Alan Agresti Personal Home Page

Alan Agresti
Distinguished Professor Emeritus

Department of Statistics
University of Florida
Gainesville, FL 32611-8545

E-MAIL: aa "at" stat "dot" ufl "dot" edu

I was employed by the University of Florida from 1972-2010. I have also had visiting professor positions at Harvard University (including fall semester each year 2008-2014), Imperial College (London), the London School of Economics, and shorter visiting positions at several universities including Florence and Padova (Italy), Hasselt (Belgium), Paris VII, Boston University, and Oregon State. I have taught short courses in about 35 countries, such as at various universities each year since 1991 in Italy, where I became a citizen in 2017 to supplement my American citizenship.

Honors

Honorary doctorate, De Montfort University (Leicester, U.K.), 1999
Statistician of the Year, Chicago chapter of American Statistical Association, 2003
Recipient of the first Herman Callaert Leadership Award in Statistical Education and Dissemination, Hasselt University, Diepenbeek, Belgium, 2004
Fellow, American Statistical Association, 1990
Fellow, Institute of Mathematical Statistics, 2008
University of Florida Distinguished Professor, 2000-2010
University of Florida Research Foundation (UFRF) Professorship, 1997-2000
Excellence in Continuing Education Award from American Statistical Association, 2002
36th Annual Allen T. Craig lecturer, University of Iowa, 2006
Categorical Data Analysis & Friends workshop in my honor, University of Firenze, Italy, 2019
Keynote lectures at conferences include Swiss Statistical Society (1992), French Biometric Society (1992), Conference on Statistical Issues in Biopharmaceutical Environments (1999) in the UK, Army Conference on Applied Statistics (2002), CDC annual awards meeting (2003), Applied Statistics in Ireland (2004), Hawaii International Conference on Statistics, Mathematics, and Related Fields (2004), International Society of Clinical Biostatistics (2005) in Hungary, CompStat (2006) in Italy, Applied Statistics (2007) in Slovenia, Royal Statistical Society (2008) in UK, Colombian Statistics Symposium (2012), Portuguese-Galician Biometry Meeting (2013), New England Statistics Symposium (2014), and invited lectures and short courses in about 35 countries

Book Information and Supplemental Files

1. The textbook DS cover Foundations of Statistics for Data Scientists, with R and Python , written with Maria Kateri, has been published in November 2021 by CRC Press. Designed as a textbook for an introduction to mathematical statistics for students training to become data scientists, the book provides an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modelling. Compared to traditional math stat textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python. The book also introduces modern topics that do not normally appear in mathematical statistics texts but are highly relevant for data scientists, such as Bayesian inference, generalized linear models for non-normal responses (e.g., logistic regression and Poisson loglinear models), and regularized model fitting. It contains nearly 500 exercises. The book's website has expanded R, Python, and Matlab appendices and all data sets from the examples and exercises.

2. The book

An Introduction to Categorical Data Analysis, (Wiley, 2019) was recently published in its 3rd edition. This new edition shows how to do all analyses using R software and add some new material (e.g., Bayesian methods, classification and smoothing). This book, which presents a nontechnical introduction to topics such as logistic regression, is a lower-technical-level and shorter version of the "Categorical Data Analysis" text mentioned above. I've constructed a website for these texts that provides information about the use of Software for Categorical Data Analysis such as SAS, R and S-Plus, SPSS, Stata, and StatXact. Data files from the text are at data files for Intro CDA. They are also available at a Github site for CDA data files and at the Wiley companion website for Intro CDA. For some data files from the 2nd edition, click on data files for Intro CDA. For SAS files containing data sets from the 2nd edition, click on SAS data sets for Intro CDA. Here are some corrections for the 1st edition of this book, a pdf file of corrections for the 2nd edition, and a pdf file of corrections for the 3rd edition. For those who use R, a very useful and helpful R language compendium has been prepared by Professor Raymond Balise. Containing code and formulas to accompany this book, it can be found at Balise R compendium for Intro CDA. Its Github project repository is public at Balise Github R repository for Intro CDA.

3. The text
Foundations of Linear and Generalized Linear Models, published by Wiley in February 2015, presents an overview of the most commonly used statistical models by discussing the theory underlying the models and showing examples using R software. The book begins with the fundamentals of linear models, such as showing how least squares projects the data onto a model vector subspace and orthogonal decompositions of the data yield comparisons of models. The book then covers the theory of generalized linear models, with chapters on binomial and multinomial logistic regression for categorical data and Poisson and negative binomial loglinear models for count data. The book also introduces quasi-likelihood methods (such as generalized estimating equations), linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian linear and generalized linear modeling, and regularization methods for high-dimensional data. The book has more than 400 exercises. The book's website contains supplementary information, including data sets and corrections. Here is an interview about the book in the Wiley publication "Statistics Views." Here is a book review in the journal Biometrics,a book review in Biometrical Journal, and some reviews at Amazon.

4. The book

Strength in Numbers: The Rising of Academic Statistics Departments in the U.S., co-edited with Xiao-Li Meng, has been published by Springer (2012). This book has a chapter for each of about 40 Statistics and Biostatistics departments founded in the U.S. by the mid-1960s, describing the evolution of those departments and the faculty and students who worked in them. Included are about 200 historical photos. See the Springer site for other details.

5. The text
Categorical Data Analysis, 3rd Edition

is in its third edition (Wiley, 2013). I've constructed a Website for Categorical Data Analysis that provides datasets used for examples, solutions to some exercises, information about using R, SAS, Stata, and SPSS software for conducting the analyses in the text, a list of some typos and errors, and powerpoint slides. For users of R, Prof. Charles Geyer has prepared a CatDataAnalysis package for CDA at CRAN, with data files used in text examples and exercises. He also has a website with notes on CDA for the course he has taught at the University of Minnesota on categorical data analysis, with one section dealing with infinite ML estimates in canonical link models. Here is an interview that the Wiley publication "Statistics Views" conducted with me to mark the publication of the new edition. A website for second edition has some material for the 2nd edition. Dr. Laura Thompson has prepared a detailed manual on the use of R or S-Plus to conduct all the analyses in the 2nd edition. Here is a copy of this excellent resource: Laura Thompson R and S manual for CDA.

6. The text
Analysis of Ordinal Categorical Data

(Wiley, 1984) has been revised, and the second edition was published in 2010. My ordinal categorical website contains (1) data sets for some examples in the form of SAS programs for conducting the analyses, (2) examples of the use or R for fitting various ordinal models, (3) examples of the use of Joe Lang's mph.fit R function for various analyses in the book that are not easily conducted with SAS, Stata, SPSS, and standard functions in R, and (4) corrections of errors in early printings of the book (Please send me any that you notice). Here is the video for a half-day course I taught in 2020 for the Harvard School of Public Health on Modeling Ordinal Categorical Data.

7. The text >
Statistics: The Art and Science of Learning from Data (5th edition, Pearson, 2021) was written with Christine Franklin of the University of Georgia and Bernhard Klingenberg of Williams College. Professor Klingenberg has developed a wonderful set of applets and other resources for teaching from the book (see Art of Stat ). This text is designed for a one-term or two-term undergraduate course or a high school AP course on an introduction to statistics, presented with a conceptual approach. The link Table of Contents has a Table of Contents and information about the book. Many supplemental materials are available from Pearson, including an annotated instructor's edition, a lab workbook, videotaped lectures, and software supplements. Contact Ms. Suzanna Bainbridge, the Acquisitions Editor for Statistics at Pearson Education, for details (suzanna.bainbridge@Pearson.com). An Italian translation of the 3rd edition is now available, thanks to Giuseppe Espa, Rocco Micciolo, Diego Giuliani and Maria Michela Dickson at the University of Trento.

8. The book

Statistical Methods for the Social Sciences, (5th edition, Pearson, 2018; 4th edition, by A. Agresti and B. Finlay, published 2009) is designed for a two-semester sequence. The book begins with the basics of statistical description and inference, and the second half concentrates on regression methods, including multiple regression, ANOVA and repeated measures ANOVA, analysis of covariance, logistic regression, and generalized linear models. The new edition adds R and Stata for software examples as well as introductions to new methodology such as multiple imputation for missing data, random effects modeling including multilevel models, robust regression, and the Bayesian approach to statistical inference. For applets used in some examples and exercises of the new edition, go to applets. These were designed by Bernhard Klingenberg for the text "Statistics: The Art and Science of Learning from Data" (4th and 5th ed.) by Agresti, Franklin, and Klingenberg. The book's website contains supplementary information, including data sets and corrections. Special directories also have data files in Stata format and in SPSS format. Some years ago, Jeffrey Arnold of the U of Washington kindly set up a R package at CRAN for R users to be able to access the datasets used in the 4th edition of this text. See R data files. He has also put the data files at a GitHub site, data files at GitHub. For examples of the use of the software Stata for various analyses for examples in the 4th edition of this text, see the useful site set up by the UCLA Statistical Computing Center. Thanks to Margaret Ross Tolbert for the cover art for the 5th edition. Margaret is an incredibly talented artist who has helped draw attention to the beauty but environmental degradation of the springs in north-central Florida (see www.margaretrosstolbert.com). I am also pleased to report (due to my partial Italian heritage) that there is also an Italian version of the first ten chapters of the 4th edition of this book (Statistica per le Scienze Sociali) and of the entire book (Metodi Statistici di Base e Avanzati per le scienze sociali) published by Pearson, and there is also a Portuguese version -- see "Metodos Estatisticos para as Ciencas Socias" at Portuguese SMSS -- and a Chinese version, and it is being translated into Spanish. I have developed Powerpoint files for lectures from Chapters 1-12 of this text that are available to instructors using this text. (Chapters 1-7 of these have also been translated into Spanish by Norma Leyva of Universidad Iberoamericana in Mexico.) Please contact me for details. Finally, here is a link to a workshop held by the Department of Sociology, Oxford University, in 2012 that discussed issues in the teaching of quantitative methods to social science students.

Short Courses

I have taught short courses on categorical data analysis topics for many universities, professional organizations, conferences, and companies, mainly in Europe and the U.S. These range in length from half-day to a week, most commonly one or two days on topics such as "Modeling Ordinal Categorical Responses," "Analyzing Clustered Categorical Data," "Introduction to Categorical Data Analysis," "Discrete Data Analysis," and "Generalized Linear Modeling." Here is the video for a half-day course I taught in 2020 for the Harvard School of Public Health on Modeling Ordinal Categorical Data.

History of Statistics at UF

Click on UF Statistics to download the chapter on the history of the University of Florida Statistics Department, taken from the book Strength in Numbers: The Rising of Academic Statistics Departments in the U.S. edited by Alan Agresti and Xiao-Li Meng. See UF Stat documents for other historical documents, including pictures (unfortunately, not updated for some time).

Research and Publications

My primary research interests have been in categorical data analysis.

Books

Foundations of Statistics for Data Scientists, with R and Python, with Maria Kateri, CRC Press (2021).

Foundations of Linear and Generalized Linear Models, Wiley (2015).

Strength in Numbers: The Rising of Academic Statistics Departments in the U.S., Springer (2012), co-edited with Xiao-Li Meng.

Statistics: The Art and Science of Learning from Data, 5th edition, Pearson (2021), with Chris Franklin and Bernhard Klingenberg.

Analysis of Ordinal Categorical Data, 2nd ed., Wiley (2010).

An Introduction to Categorical Data Analysis, 3rd ed., Wiley (2019).

Categorical Data Analysis, 3rd edition, Wiley (2013).

Statistical Methods for the Social Sciences, 5th edition, Pearson (2018) (4th edition 2009 with B. Finlay).

Some Articles

Bounds on the extinction time distribution of a branching process. Advances in Applied Probability, 6 (1974), 322-335. pdf file

On the extinction times of varying and random environment branching processes. Journal of Applied Probability, 12 (1975), 39-46. pdf file

The effect of category choice on some ordinal measures of association. Journal of the American Statistical Association, 71 (1976), 49-55.

Some exact conditional tests of independence for r x c cross-classification tables. (with D. Wackerly) Psychometrika, 42 (1977), 111-125. pdf file

Some considerations in measuring partial association for ordinal categorical data. Journal of the American Statistical Association, 72 (1977), 37-45.

A coefficient of multiple association based on ranks. Communications in Statistics, A6 (1977), 1341-1359.

Statistical analysis of qualitative variation. (with B. Agresti), Chapter 10, in Sociological Methodology (1978) ed. by K. F. Schuessler, Jossey-Bass Publ., 204-237.

Descriptive measures for rank comparisons of groups. Proceedings of the Social Statistics Section of the American Statistical Association, (1978), 585-590.

Exact conditional tests for cross-classifications: Approximation of attained significance level. (with D. Wackerly and J. Boyett), Psychometrika, 44 (1979), 75-83. pdf file

Measuring association and modelling relationships between interval and ordinal variables. (J. Schollenberger, A. Agresti, and D. Wackerly), Proceedings of the Social Statistics Section of the American Statistical Association, (1979), 624-626.

Generalized odds ratios for ordinal data. Biometrics, 36 (1980), 59-67. pdf file

A hierarchical system of interaction measures for multidimensional contingency tables. Journal of the Royal Statistical Society B, 43 (1981), 293-301.

Measures of nominal-ordinal association, Journal of the American Statistical Association, 76 (1981), 524-529. pdf file

Statistical fallacies. Encyclopedia of the Statistical Sciences, Vol. 3 (1983), John Wiley and Sons, 24-28.

Testing marginal homogeneity for ordinal categorical variables, Biometrics, 39, (1983), 505-510. pdf file

A survey of strategies for modelling cross-classifications having ordinal variables. Invited Essay Review in Journal of the American Statistical Association, 78 (1983), 184-198.

Association models for multidimensional cross-classifications of ordinal variables (with A. Kezouh), invited paper for issue on categorical data, Communications in Statistics, A12 (1983), 1261-1276.

A simple diagonals-parameter symmetry and quasisymmetry model, Statistics and Probability Letters, 1 (1983), 313-316. pdf file

The measurement of classification agreement: An adjustment to the Rand statistic for chance agreement (with L. Morey), Educational and Psychological Measurement, 44 (1984), 33-37.

Ordinal data. Encyclopedia of the Statistical Sciences, Vol. 6 (1985), John Wiley and Sons, 511-516.

Comparing mean ranks for repeated measures data (with J. Pendergast), Communications in Statistics, A15 (1986), 1417-1433. pdf file

A new model for ordinal pain data from a pharmaceutical study (with C. Chuang), Statistics in Medicine, 5 (1986), 15-20.

Applying R-squared type measures to ordered categorical data, Technometrics, 28 (1986), 133-138. pdf file

Models for the probability of concordance in cross-classification tables (with J. Schollenberger and D. Wackerly), Quality and Quantity (International Journal of Methodology), 21 (1987), 49-57. pdf file

Order-restricted score parameters in association models for contingency tables (with C. Chuang and A. Kezouh), Journal of the American Statistical Association, 82 (1987), 619-623.

Bayesian and maximum likelihood approaches to order-restricted inference for models for ordinal categorical data (with C. Chuang), pp. 6-27 in Advances in Order Restricted Statistical Inference, (1986), ed. by R. Dykstra, T. Robertson, and F.T. Wright, New York: Springer-Verlag.

An empirical investigation of some effects of sparseness in contingency tables (with M. Yang), Computational Statistics & Data Analysis, 5 (1987), 9-21.

A model for agreement between ratings on an ordinal scale, Biometrics, 44 (1988), 539-548.

Logit models for repeated ordered categorical response data, invited paper for Proceedings of 13th SAS Users Group Conference, (1988), 997-1005.

An agreement model with Kappa as parameter, Statistics and Probability Letters, 7 (1989), 271-273.

Model-based Bayesian methods for estimating cell proportions in cross-classification tables having ordered categories (with C. Chuang), Computational Statistics \& Data Analysis, 7 (1989), 245-258.

A tutorial on modeling ordered categorical response data, Psychological Bulletin, 105 (1989), 290-301.

A survey of models for repeated ordered categorical response data, Statistics in Medicine, 8 (1989), 1209-1224.

Exact inference for contingency tables with ordered categories (with C. Mehta and N. Patel), Journal of the American Statistical Association, 85 (1990), 453-458.

Analysis of sparse repeated categorical measurement data (with S. Lipsitz and J. B. Lang), SAS Users Group International Conference Proceedings, 1991, 1452-1460.

Parsimonious latent class models for ordinal variables, invited paper in Proceedings of 6th International Workshop on Statistical Modeling, (1991), 1-12, Utrecht, Netherlands.

Analysis of ordinal paired comparison data, Journal of the Royal Statistical Society C (Applied Statistics), 41 (1992), 287-297. pdf file

Loglinear modeling of pairwise interobserver agreement on a categorical scale (M. P. Becker and A. Agresti), Statistics in Medicine, 11 (1992), 101-114.

Comparing marginal distributions of large, sparse contingency tables (with S. Lipsitz and J. B. Lang), Computational Statistics and Data Analysis, 14 (1992), 55-73.

A survey of exact inference for contingency tables (with discussion), Statistical Science, 7 (1992), 131-177. pdf file

Quasi-symmetric latent class models, with application to rater agreement (with J. Lang), Biometrics, 49 (1993), 131-140. pdf file

Modeling patterns of agreement and disagreement, Statistical Methods in Medical Research, 1 (1992), 201-218.

Computing conditional maximum likelihood estimates for generalized Rasch models using simple loglinear models with diagonals parameters, Scandinavian Journal of Statistics, 20 (1993), 63-72.

Some empirical comparisons of exact, modified exact, and higher-order asymptotic tests of independence for ordered categorical variables (with J. Lang and C. Mehta), Communications in Statistics, Simulation and Computation, 22 (1993), 1-18.

A proportional odds model with subject-specific effects for repeated ordered categorical responses (with J. Lang), Biometrika, 80 (1993), 527-534. pdf file

Distribution-free fitting of logit models with random effects for repeated categorical responses, Statistics in Medicine, 12 (1993), 1969-1987.

Simultaneously modeling joint and marginal distributions of multivariate categorical responses (J. Lang and A. Agresti), Journal of the American Statistical Association, 89 (1994), 625-632.

pdf file

Simple capture-recapture models permitting unequal catchability and variable sampling effort, Biometrics, 50, (1994), 494-500.

pdf file

Logit models and related quasi-symmetric loglinear models for comparing responses to similar items in a survey, Sociological Methods and Research, 24 (1995), 68-95. pdf file

Improved exact inference about conditional association in three-way contingency tables (D. Kim and A. Agresti), Journal of the American Statistical Association, 90 (1995), 632-639.

Raking kappa: Describing potential impact of marginal distributions on measures of agreement (with A. Ghosh and M. Bini), Biometrical Journal, 37 (1995) 811-820.

Order-restricted tests for stratified comparisons of binomial proportions (with B. Coull), Biometrics, 52 (1996) 1103-1111.

pdf file

Mantel--Haenszel--type inference for cumulative odds ratios (I-M. Liu and A. Agresti), Biometrics, 52 (1996) 1223-1234.

Logit models with random effects and quasi-symmetric loglinear models, pp. 3-12 in Statistical Modelling, Proceedings of the 11th International Workshop on Statistical Modelling (Orvieto, Italy, July 1996).

Connections between loglinear models and generalized Rasch models for ordinal responses, Chapter 20 in Applications of Latent Trait and Latent Class Models in the Social Sciences, pp. 209-218, edited by J. Rost and R. Langeheine, Berlin: Waxmann Munster, (1997).

Nearly exact tests of conditional independence and marginal homogeneity for sparse contingency tables (D. Kim and A. Agresti), Computational Statistics and Data Analysis, (1997), 24, 89-104.

A review of tests for detecting a monotone dose-response relationship with ordinal response data (with C. Chuang-Stein), Statistics in Medicine, (1997), 16, 2599-2618.

A model for repeated measurements of a multivariate binary response, Journal of the American Statistical Association (1997), 92, 315-321.

pdf file

An empirical comparison of inference using order-restricted and linear logit models for a binary response (with B. Coull), Communications in Statistics, Simulation and Computation, (1998), 27, 147-166.

Evaluating agreement and disagreement among movie reviewers, Chance (1997) (with L. Winner). pdf file

Comment on article by Strawderman and Wells, Journal of the American Statistical Association, (1998), 93, 1307-1310.

Approximate is better than exact for interval estimation of binomial proportions, The American Statistician (1998) (with B. Coull). pdf file

Order-restricted inference for monotone trend alternatives in contingency tables Computational Statistics & Data Analysis (1998) (with B. Coull). pdf file

On logit confidence intervals for the odds ratio with small samples, Biometrics (1999).

The use of mixed logit models to reflect subject heterogeneity in capture-recapture studies, Biometrics (1999) (B. Coull and A. Agresti).

Modeling a categorical variable allowing arbitrarily many category choices, Biometrics (1999) (with I. Liu).

Modelling ordered categorical data: Recent advances and future challenges, Statistics in Medicine (1999).

Random effects modeling of multiple binary responses using the multivariate binomial logit-normal distribution, Biometrics (2000) (B. A. Coull and A. Agresti).

Strategies for comparing treatments on a binary response with multi-center data, Statistics in Medicine (2000) (with J. Hartzel). pdf file

Hierarchical Bayesian analysis of binary matched pairs data, Statistica Sinica (2000) (M. Ghosh, M. Chen, A. Ghosh, and A. Agresti).

Noninformative priors for one parameter item response models, Journal of Statistical Planning and Inference (2000) (M. Ghosh, M. Chen, A. Ghosh, and A. Agresti).

Challenges for categorical data analysis in the twenty-first century, in Statistics for the 21st Century, edited by C. R. Rao and G. J. Szekely, Marcel Dekker (2000).

Summarizing the predictive power of a generalized linear model, Statistics in Medicine (2000) (B. Zheng and A. Agresti) pdf file

Simple and effective confidence intervals for proportions and difference of proportions result from adding two successes and two failures, The American Statistician (2000) (with B. Caffo). pdf file

Random effects modeling of categorical response data, Sociological Methodology (2000) (A. Agresti, J. Booth, J. P. Hobert, and B. Caffo). pdf file

Describing heterogeneous effects in stratified ordinal contingency tables, with application to multi-center clinical trials, Computational Statistics & Data Analysis (2001) (J. Hartzel, I. Liu, and A. Agresti). pdf file

Strategies for modeling a categorical variable allowing multiple category choices, Sociological Methods and Research (2001) (A. Agresti and I. Liu).

Exact inference for categorical data: recent advances and continuing controversies, Statistics in Medicine (2001).

A correlated probit model for multivariate repeated measures of mixtures of binary and continuous responses, Journal of American Statistical Association (2001) (R.V. Gueorguieva and A. Agresti). pdf file

On small-sample confidence intervals for parameters in discrete distributions, Biometrics (2001) (A. Agresti and Y. Min). pdf file

Multinomial logit random effects models, Statistical Modelling (2001) (J. Hartzel, A. Agresti, and B. Caffo). pdf file

Modeling clustered ordered categorical data: A survey, International Statistical Review (2001) (A. Agresti and R. Natarajan). pdf file

Statistical issues in the 2000 U.S. Presidential election in Florida, University of Florida Journal of Law and Public Policy (Fall 2001 issue) (A. Agresti and B. Presnell). pdf file

Comment (with B. Coull) on article by Brown, Cai, and DasGupta. Statistical Science, (2001), 16, 117-120.

The analysis of contingency tables under inequality constraints, Journal of Statistical Planning and Inference (2002) (A. Agresti and B. A. Coull). pdf file

Measures of relative model fit, Computational Statistics and Data Analysis (2002) (A. Agresti and B. Caffo).

Unconditional small-sample confidence intervals for the odds ratio, Biostatistics (2002) (A. Agresti and Y. Min). pdf file

Modeling nonnegative data with clumping at zero: A survey, Journal of the Iranian Statistical Society (2002) (Y. Min and A. Agresti). pdf file

Links between binary and multi-category logit item response models and quasi-symmetric loglinear models, for special issue of Annales de la Faculte des Sciences de Toulouse Mathematiques, to honor retirement of Henri Caussinus, (2002). pdf file

On sample size guidelines for teaching inference about the binomial parameter in introductory statistics, unpublished manuscript by A. Agresti and Y. Min (2002). pdf file

The 2000 Presidential election in Florida: Misvotes, undervotes, overvotes, Statistical Science (2002) (A. Agresti and B. Presnell). pdf file

Dealing with discreteness: Making `exact' confidence intervals for proportions, differences of proportions, and odds ratios more exact, Statistical Methods in Medical Research (2003). pdf file

A class of generalized log-linear models with random effects, Statistical Modelling (2003) (B. A. Coull and A. Agresti). pdf file

Interview with Alan Agresti, conducted by Jackie Dietz, STATS (The Magazine for Students of Statistics) (2004). Word file

Examples in which misspecification of a random effects distribution reduces efficiency, Computational Statistics & Data Analysis (2004) (A. Agresti, P. Ohman, and B. Caffo). pdf file

Effects and noneffects of paired identical observations in comparing proportions with binary matched-pairs data, Statistics in Medicine (2004) (A. Agresti and Y. Min). pdf file

Improved confidence intervals for comparing matched proportions, Statistics in Medicine (2005) (A. Agresti and Y. Min). pdf file

Frequentist performance of Bayesian confidence intervals for comparing proportions in 2x2 contingency tables, Biometrics (2005) (A. Agresti and Y. Min). pdf file

Random effect models for repeated measures of zero-inflated count data, Statistical Modelling (2005) (Y. Min and A. Agresti). pdf file

The analysis of ordered categorical data: An overview and a survey of recent developments, invited discussion paper for the Spanish Statistical Journal, TEST (2005) (I. Liu and A. Agresti). pdf file

Multivariate tests comparing binomial probabilities, with application to safety studies for drugs, Applied Statistics (JRSS-C) (2005) (A. Agresti and B. Klingenberg). pdf file

Bayesian inference for categorical data analysis, Statistical Methods and Application (Journal of the Italian Statistical Society), (2005) (A. Agresti and D. Hitchcock). pdf file

Randomized confidence intervals and the mid-P approach, discussion of article by C. Geyer and G. Meeden, Statistical Science, (2005) (A. Agresti and A. Gottard). pdf file

Multivariate extensions of McNemar's test, Biometrics, (2006) (B. Klingenberg and A. Agresti). pdf file

Independence in multi-way contingency tables: S. N. Roy's breakthroughs and later developments, Journal of Statistical Planning and Inference, (2007) (A. Agresti and A. Gottard). pdf file

A class of ordinal quasi-symmetry models for square contingency tables, Statistics and Probability Letters, (2007) (M. Kateri and A. Agresti). pdf file

Reducing conservativism of exact small-sample methods of inference for discrete data, Computational Statistics and Data Analysis, (2007) (A. Agresti and A. Gottard). pdf file

Modeling and inference for an ordinal effect size measure, Statistics in Medicine, (2008) (E. Ryu and A. Agresti). pdf file

Simultaneous confidence intervals for comparing binomial parameters, Biometrics, (2008) (A. Agresti, M. Bini, B. Bertaccini, and E. Ryu). pdf file

A generalized regression model for a binary response, Statistics and Probability Letters, (2010) (M. Kateri and A. Agresti). pdf file

Pseudo-score confidence intervals for parameters in discrete statistical models, Biometrika, (2010) (A. Agresti and E. Ryu). pdf file

Score and pseudo score confidence intervals for categorical data analysis, invited article for Gary Koch festschrift, Statistics in Biopharmaceutical Research, (2011). pdf file

Quasi-symmetric graphical log-linear models, Scandinavian Journal of Statistics, (2011) (A. Gottard, G.M. Marchetti, and A. Agresti). pdf file

Statistics as an academic discipline, by A. Agresti and X.-L. Meng, Chapter 1 in Strength in Numbers: The Rising of Academic Statistics Departments in the U.S., edited by A. Agresti and X.-L. Meng, (2012) Springer. pdf file

University of Florida Department of Statistics, by A. Agresti, W. Mendenhall III, and Richard Scheaffer. Chapter in Strength in Numbers: The Rising of Academic Statistics Departments in the U.S., edited by A. Agresti and X.-L. Meng, (2012) Springer. pdf file

Bayesian inference about odds ratio structure in ordinal contingency tables, (2013) (A. Agresti and M. Kateri), in special issue of Environmetrics to honor the memory of George Casella. pdf file

GEE for multinomial responses using a local odds ratios parameterization, Biometrics, (2013) (A. Touloumis, A. Agresti, and M. Kateri). pdf file

Some remarks on latent variable models in categorical data analysis, Communications in Statistics, Theory and Methods, (2014) (A. Agresti and M. Kateri), in special issue of invited contributions to the conference "Methods and Models on Latent Variables" held in Naples, Italy in May 2012. pdf file

Two Bayesian/frequentist challenges for categorical data analyses, Metron, (2014), in special issue of invited contributions to the conference "Recent Advances in Statistical Inference: Theory and Case Studies" held in Padova, Italy in March 2013.

Ordinal effect size measures for group comparisons in models, (A. Agresti and M. Kateri), (2015), in proceedings of International Workshop on Statistical Modelling in Linz, Austria.

Categorical regularization: Discussion of article by Tutz and Gertheiss, Statistical Modelling, (2016).

Ordinal probability effect measures for group comparisons in multinomial cumulative link models, (A. Agresti and M. Kateri), Biometrics (2017).

Simple effect measures for interpreting models for ordinal categorical data, (A. Agresti and C. Tarantola), pp. 252-257 in Proceedings of the 32nd International Workshop on Statistical Modelling, Groningen, Netherlands, 2017.

Simple effect measures for interpreting models for ordinal categorical data (A. Agresti and C. Tarantola), Statistica Neerlandica (2018) . pdf file

pdf file of appendix with supplementary R functions

The class of CUB models: statistical foundations, inferential issues and empirical evidence, comments on article by D. Piccolo and R. Simone (A. Agresti and M. Kateri), Statistical Methods and Applications (SMA) (2019).

Some issues in generalized linear modeling, in Springer proceedings of International Workshop on Matrices and Statistics, Funchal, Portugal (2020).

Interpreting effects in generalized linear modeling, (A. Agresti, C. Tarantola, and R. Varriale), Pages 1-8 in Statistical Learning and Modeling in Data Analysis}, edited by S. Balzano, G. Porzio, R. Salvatore, D. Vistocco, and M. Vichi, Springer (2021).

Simple ways to interpret effects in modeling binary data, (A. Agresti, C. Tarantola, and R. Varriale), to appear in Trends and Challenges in Categorical Data Analysis, edited by Maria Kateri and Irini Moustaki, Springer (2021).

Reflections on Murray Aitkin's contributions to nonparametric mixture models and Bayes factors, (Alan Agresti, Francesco Bartolucci, and Antonietta Mira), in special issue of Statistical Modelling to honor Murray Aitkin, (2021).

The foundations of statistical science: A history of textbook presentations, invited paper in Brazilian Journal of Probability and Statistics, with comments by several statisticians (including Sir David Cox, Bradley Efron, and Xiao-Li Meng), (2021). pdf file

A review of score-test-based inference for categorical data, (A. Agresti, S. Giordano, and A. Gottard}, for special issue of Journal of Quantitative Economics to honor C. R. Rao, (2022).

A few recent photos with Statistics connections

Participants at workhop in my honor, "Categorical Data Analysis & Friends," Florence Italy, September 18, 2019.

Agresti pic8

"Grazie mille ad Anna Gottard per aver organizzato questo meraviglioso evento." And thanks to my Italian friends who came from Padova, Milano, Roma, Pavia, Firenze, Napoli, Venezia, Perugia, Parma, Bologna, Bergamo, Cosenza, Trento, and Verona. Here is the program about the Florence workshop, and here is the talk I gave with reminiscences of visits to Italian universities.

Agresti pic6 From my Italian citizenship ceremony in Ferrazzano, Molise, Italy (2017)

Agresti pic7

European Statistics research colleagues and dear friends, from my Italian citizenship ceremony (Ferrazzano, 2017)

My roots (and two of my favorite spots on earth)

Agresti pic5 Ferrazzano, Molise, Italy

(la citta` di mia nonna italiana)

Agresti pic3 Ferrazzano, Molise, Italy

Agresti pic1 Forest of Dean, Gloucestershire, England (the land of my mother and my British grandparents)

Premio Premio a Giovani Studiosi e Studiose per Contributi alle Discipline Statistiche (annual award I initiated and funded in 2021 for outstanding Italian statistician under age 40; first award to Daniele Durante)

Here is a seminar (in mp4 format) on the History of Categorical Data Analysis that I presented in October 2015 at Istat (the Italian Census Bureau) in Rome, Italy

Here is a YouTube video of a seminar on Simple Ways to Interpret Effects in Modeling Binary and Ordinal Data presented in 2021 for National University of Rosario, Argentina (and also at La Sapienza University in Rome and Ca' Foscari in Venice, Italy, and at Ohio State University)

Here are Power Point slides from a seminar on Uncertainty (about Covid and related things, in Italian), presented at Padova, Italy in December 2020 by Antonietta Mira of the University of Insubria (Italy) and University of Lugano (Svizzera Italiana), with my help.

Jacki and Alan's Pound-wise guide to London from the December--January 2007 issue of Gainesville magazine. (My wife Jacki Levine is founder and was editor of this bi-monthly magazine for 13 years.) More up-to-date, here is a useful site, courtesy of Rick Steves, about experiencing untouristy London: Untouristy London. We would also suggest Borough Market, and the walk along the south bank of the Thames from the South Bank arts complex to the Tate and then Tower Bridge (that part is touristy), but then further east to Rotherhithe, stopping at the Mayflower pub.

The picture at the top of my home page was taken by Jacki Levine in the Forest of Dean, Gloucestershire, UK, among the May bluebells.