Obituaries: Keith Worsley; I.J. Good organization of scientists and engineers dedicated to the furtherance of science and its use for general welfare. 12-3 JSM program highlights; It was established in 1863 by a congressional act of IMS sessions at JSM incorporation signed by Abraham Lincoln that calls on 14-5 JSM tours; More things to the Academy to act as an official adviser to the federal do in DC government, upon request, in any matter of science or 16 Accepting rejections technology. -
Memorial to Sir Harold Jeffreys 1891-1989 JOHN A
Memorial to Sir Harold Jeffreys 1891-1989 JOHN A. HUDSON and ALAN G. SMITH University of Cambridge, Cambridge, England Harold Jeffreys was one of this century’s greatest applied mathematicians, using mathematics as a means of under standing the physical world. Principally he was a geo physicist, although statisticians may feel that his greatest contribution was to the theory of probability. However, his interest in the latter subject stemmed from his realization of the need for a clear statistical method of analysis of data— at that time, travel-time readings from seismological stations across the world. He also made contributions to astronomy, fluid dynamics, meteorology, botany, psychol ogy, and photography. Perhaps one can identify Jeffreys’s principal interests from three major books that he wrote. His mathematical skills are displayed in Methods of Mathematical Physics, which he wrote with his wife Bertha Swirles Jeffreys and which was first published in 1946 and went through three editions. His Theory o f Probability, published in 1939 and also running to three editions, espoused Bayesian statistics, which were very unfashionable at the time but which have been taken up since by others and shown to be extremely powerful for the analysis of data and, in particular, image enhancement. However, the book for which he is probably best known is The Earth, Its Origin, History and Physical Consti tution, a broad-ranging account based on observations analyzed with care, using mathematics as a tool. Jeffreys’s scientific method (now known as Inverse Theory) was a logical process, clearly stated in another of his books, Scientific Inference. -
Rothamsted in the Making of Sir Ronald Fisher Scd FRS
Rothamsted in the Making of Sir Ronald Fisher ScD FRS John Aldrich University of Southampton RSS September 2019 1 Finish 1962 “the most famous statistician and mathematical biologist in the world” dies Start 1919 29 year-old Cambridge BA in maths with no prospects takes temporary job at Rothamsted Experimental Station In between 1919-33 at Rothamsted he makes a career for himself by creating a career 2 Rothamsted helped make Fisher by establishing and elevating the office of agricultural statistician—a position in which Fisher was unsurpassed by letting him do other things—including mathematical statistics, genetics and eugenics 3 Before Fisher … (9 slides) The problems were already there viz. o the relationship between harvest and weather o design of experimental trials and the analysis of their results Leading figures in agricultural science believed the problems could be treated statistically 4 Established in 1843 by Lawes and Gilbert to investigate the effective- ness of fertilisers Sir John Bennet Lawes Sir Joseph Henry Gilbert (1814-1900) land-owner, fertiliser (1817-1901) professional magnate and amateur scientist scientist (chemist) In 1902 tired Rothamsted gets make-over when Daniel Hall becomes Director— public money feeds growth 5 Crops and weather and experimental trials Crops & the weather o examined by agriculturalists—e.g. Lawes & Gilbert 1880 o subsequently treated more by meteorologists Experimental trials a hotter topic o treated in Journal of Agricultural Science o by leading figures Wood of Cambridge and Hall -
A Parsimonious Tour of Bayesian Model Uncertainty
A Parsimonious Tour of Bayesian Model Uncertainty Pierre-Alexandre Mattei Université Côte d’Azur Inria, Maasai project-team Laboratoire J.A. Dieudonné, UMR CNRS 7351 e-mail: [email protected] Abstract: Modern statistical software and machine learning libraries are enabling semi-automated statistical inference. Within this context, it appears eas- ier and easier to try and fit many models to the data at hand, thereby reversing the Fisherian way of conducting science by collecting data after the scientific hypothesis (and hence the model) has been determined. The renewed goal of the statistician becomes to help the practitioner choose within such large and heterogeneous families of models, a task known as model selection. The Bayesian paradigm offers a systematized way of as- sessing this problem. This approach, launched by Harold Jeffreys in his 1935 book Theory of Probability, has witnessed a remarkable evolution in the last decades, that has brought about several new theoretical and methodological advances. Some of these recent developments are the focus of this survey, which tries to present a unifying perspective on work carried out by different communities. In particular, we focus on non-asymptotic out-of-sample performance of Bayesian model selection and averaging tech- niques, and draw connections with penalized maximum likelihood. We also describe recent extensions to wider classes of probabilistic frameworks in- cluding high-dimensional, unidentifiable, or likelihood-free models. Contents 1 Introduction: collecting data, fitting many models . .2 2 A brief history of Bayesian model uncertainty . .2 3 The foundations of Bayesian model uncertainty . .3 3.1 Handling model uncertainty with Bayes’s theorem . -
Basic Statistics
Statistics Kristof Reid Assistant Professor Medical University of South Carolina Core Curriculum V5 Financial Disclosures • None Core Curriculum V5 Further Disclosures • I am not a statistician • I do like making clinical decisions based on appropriately interpreted data Core Curriculum V5 Learning Objectives • Understand why knowing statistics is important • Understand the basic principles and statistical test • Understand common statistical errors in the medical literature Core Curriculum V5 Why should I care? Indications for statistics Core Curriculum V5 47% unable to determine study design 31% did not understand p values 38% did not understand sensitivity and specificity 83% could not use odds ratios Araoye et al, JBJS 2020;102(5):e19 Core Curriculum V5 50% (or more!) of clinical research publications have at least one statistical error Thiese et al, Journal of thoracic disease, 2016-08, Vol.8 (8), p.E726-E730 Core Curriculum V5 17% of conclusions not justified by results 39% of studies used the wrong analysis Parsons et al, J Bone Joint Surg Br. 2011;93-B(9):1154-1159. Core Curriculum V5 Are these two columns different? Column A Column B You have been asked by your insurance carrier 19 10 12 11 to prove that your total hip patient outcomes 10 9 20 19 are not statistically different than your competitor 10 17 14 10 next door. 21 19 24 13 Column A is the patient reported score for you 12 18 and column B for your competitor 17 8 12 17 17 10 9 9 How would you prove you are 24 10 21 9 18 18 different or better? 13 4 18 8 12 14 15 15 14 17 Core Curriculum V5 Are they still the same? Column A: Mean=15 Column B: Mean=12 SD=4 Core Curriculum V5 We want to know if we're making a difference Core Curriculum V5 What is statistics? a. -
School of Social Sciences Economics Division University of Southampton Southampton SO17 1BJ, UK
School of Social Sciences Economics Division University of Southampton Southampton SO17 1BJ, UK Discussion Papers in Economics and Econometrics Professor A L Bowley’s Theory of the Representative Method John Aldrich No. 0801 This paper is available on our website http://www.socsci.soton.ac.uk/economics/Research/Discussion_Papers ISSN 0966-4246 Key names: Arthur L. Bowley, F. Y. Edgeworth, , R. A. Fisher, Adolph Jensen, J. M. Keynes, Jerzy Neyman, Karl Pearson, G. U. Yule. Keywords: History of Statistics, Sampling theory, Bayesian inference. Professor A. L. Bowley’s Theory of the Representative Method * John Aldrich Economics Division School of Social Sciences University of Southampton Southampton SO17 1BJ UK e-mail: [email protected] Abstract Arthur. L. Bowley (1869-1957) first advocated the use of surveys–the “representative method”–in 1906 and started to conduct surveys of economic and social conditions in 1912. Bowley’s 1926 memorandum for the International Statistical Institute on the “Measurement of the precision attained in sampling” was the first large-scale theoretical treatment of sample surveys as he conducted them. This paper examines Bowley’s arguments in the context of the statistical inference theory of the time. The great influence on Bowley’s conception of statistical inference was F. Y. Edgeworth but by 1926 R. A. Fisher was on the scene and was attacking Bayesian methods and promoting a replacement of his own. Bowley defended his Bayesian method against Fisher and against Jerzy Neyman when the latter put forward his concept of a confidence interval and applied it to the representative method. * Based on a talk given at the Sample Surveys and Bayesian Statistics Conference, Southampton, August 2008. -
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. -
Principles of the Design and Analysis of Experiments
A FRESH LOOK AT THE BASIC PRINCIPLES OF THE DESIGN AND ANALYSIS OF EXPERIMENTS F. YATES ROTHAMSTED EXPERIMENTAL STATION, HARPENDEN 1. Introduction When Professor Neyman invited me to attend the Fifth Berkeley Symposium, and give a paper on the basic principles of the design and analysis of experiments, I was a little hesitant. I felt certain that all those here must be thoroughly conversant with these basic principles, and that to mull over them again would be of little interest. This, however, is the first symposium to be held since Sir Ronald Fisher's death, and it does therefore seem apposite that a paper discussing some aspect of his work should be given. If so, what could be better than the design and analysis of experiments, which in its modern form he created? I do not propose today to give a history of the development of the subject. This I did in a paper presented in 1963 to the Seventh International Biometrics Congress [14]. Instead I want to take a fresh look at the logical principles Fisher laid down, and the action that flows from them; also briefly to consider certain modern trends, and see how far they are really of value. 2. General principles Fisher, in his first formal exposition of experimental design [4] laid down three basic principles: replication; randomization; local control. Replication and local control (for example, arrangement in blocks or rows and columns of a square) were not new, but the idea of assigning the treatments at random (subject to the restrictions imposed by the local control) was novel, and proved to be a most fruitful contribution. -
Mathematical Statistics and the ISI (1885-1939)
Mathematical Statistics and the ISI (1885-1939) Stephen M. Stigler University of Chicago, Department of Statistics 5734 University Avenue Chicago IL 60637, USA [email protected] 1. Introduction From its inception in 1885, the International Statistical Institute took all of statistics as its province. But the understood meaning of “statistics” has undergone significant change over the life of the Institute, and it is of interest to inquire how that change, whether evolutionary or revolutionary, has been reflected in the business of the Institute, in its meetings and its membership. I propose to consider this question in regard to one particular dimension, that of mathematical statistics, and one particular period, that from the rebirth of international statistics in 1885 until the eve of World War II. Mathematical statistics has been a recognized division of statistics at least since the 1860s, when Quetelet at the Congress in the Hague admonished the assembled statisticians not to divide the école mathématique from the école historique, but to take statistics whole, to open the doors to statistics in the broadest sense in all its flavors (Stigler, 2000). But even with this recognition, mathematical statistics only emerged as a discipline later. I have argued elsewhere that this emergence can be dated to the year 1933, and even if one thinks that date absurdly precise, there was a distinct and palpable change that was evident around that time (Stigler, 1999, Ch. 8). It was then that changes initiated a half-century earlier by Galton and Edgeworth and developed subsequently by Karl Pearson and Udny Yule, were seized upon and transformed by Ronald Fisher and Jerzy Neyman and Egon S. -
Bayesian Data-Analysis Toolbox User Manual
Bayesian Data-Analysis Toolbox Release 4.23, Manual Version 3 G. Larry Bretthorst Biomedical MR Laboratory Washington University School Of Medicine, Campus Box 8227 Room 2313, East Bldg., 4525 Scott Ave. St. Louis MO 63110 http://bayes.wustl.edu Email: [email protected] September 18, 2018 Appendix C Thermodynamic Integration Thermodynamic Integration is a technique used in Bayesian probability theory to compute the posterior probability for a model. As a reminder, if a set of m models is designated as M 2 f1; 2; : : : ; mg, then one can compute the posterior probability for the models by an application of Bayes' Theorem [1] P (MjDI) / P (MjI)P (DjMI) (C.1) where we have dropped a normalization constant, M = 1 means we are computing the posterior probability for model 1, M = 2 the probability for model 2, etc. The three terms in this equation, going from left to right, are the posterior probability for the model indicator given the data and the prior information, P (MjDI), the prior probability for the model given only the prior information, P (MjI), and the marginal direct probability for the data given the model and the prior information, P (DjMI). The marginal direct probability for the data given a model can be computed from the joint posterior probability for the data and the model parameters, which we will call Ω, given the Model and the prior information Z P (DjMI) = dΩP (ΩjMI)P (DjΩMI): (C.2) Unfortunately, the integrals on the right-hand side of this equation can be very high dimensional. Consequently, although we know exactly what calculation must be done to compute the marginal direct probability, in most applications the integrals are not tractable.