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Alan Agresti Personal Home Page


Alan Agresti
Distinguished Professor Emeritus
Department of Statistics
University of Florida
226 GriffinFloyd Hall
Gainesville, FL 326118545
EMAIL: aa "at" stat
"dot" ufl "dot" edu
I was employed by the University of Florida from 19722010. I have
also had visiting professor positions at Harvard University (including
fall semester each year 20082014), 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.

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
 University of Florida Research Foundation (UFRF) Professorship, 19972000
 Excellence in Continuing Education Award from American Statistical
Association, 2002
 36th Annual Allen T. Craig lecturer, University of Iowa, 2006
 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), PortugueseGalician Biometry Meeting (2013), New England
Statistics Symposium (2014), and invited lectures and short courses in
about 30 countries
Book Information and Supplemental Files
1. 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 quasilikelihood 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 highdimensional 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."
2. The
book
Strength in Numbers: The Rising of Academic Statistics Departments in
the U.S., coedited with XiaoLi 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
mid1960s, 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.
3. The
text
Categorical Data Analysis, 3rd Edition
has been published
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, and
a list of some typos and errors.
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
SPlus 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.
4. 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).
5. The
text
Statistics: The Art and Science of Learning from
Data (4th edition, Pearson, 2017) was written with Christine
Franklin of the University of Georgia. The latest (4th) edition was
coauthored by Bernhard Klingenberg of Williams College, who 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 oneterm or twoterm undergraduate course or a high
school AP course on an introduction to statistics, presented with a
conceptual approach. The link
AFK
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 Univ. of
Trento.
6. The
text
Statistical
Methods for the Social Sciences (4th edition, Pearson Prentice
Hall, 2009, 5th edition to appear late 2016) is designed for a
twosemester 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. I am pleased to report (due to my partial
Italian heritage) that there is also an Italian version of the first
ten chapters 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
www.grupoa.com.br/livros/pesquisacientifica/metodosestatisticosparaascienciassociais/9788563899576
 and a Chinese version, and it is being translated into Spanish. I
have developed Powerpoint files for lectures from Chapters 112 of
this text that are available to instructors using this text.
(Chapters 17 of these have also been translated into Spanish by Norma
Leyva of Universidad Iberoamericana in Mexico.) Please contact me for
details. For applets used in some exercises of the new edition, go
to applets for
SMSS. For a file containing most of the large data sets
from the text, click on Website with
data for SMSS. Many of these data files are also
available in commaseparated files
(.csv) csv
data files, thanks to Andrew C. Thomas. Jeffrey Arnold of
Emory University has kindly set up a R package at CRAN for R users to
be able to access the datasets used in this text. See
R
data files. For examples of the use of the software Stata
for various analyses for examples in this text, see the useful site
set up by
the UCLA
Statistical Computing Center. (I can supply to anyone who is
interested an Internet address for downloading Stata data files
for the new edition of the book.) Here is a list (pdf file)
of errors
and typos that I've noticed so far in this new edition.
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. This text has been revised for a
5th edition, which will be published late in 2016.
7. The text
An Introduction to Categorical Data Analysis
is in its second edition (Wiley, 2007). It presents a nontechnical
introduction to topics such as logistic regression. This book is a
lowertechnicallevel 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 SPlus, SPSS,
Stata, and StatXact. For SAS files containing data sets from the
text, click on Data sets for
Intro CDA. There are some very good online notes, using
R code, developed by instructors who have used this text. For
example, see the website
of Brett
Presnell for a course on this topic at the University of Florida.
Brett has improved some of my own course notes and added R code and
output. Here are
some corrections
for the 1st edition of this book and a pdf file
of corrections for the 2nd
edition.
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 halfday 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."
History of Statistics at UF
See UF Statistics for 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 Agresti and 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 Linear and Generalized Linear Models,
Wiley (2015).
Strength in Numbers: The Rising of Academic Statistics
Departments in the U.S., Springer (2012), coedited
with XiaoLi Meng.
Statistics: The Art and Science of Learning from Data,
3rd edition, Pearson Prentice Hall (2012), with Chris Franklin.
Analysis of Ordinal Categorical Data, 2nd ed., Wiley
(2010).
An Introduction to Categorical Data Analysis, 2nd ed., Wiley
(2007).
Categorical Data Analysis, 3rd edition, Wiley
(2013).
Statistical Methods for the Social Sciences, 4th
edition, Pearson Prentice Hall (2009) (with B. Finlay).
Some Articles
Bounds on the extinction time distribution of a branching
process. Advances in Applied Probability, 6 (1974),
322335.
pdf file
On the extinction times of varying and random environment
branching processes. Journal of Applied Probability, 12
(1975), 3946. pdf file
The effect of category choice on some ordinal measures of
association. Journal of the American Statistical Association,
71 (1976), 4955.
Some exact conditional tests of independence for r x c
crossclassification tables. (with D. Wackerly)
Psychometrika, 42 (1977), 111125.
pdf file
Some considerations in measuring partial association for ordinal
categorical data. Journal of the American Statistical
Association, 72 (1977), 3745.
A coefficient of multiple association based on ranks.
Communications in Statistics, A6 (1977), 13411359.
Statistical analysis of qualitative variation. (with B.
Agresti), Chapter 10, in Sociological Methodology (1978) ed.
by K. F. Schuessler, JosseyBass Publ., 204237.
Descriptive measures for rank comparisons of groups.
Proceedings of the Social Statistics Section of the American
Statistical Association, (1978), 585590.
Exact conditional tests for crossclassifications: Approximation
of attained significance level. (with D. Wackerly and J. Boyett),
Psychometrika, 44 (1979), 7583.
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), 624626.
Generalized odds ratios for ordinal data. Biometrics, 36
(1980), 5967.
A hierarchical system of interaction measures for
multidimensional contingency tables. Journal of the Royal
Statistical Society B, 43 (1981), 293301.
Measures of nominalordinal association, Journal of the
American Statistical Association, 76 (1981), 524529.
pdf file
Statistical fallacies. Encyclopedia of the Statistical
Sciences, Vol. 3 (1983), John Wiley and Sons, 2428.
Testing marginal homogeneity for ordinal categorical variables,
Biometrics, 39, (1983), 505510.
pdf file
A survey of strategies for modelling crossclassifications
having ordinal variables. Invited Essay Review in Journal of
the American Statistical Association, 78 (1983), 184198.
Association models for multidimensional crossclassifications of
ordinal variables (with A. Kezouh), invited paper for issue on
categorical data, Communications in Statistics, A12 (1983),
12611276.
A simple diagonalsparameter symmetry and quasisymmetry model,
Statistics and Probability Letters, 1 (1983), 313316.
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), 3337.
Ordinal data. Encyclopedia of the Statistical Sciences,
Vol. 6 (1985), John Wiley and Sons, 511516.
Comparing mean ranks for repeated measures data (with J.
Pendergast), Communications in Statistics, A15 (1986),
14171433.
A new model for ordinal pain data from a pharmaceutical study
(with C. Chuang), Statistics in Medicine, 5 (1986), 1520.
Applying Rsquared type measures to ordered categorical
data, Technometrics, 28 (1986), 133138.
Models for the probability of concordance in
crossclassification tables (with J. Schollenberger and D.
Wackerly), Quality and Quantity (International Journal of
Methodology), 21 (1987), 4957.
pdf file
Orderrestricted score parameters in association models for
contingency tables (with C. Chuang and A. Kezouh), Journal
of
the American Statistical Association, 82 (1987), 619623.
Bayesian and maximum likelihood approaches to orderrestricted
inference for models for ordinal categorical data (with C. Chuang),
pp. 627 in Advances in Order Restricted Statistical
Inference, (1986), ed. by R. Dykstra, T. Robertson, and F.T.
Wright, New York: SpringerVerlag.
An empirical investigation of some effects of sparseness in
contingency tables (with M. Yang), Computational Statistics &
Data Analysis, 5 (1987), 921.
A model for agreement between ratings on an ordinal scale,
Biometrics, 44 (1988), 539548.
Logit models for repeated ordered categorical response data,
invited paper for Proceedings of 13th SAS Users Group
Conference, (1988), 9971005.
An agreement model with Kappa as parameter, Statistics and
Probability Letters, 7 (1989), 271273.
Modelbased Bayesian methods for estimating cell proportions in
crossclassification tables having ordered categories (with C.
Chuang), Computational Statistics \& Data Analysis, 7 (1989),
245258.
A tutorial on modeling ordered categorical response data,
Psychological Bulletin, 105 (1989), 290301.
A survey of models for repeated ordered categorical response
data, Statistics in Medicine, 8 (1989), 12091224.
Exact inference for contingency tables with ordered categories
(with C. Mehta and N. Patel), Journal of the American
Statistical Association, 85 (1990), 453458.
Analysis of sparse repeated categorical measurement data (with
S. Lipsitz and J. B. Lang), SAS Users Group International
Conference Proceedings, 1991, 14521460.
Parsimonious latent class models for ordinal variables, invited
paper in Proceedings of 6th International Workshop on
Statistical Modeling, (1991), 112, Utrecht, Netherlands.
Analysis of ordinal paired comparison data, Journal of the
Royal Statistical Society C (Applied Statistics), 41 (1992),
287297.
Loglinear modeling of pairwise interobserver agreement on a
categorical scale (M. P. Becker and A. Agresti), Statistics
in
Medicine, 11 (1992), 101114.
Comparing marginal distributions of large, sparse contingency
tables (with S. Lipsitz and J. B. Lang), Computational
Statistics and Data Analysis, 14 (1992), 5573.
A survey of exact inference for contingency tables (with
discussion), Statistical Science, 7 (1992), 131177.
pdf
file
Quasisymmetric latent class models, with application to rater
agreement (with J. Lang), Biometrics, 49 (1993),
131140. pdf
file
Modeling patterns of agreement and disagreement,
Statistical Methods in Medical Research, 1 (1992), 201218.
Computing conditional maximum likelihood estimates for
generalized Rasch models using simple loglinear models with
diagonals parameters, Scandinavian Journal of Statistics, 20
(1993), 6372.
Some empirical comparisons of exact, modified exact, and
higherorder asymptotic tests of independence for ordered
categorical variables (with J. Lang and C. Mehta),
Communications in Statistics, Simulation and Computation, 22
(1993), 118.
A proportional odds model with subjectspecific effects for
repeated ordered categorical responses (with J. Lang),
Biometrika, 80 (1993), 527534.
Distributionfree fitting of logit models with random effects
for repeated categorical responses, Statistics in Medicine, 12
(1993), 19691987.
Simultaneously modeling joint and marginal distributions of
multivariate categorical responses (J. Lang and A. Agresti),
Journal of the American Statistical Association, 89 (1994),
625632.
Simple capturerecapture models permitting unequal catchability
and variable sampling effort, Biometrics, 50, (1994),
494500.
Logit models and related quasisymmetric loglinear models for
comparing responses to similar items in a survey, Sociological
Methods and Research, 24 (1995), 6895.
pdf file
Improved exact inference about conditional association in
threeway contingency tables (D. Kim and A. Agresti), Journal
of the American Statistical Association, 90 (1995), 632639.
Raking kappa: Describing potential impact of marginal
distributions on measures of agreement (with A. Ghosh and M.
Bini),
Biometrical Journal, 37 (1995) 811820.
Orderrestricted tests for stratified comparisons of binomial
proportions (with B. Coull), Biometrics, 52 (1996) 11031111.
MantelHaenszeltype inference for cumulative odds ratios
(IM. Liu and A. Agresti), Biometrics, 52 (1996) 12231234.
Logit models with random effects and quasisymmetric loglinear
models, pp. 312 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.
209218, 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, 89104.
A review of tests for detecting a monotone doseresponse
relationship with ordinal response data (with C. ChuangStein),
Statistics in Medicine, (1997), 16, 25992618.
A model for repeated measurements of a multivariate binary response,
Journal of the American Statistical Association (1997).
An empirical comparison of inference using orderrestricted
and linear logit models for a binary response (with
B. Coull), Communications in Statistics, Simulation and
Computation, (1998), 27, 147166.
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,
13071310.
Approximate is better than exact for interval estimation of
binomial proportions, The American Statistician
(1998) (with B. Coull). pdf
file
Orderrestricted 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
capturerecapture 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 logitnormal distribution, Biometrics
(2000) (B. A. Coull and A. Agresti).
Strategies for comparing treatments on a binary response with
multicenter 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 twentyfirst
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).
Describing heterogeneous effects in stratified ordinal contingency
tables, with application to multicenter clinical trials,
Computational Statistics & Data Analysis (2001) (J. Hartzel,
I. Liu, and A. Agresti).
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 smallsample 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).
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, 117120.
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 smallsample confidence intervals for the odds
ratio, Biostatistics (2002) (A. Agresti and Y. Min).
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 multicategory logit item response
models and quasisymmetric 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 (2003) (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 loglinear 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 matchedpairs 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 zeroinflated
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 (JRSSC) (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 midP 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 multiway 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 quasisymmetry models for square contingency
tables, Statistics and Probability Letters, (2007)
(M. Kateri and A. Agresti). pdf
file
Reducing conservativism of exact smallsample 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
Pseudoscore 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
Quasisymmetric graphical loglinear 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.
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),
(2016), to appear in Biometrics.
Old home pages for courses at UF
Other Information
The University of Florida Statistics Department home page
Here is a seminar (in mp4 format) on the
Jacki and Alan's

Poundwise guide to London from the DecemberJanuary 2007
issue of Gainesville magazine. (My wife Jacki Levine is founder
and editor of this bimonthly magazine.)
My roots (and two of my favorite spots on earth)
Ferrazzano, Molise, Italy
Ferrazzano, Molise, Italy
Forest of Dean, Gloucestershire, England
My home page picture at the top was taken by Jacki Levine in the
Forest of Dean, among the May bluebells. It is an old picture, but
below is a more recent one, taken while I was teaching a short course
at the International Workshop on Statistical Modelling, in Linz,
Austria in July 2015. This is a very friendly conference, typically
held in some of the nicest European cities and thus a good way to get
to know European statisticians. See
IWSM. The
Statistical Modelling Society also sponsors a
journal, Statistical
Modelling.