/docs/statistics/ Directory Listing



  • 2020-blake.pdf: ⁠, Khandis R. Blake, Steven Gangestad (2020-03-25; backlinks):

    The replication crisis has seen increased focus on best practice techniques to improve the reliability of scientific findings. What remains elusive to many researchers and is frequently misunderstood is that predictions involving interactions dramatically affect the calculation of statistical power. Using recent papers published in Personality and Social Psychology Bulletin (PSPB), we illustrate the pitfalls of improper power estimations in studies where attenuated interactions are predicted. Our investigation shows why even a programmatic series of 6 studies employing 2×2 designs, with samples exceeding n = 500, can be woefully underpowered to detect genuine effects. We also highlight the importance of accounting for error-prone measures when estimating effect sizes and calculating power, explaining why even positive results can mislead when power is low. We then provide five guidelines for researchers to avoid these pitfalls, including cautioning against the heuristic that a series of underpowered studies approximates the credibility of one well-powered study.

    [Keywords: statistical power, effect size, fertility, ovulation, interaction effects]

  • 2020-raudenbush.pdf: ⁠, Stephen W. Raudenbush, Daniel Schwartz (2020-03-01):

    Education research has experienced a methodological renaissance over the past two decades, with a new focus on large-scale randomized experiments. This wave of experiments has made education research an even more exciting area for statisticians, unearthing many lessons and challenges in experimental design, causal inference, and statistics more broadly. Importantly, educational research and practice almost always occur in a multilevel setting, which makes the statistics relevant to other fields with this structure, including social policy, health services research, and clinical trials in medicine. In this article we first briefly review the history that led to this new era in education research and describe the design features that dominate the modern large-scale educational experiments. We then highlight some of the key statistical challenges in this area, including endogeneity of design, heterogeneity of treatment effects, noncompliance with treatment assignment, mediation, generalizability, and spillover. Though a secondary focus, we also touch on promising trial designs that answer more nuanced questions, such as the SMART design for studying dynamic treatment regimes and factorial designs for optimizing the components of an existing treatment.

  • 2019-panaretos.pdf: ⁠, Victor M. Panaretos, Yoav Zemel (2019-01-01):

    Wasserstein distances are metrics on probability distributions inspired by the problem of optimal mass transportation. Roughly speaking, they measure the minimal effort required to reconfigure the probability mass of one distribution in order to recover the other distribution. They are ubiquitous in mathematics, with a long history that has seen them catalyze core developments in analysis, optimization, and probability. Beyond their intrinsic mathematical richness, they possess attractive features that make them a versatile tool for the statistician: They can be used to derive weak convergence and convergence of moments, and can be easily bounded; they are well-adapted to quantify a natural notion of perturbation of a probability distribution; and they seamlessly incorporate the geometry of the domain of the distributions in question, thus being useful for contrasting complex objects. Consequently, they frequently appear in the development of statistical theory and inferential methodology, and they have recently become an object of inference in themselves. In this review, we provide a snapshot of the main concepts involved in Wasserstein distances and optimal transportation, and a succinct overview of some of their many statistical aspects.

  • 2019-mclachlan.pdf: ⁠, Geoffrey J. McLachlan, Sharon X. Lee, Suren I. Rathnayake (2019-01-01):

    The important role of finite mixture models in the statistical analysis of data is underscored by the ever-increasing rate at which articles on mixture applications appear in the statistical and general scientific literature. The aim of this article is to provide an up-to-date account of the theory and methodological developments underlying the applications of finite mixture models. Because of their flexibility, mixture models are being increasingly exploited as a convenient, semiparametric way in which to model unknown distributional shapes. This is in addition to their obvious applications where there is group-structure in the data or where the aim is to explore the data for such structure, as in a cluster analysis. It has now been three decades since the publication of the monograph by McLachlan & Basford (1988) with an emphasis on the potential usefulness of mixture models for inference and clustering. Since then, mixture models have attracted the interest of many researchers and have found many new and interesting fields of application. Thus, the literature on mixture models has expanded enormously, and as a consequence, the bibliography here can only provide selected coverage.

  • 2017-mosenia.pdf: “PinMe_PrateekCommentsIncluded_Aug_21.pdf”⁠, arsalan (backlinks)

  • 2016-hildebrandt.pdf: ⁠, Andrea Hildebrandt, Oliver Lüdtke, Alexander Robitzsch, Christopher Sommer, Oliver Wilhelm (2016-04-06):

    Using an empirical data set, we investigated variation in factor model parameters across a continuous moderator variable and demonstrated three modeling approaches: multiple-group mean and covariance structure (MGMCS) analyses, local structural equation modeling (LSEM), and moderated factor analysis (MFA). We focused on how to study variation in factor model parameters as a function of continuous variables such as age, socioeconomic status, ability levels, acculturation, and so forth. Specifically, we formalized the LSEM approach in detail as compared with previous work and investigated its statistical properties with an analytical derivation and a simulation study. We also provide code for the easy implementation of LSEM. The illustration of methods was based on cross-sectional cognitive ability data from individuals ranging in age from 4 to 23 years. Variations in factor loadings across age were examined with regard to the age differentiation hypothesis. LSEM and MFA converged with respect to the conclusions. When there was a broad age range within groups and varying relations between the indicator variables and the common factor across age, MGMCS produced distorted parameter estimates. We discuss the pros of LSEM compared with MFA and recommend using the two tools as complementary approaches for investigating moderation in factor model parameters.

    [Keywords: Local structural equation model, moderated factor analysis, multiple-group mean and covariance structures, age differentiation of cognitive abilities, WJ-III tests of cognitive abilities]

  • 2016-cook.pdf: “Do scholars follow Betteridge’s Law? The use of questions in journal article titles”⁠, James M. Cook, Dawn Plourde (backlinks)

  • 2015-gaukler.pdf: ⁠, James S. Ruff, Tessa Galland, Kirstie A. Kandaris, Tristan K. Underwood, Nicole M. Liu, Elizabeth L. Young, Linda C. Morrison, Garold S. Yost, Wayne K. Potts (2015; backlinks):

    Paroxetine is a selective serotonin reuptake inhibitor (SSRI) that is currently available on the market and is suspected of causing congenital malformations in babies born to mothers who take the drug during the first trimester of pregnancy. We utilized organismal performance assays (OPAs), a novel toxicity assessment method, to assess the safety of paroxetine during pregnancy in a rodent model. OPAs utilize genetically diverse wild mice (Mus musculus) to evaluate competitive performance between experimental and control animals as they compete amongst each other for limited resources in semi-natural enclosures. Performance measures included reproductive success, male competitive ability and survivorship. Paroxetine-exposed males weighed 13% less, had 44% fewer offspring, dominated 53% fewer territories and experienced a 2.5-fold increased trend in mortality, when compared with controls. Paroxetine-exposed females had 65% fewer offspring early in the study, but rebounded at later time points. In cages, paroxetine-exposed breeders took 2.3 times longer to produce their first litter and pups of both sexes experienced reduced weight when compared with controls. Low-dose paroxetine-induced health declines detected in this study were undetected in preclinical trials with dose 2.5-8 times higher than human therapeutic doses. These data indicate that OPAs detect phenotypic adversity and provide unique information that could useful towards safety testing during pharmaceutical development.

    [Keywords: intraspecific competition, pharmacodynamics, reproductive success, semi-natural enclosures, SSRI, toxicity assessment.]

  • 2014-copss-pastpresentfuturestatistics.pdf: ⁠, Xihong Lin, Christian Genest, David L. Banks, Geert Molenberghs, David W. Scott, Jane-Ling Wang (2014-03-26; backlinks):

    Past, Present, and Future of Statistical Science was commissioned in 2013 by the Committee of Presidents of Statistical Societies (COPSS) to celebrate its 50th anniversary and the International Year of Statistics. COPSS consists of five charter member statistical societies in North America and is best known for sponsoring prestigious awards in statistics, such as the COPSS Presidents’ award. Through the contributions of a distinguished group of 50 statisticians who are past winners of at least one of the five awards sponsored by COPSS, this volume showcases the breadth and vibrancy of statistics, describes current challenges and new opportunities, highlights the exciting future of statistical science, and provides guidance to future generations of statisticians. The book is not only about statistics and science but also about people and their passion for discovery. Distinguished authors present expository articles on a broad spectrum of topics in statistical education, research, and applications. Topics covered include reminiscences and personal reflections on statistical careers, perspectives on the field and profession, thoughts on the discipline and the future of statistical science, and advice for young statisticians. Many of the articles are accessible not only to professional statisticians and graduate students but also to undergraduate students interested in pursuing statistics as a career and to all those who use statistics in solving real-world problems. A consistent theme of all the articles is the passion for statistics enthusiastically shared by the authors. Their success stories inspire, give a sense of statistics as a discipline, and provide a taste of the exhilaration of discovery, success, and professional accomplishment.

    “This collection of reminiscences, musings on the state of the art, and advice for young statisticians makes for compelling reading. There are 52 contributions from eminent statisticians who have won a Committee of Presidents of Statistical Societies award. Each is a short, focused chapter and so one could even say this is ideal bedtime (or coffee break) reading. Anyone interested in the history of statistics will know that much has been written about the early days but little about the field since the Second World War. This book goes some way to redress this and is all the more valuable for coming from the horse’s mouth…the closing chapter, the shortest of all, from Brad Efron: a list of”thirteen rules for giving a really bad talk“. This made me laugh out loud and should be posted on the walls of all conferences. I shall leave the final word to Peter Bickel:”We should glory in this time when statistical thinking pervades almost every field of endeavor. It is really a lot of fun."

    ―Robert Grant, in Significance, April 2017

    The History of COPSS: “A brief history of the Committee of Presidents of Statistical Societies (COPSS)”, Ingram Olkin

    Reminiscences and Personal Reflections on Career Paths

    “Reminiscences of the Columbia University Department of Mathematical Statistics in the late 1940s”, Ingram Olkin·“A career in statistics”, Herman Chernoff·“. . . how wonderful the field of statistics is . . .”, David R. Brillinger·“An unorthodox journey to statistics: Equity issues, remarks on multiplicity”, Juliet Popper Shaffer·“Statistics before and after my COPSS Prize”, Peter J. Bickel·“The accidental biostatistics professor”, Donna Brogan·“Developing a passion for statistics”, Bruce G. Lindsay·“Reflections on a statistical career and their implications”, R. Dennis Cook·“Science mixes it up with statistics”, Kathryn Roeder·“Lessons from a twisted career path”, Jeffrey S. Rosenthal·“Promoting equity”, Mary Gray

    Perspectives on the Field and Profession

    “Statistics in service to the nation”, Stephen E. Fienberg·“Where are the majors?”, Iain M. Johnstone·“We live in exciting times”, Peter Hall·“The bright future of applied statistics”, Rafael A. Irizarry·“The road travelled: From a statistician to a statistical scientist”, Nilanjan Chatterjee·“Reflections on a journey into statistical genetics and genomics”, Xihong Lin·“Reflections on women in statistics in Canada”, Mary E. Thompson·“The whole women thing”, Nancy Reid·“Reflections on diversity”, Louise Ryan

    Reflections on the Discipline

    “Why does statistics have two theories?”, Donald A. S. Fraser·“Conditioning is the issue”, James O. Berger·“Statistical inference from a Dempster-Shafer perspective”, Arthur P. Dempster·“Nonparametric Bayes”, David B. Dunson·“How do we choose our default methods?”, Andrew Gelman·“Serial correlation and Durbin-Watson bounds”, T. W. Anderson·“A non-asymptotic walk in probability and statistics”, Pascal Massart·“The past’s future is now: What will the present’s future bring?”, Lynne Billard·“Lessons in biostatistics”, Norman E. Breslow·“A vignette of discovery”, Nancy Flournoy·“Statistics and public health research”, Ross L. Prentice·“Statistics in a new era for finance and health care”, Tze Leung Lai·“Meta-analyses: Heterogeneity can be a good thing”, Nan M. Laird·“Good health: Statistical challenges in personalizing disease prevention”, Alice S. Whittemore·“Buried treasures”, Michael A. Newton·“Survey sampling: Past controversies, current orthodoxy, future paradigms”, Roderick J. A. Little·“Environmental informatics: Uncertainty quantification in the environmental sciences”, Noel A. Cressie·“A journey with statistical genetics”, Elizabeth Thompson·“Targeted learning: From MLE to TMLE”, Mark van der Laan·“Statistical model building, machine learning, and the ah-ha moment”, Grace Wahba·“In praise of sparsity and convexity”, Robert J. Tibshirani·“Features of Big Data and sparsest solution in high confidence set”, Jianqing Fan·“Rise of the machines”, Larry A. Wasserman·“A trio of inference problems that could win you a Nobel Prize in statistics (if you help fund it)”, Xiao-Li Meng

    Advice for the Next Generation

    “Inspiration, aspiration, ambition”, C. F. Jeff Wu·“Personal reflections on the COPSS Presidents’ Award”, Raymond J. Carroll·“Publishing without perishing and other career advice”, Marie Davidian·“Converting rejections into positive stimuli”, Donald B. Rubin·“The importance of mentors”, Donald B. Rubin·“Never ask for or give advice, make mistakes, accept mediocrity, enthuse”, Terry Speed·“Thirteen rules”, Bradley Efron

  • 2013-hood.pdf: “Psychological Measurement and Methodological Realism”⁠, S. Brian Hood

  • 2012-decrouez.pdf: ⁠, Geoffrey Decrouez, Andrew P. Robinson (2012-09-01):

    Confidence intervals for the difference of two binomial proportions are well known, however, confidence intervals for the weighted sum of two binomial proportions are less studied. We develop and compare 7 methods for constructing confidence intervals for the weighted sum of 2 independent binomial proportions. The interval estimates are constructed by inverting the Wald test, the score test and the Likelihood ratio test. The weights can be negative, so our results generalize those for the difference between two independent proportions. We provide a numerical study that shows that these confidence intervals based on large-sample approximations perform very well, even when a relatively small amount of data is available. The intervals based on the inversion of the score test showed the best performance. Finally, we show that as for the difference of two binomial proportions, adding four pseudo-outcomes to the Wald interval for the weighted sum of two binomial proportions improves its coverage substantially, and we provide a justification for this correction.

    [Keywords: border security, leakage survey, likelihood ratio test, quarantine inspection, score test, small sample, sum of proportions, Wald test]

  • 2010-stigler.pdf: “The Changing History of Robustness”⁠, Stephen M. Stigler

  • 2010-li.pdf: “A new car-following model yielding log-normal type headways distributions”⁠, Li Li (李 力), Wang Fa (王 法), Jiang Rui (姜 锐), Hu Jian-Ming (胡坚明), Ji Yan (吉 岩) (backlinks)

  • 2005-gorsuch.pdf: “Continuous Parameter Estimation Model: Expanding the Standard Statistical Paradigm”⁠, Richard L. Gorsuch

  • 2001-fienberg.pdf: “William Sealy Gosset”⁠, Stephen E. Fienberg, Nicole Lazar

  • 1997-muzaik.pdf: “There Is a Time and a Place for Significance Testing”⁠, Stanley A. Mulaik, Nambury S. Raju, Richard A. Harshman (backlinks)

  • 1996-teahan.pdf: “THE ENTROPY OF ENGLISH USING PPM-BASED MODELS - Data Compression Conference, 1996. DCC '96. Proceedings” (backlinks)

  • 1994-flury.pdf: ⁠, Bernhard W. Flury, Martin J. Schmid, A. Narayanan (1994-03-01; backlinks):

    In multivariate discrimination of several normal populations, the optimal classification procedure is based on quadratic ⁠.

    We compare expected error rates of the quadratic classification procedure if the covariance matrices are estimated under the following 4 models: (1) arbitrary covariance matrices, (2) common principal components, (3) proportional covariance matrices, and (4) identical covariance matrices.

    Using Monte Carlo simulation to estimate expected error rates, we study the performance of the 4 discrimination procedures for 5 different parameter setups corresponding to “standard” situations that have been used in the literature. The procedures are examined for sample sizes ranging from 10 to 60, and for 2 to 4 groups.

    Our results quantify the extent to which a parsimonious method reduces error rates, and demonstrate that choosing a simple method of discrimination is often beneficial even if the underlying model assumptions are wrong.

    [Keywords: Common ⁠, Linear Discriminant Function, Monte Carlo simulation, Proportional Covariance Matrices]

  • 1992-john.pdf: “Statistics as Rhetoric in Psychology”⁠, I. D. John

  • 1983-kolmogorov.pdf: “1_1.tif”

  • 1982-loftus-essenceofstatistics.pdf: “Essence of Statistics (Second Edition)”⁠, Geoffry R. Loftus, Elizabeth F. Loftus (backlinks)

  • 1981-mackenzie-statisticsinbritain18651930.pdf: “Statistics in Britain 1865-1930: The Social Construction of Scientific Knowledge”⁠, MacKenzie, Donald A.

  • 1976-savage.pdf: “On Rereading R. A. Fisher [Fisher Memorial lecture, with comments]”⁠, Leonard J. Savage, John Pratt, Bradley Efron, Churchill Eisenhart, Bruno de Finetti, D. A. S. Fraser, V. P. Godambe, I. J. Good, O. Kempthorne, Stephen M. Stigler, I. Richard Savage (backlinks)

  • 1975-swoyer.pdf: “Theory Confirmation in Psychology”⁠, Chris Swoyer, Thomas C. Monson (backlinks)

  • 1975-oakes.pdf: “On the alleged falsity of the null hypothesis”⁠, William F. Oakes (backlinks)

  • 1973-keuth.pdf: “On Prior Probabilities of Rejecting Statistical Hypotheses”⁠, Herbert Keuth (backlinks)

  • 1965-kahneman.pdf: “Control of spurious association and the reliability of the controlled variable”⁠, Daniel Kahneman (backlinks)

  • 1961-stanley.pdf: “205_1.tif”

  • 1960-blalock-socialstatistics.pdf: “Social Statistics”⁠, Hubert M. Blalock, Jr.

  • 1958-welch.pdf: “'Student' and Small Sample Theory”⁠, B. L. Welch

  • 1951-yates.pdf: “The Influence of 'Statistical Methods for Research Workers' on the Development of the Science of Statistics”⁠, Francis Yates (backlinks)

  • 1942-thorndike.pdf: “85_1.tif”⁠, Robert L. Thorndike (backlinks)

  • 1940-dantzig.pdf: “On the Non-Existence of Tests of "Student's" Hypothesis Having Power Functions Independent of σ”⁠, George B. Dantzig

  • 1938-mahalanobis.pdf: “Professor Ronald Aylmer Fisher [profile]”⁠, P. C. Mahalanobis

  • 1936-stouffer.pdf: ⁠, Samuel A. Stouffer (1936; backlinks):

    It is not generally recognized that such an analysis [using regression] assumes that each of the variables is perfectly measured, such that a second measure X’i, of the variable measured by Xi, has a correlation of unity with Xi. If some of the measures are more accurate than others, the analysis is impaired [by measurement error]. For example, the sociologist may have a problem in which an index of economic status and an index of nativity are independent variables. What is the effect, if the index of economic status is much less satisfactory than the index of nativity? Ordinarily, the effect will be to underestimate the [coefficient] of the less adequately measured variable and to overestimate the [coefficient] of the more adequately measured variable.

    If either the reliability or validity of an index is in question, at least two measures of the variable are required to permit an evaluation. The purpose of this paper is to provide a logical basis and a simple arithmetical procedure (a) for measuring the effect of the use of 2 indexes, each of one or more variables, in partial and multiple correlation analysis and (b) for estimating the likely effect if 2 indexes, not available, could be secured.

  • 1934-wright.pdf: “The Method of Path Coefficients”⁠, Sewall Wright

  • 1910-spearman.pdf: “Correlation Calculated from Faulty Data”⁠, Charles Spearman

  • 1910-brown.pdf: “Some Experimental Results in the Correlation of Mental Abilities”⁠, William Brown

  • 2013-google.csv (backlinks)

  • 2013-google-index.csv

  • 2012-silver-thesignalandthenoise-excerpts.pdf (backlinks)

  • 2003-zuehlke.pdf (backlinks)

  • 2001-thompson.html (backlinks)

  • 2001-preston-demography.pdf

  • 2001-francesrichard-obsessivegeneroustowardadiagramofmarklombardi.html

  • 1976-efron.pdf (backlinks)

  • 1973-meshalkin-collectionofproblemsinprobabilitytheory.pdf (backlinks)

  • 1964-goldman.pdf (backlinks)

  • 1957-feller-anintroductiontoprobabilitytheoryanditsapplications.pdf (backlinks)

  • 1955-duncan.pdf

  • 1930-hotelling.pdf

  • 1926-yule.pdf