Professor Freedman passed away on 17 October 2008. Obituaries may be found at statistics.berkeley.edu/~stark/Preprints/dafObituary.htm and berkeley.edu/news/media/releases/2008/10/20_freedman.shtml.
Remembrances of Professor Freedman offered by family, friends and colleagues at a memorial on 2 December 2008 may be found at statistics.berkeley.edu/~census/David_Freedman_Memorial.pdf.
David A. Freedman was Professor of Statistics at the University of California, Berkeley. He also taught in Athens, Caracas, Jerusalem, Kuwait, London, and Mexico City. He is the author of several books, including a widely-used elementary text. He has about 200 papers in the professional literature, and was a member of the American Academy of Arts and Sciences. In 2003, he received the John J. Carty Award for the Advancement of Science from the National Academy of Sciences, recognizing his profound contributions to the theory and practice of statistics.
Freedman worked on martingale inequalities, Markov processes, de Finettis theorem, consistency of Bayes estimates, sampling, the bootstrap, procedures for testing and evaluating models, census adjustment, epidemiology, statistics and the law. His research interests included methods for causal inference, and the behavior of standard statistical models under non-standard conditions; for example, how do regression models behave when fitted to data from randomized experiments? (Not as expected, is the short answer.)
Freedman consulted for the Carnegie Commission, the City of San Francisco, and the Federal Reserve, as well as several departments of the U.S. government. He testified as an expert witness on statistics in law cases that involve employment discrimination, fair loan practices, duplicate signatures on petitions, railroad taxation, ecological inference, flight patterns of golf balls, price scanner errors, sampling techniques, and census adjustment.
[Vita in PDF format]
D.A. Freedman. Statistical Models: Theory and Practice. Cambridge University Press (2005). [Cambridge website] [Whats in this book?] [Reviews] [Student comments] [Typography] [Data sets] [Schedule] [Project] [Errata] [Supplementary Lecture Notes]
D.A. Freedman, R. Pisani, and R.A. Purves. Statistics. W.W. Norton, Inc. New York (1978). [Norton] 2nd edition in 1991. Spanish translation in 1993. Chinese translation in 1995. 3rd edition in 1998. Hungarian translation in 2005. 4th edition in 2007. [Errata] for 1st printing of 4th edition
D.A. Freedman and J. Sekhon. Endogeneity in probit models. [PDF-Preprint]
D.A. Freedman. Diagnostics cannot have much power against general alternatives. [PDF-Preprint] To appear in Journal of Forecasting.
D.A. Freedman. Do the Ns justify the means. [WORD-Preprint]
S.P. Klein, D.A. Freedman, R. Shavelson and R. Bolus. Assessing school effectiveness. [PDF-Preprint] To appear in Evaluation Review. Vol. 32 (2008) Dec.
D.A. Freedman. Randomization does not justify logistic regression. [PDF-Preprint] Statistical Science vol. 23 (2008) pp. 23749.
D.A. Freedman and R.A. Berk. On weighting regressions by propensity scores. [PDF-Preprint] Evaluation Review vol. 32 (2008) pp. 392409.
D.A. Freedman. Some general theory for weighted regressions. [PDF-Preprint]
D.A. Freedman. On types of scientific enquiry: Nine success stories in medical research. To appear in The Oxford Handbook of Political Methodology pp. 30018. Janet M. Box-Steffensmeier, Henry E. Brady and David Collier, editors. [PDF-Preprint]
D.A. Freedman. On regression adjustments in experiments with several treatments. Annals of Applied Statistics vol. 2 (2008) pp. 17696. [PDF-Preprint]
D.A. Freedman. Survival analysis: A primer. The American Statistician vol. 62 (2008) pp. 110119. [PDF-Preprint]
D.A. Freedman. Oasis or mirage? CHANCE Magazine vol. 21 no. 1 (2008) pp. 5961. [PDF-Preprint]
D.A. Freedman. On regression adjustments to experimental data. Advances in Applied Mathematics vol. 40 (2008) pp. 180193. [PDF-Preprint]
D.A. Freedman and K.W. Wachter. Methods for Census 2000 and statistical adjustments. In Social Science Methodology. Sage (2007) pp. 23245. Steven Turner and William Outhwaite, editors [PDF-Preprint]
T. Dunning and D.A. Freedman. Modeling selection effects. In Social Science Methodology . Sage (2007) pp. 22531. Steven Turner and William Outhwaite, editors [PDF-Preprint]
D.A. Freedman. How can the score test be inconsistent? The American Statistician vol. 61 (2007) pp. 291295 [PDF-Preprint]
D.A. Freedman. Statistical models for causation: What inferential leverage do they provide? Evaluation Review vol. 30 (2006) pp. 691713. [PDF-Preprint]
D.A. Freedman. On the so-called Huber Sandwich Estimator and robust standard errors. The American Statistician vol. 60 (2006) pp. 299302. [PDF-Preprint]
D.B. Petitti and D.A. Freedman. Invited commentary: How far can epidemiologists get with statistical adjustment? American Journal of Epidemiology vol. 162 (2005) pp. 41518. [AJE website]
D.A. Freedman and D.B. Petitti. Hormone replacement therapy does not save lives: Comments on the Womens Health Initiative Biometrics vol. 61 (2005) pp. 918920. [TXT-Preprint]
D.A. Freedman. Linear statistical models for causation: A critical review. In the Wiley Encyclopedia of Statistics in Behavioral Science (2005). B. Everitt and D. Howell, eds. [PDF]
M. L. Eaton and D. A. Freedman. Dutch book against objective priors. The Bernoulli Journal, vol. 10 (2004) pp. 86172. [PDF-Preprint]
D. A. Freedman. Notes on the Dutch book argument. [PDF]
D.A. Freedman. On specifying graphical models for causation, and the identification problem. Evaluation Review (2004) vol. 26 pp. 26793. Reprinted in Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg, Cambridge University Press (2005) pp. 5679, D.W.K. Andrews and J.H. Stock, eds, [PDF-Preprint]
D. A. Freedman, D. B. Petitti, and J. M. Robins. On the efficacy of screening for breast cancer. International Journal of Epidemiology, vol. 33 (2004) pp. 4373. [IJE] [PDF-Preprint] Correspondence, pp. 14046.
P. Diaconis and D. A. Freedman. The Markov moment problem and de Finettis theorem: Parts I and II. Mathematische Zeitschrift, vol. 247 (2004) pp. 183212. [PDF-Preprint]
D.A. Freedman. The ecological fallacy. In the Encyclopedia of Social Science Research Methods. Sage Publications (2004) Vol. 1 p. 293. M. Lewis-Beck, A. Bryman, and T. F. Liao, eds. [TXT-Preprint]
D.A. Freedman. Sampling. In the Encyclopedia of Social Science Research Methods. Sage Publications (2004) Vol. 3 pp. 986990. M. Lewis-Beck, A. Bryman, and T. F. Liao, eds. [PDF-Preprint]
D.A. Freedman and K.W. Wachter. On the likelihood of improving the accuracy of the census through statistical adjustment. In Science and Statistics: A Festschrift for Terry Speed. Institute of Mathematical Statistics Monograph 40 (2003) pp. 197230. D. R. Goldstein, ed. [PDF-Preprint]
D.A. Freedman and P.B. Stark. What is the probability of an earthquake? In Earthquake Science and Seismic Risk Reduction. NATO Science Series IV: Earth and Environmental Sciences, vol. 32, Kluwer, Dordrecht, The Netherlands (2003) pp. 201213. F. Mulargia and R. J. Geller, eds. [PDF-Preprint]
R.A. Berk and D.A. Freedman. Statistical assumptions as empirical commitments. In Law, Punishment, and Social Control: Essays in Honor of Sheldon Messinger, 2nd ed. Aldine de Gruyter (2003) pp. 23554. T. G. Blomberg and S. Cohen, eds. [PDF-Preprint]
D.A. Freedman and D.B. Petitti. Salt, blood pressure, and public policy. International Journal of Epidemiology vol. 31 (2002) pp. 319320. [TXT-Preprint]
D.A. Freedman. Ecological inference and the ecological fallacy. International Encyclopedia of the Social & Behavioral Sciences. Elsevier (2001) vol. 6 pp. 402730. Neil J. Smelser and Paul B. Baltes, eds. [PDF-Preprint]
D.A. Freedman and K.W. Wachter. Census adjustment: Statistical promise or statistical illusion? Society vol. 39 (2001) pp. 2633 [PDF-Preprint]
D.A. Freedman and P.B. Stark. The swine flu vaccine and Guillain-Barré syndrome. Law and Contemporary Problems, vol. 64 (2001) pp. 4962 [Duke Law Journals]
D.A. Freedman and D.B. Petitti. Salt and blood pressure: Conventional wisdom reconsidered. Evaluation Review, vol. 25 (2001) pp. 26787 [PDF-preprint]
D.A. Freedman, P.B. Stark, and K.W. Wachter. A probability model for census adjustment. Mathematical Population Studies, vol. 9 (2001) pp. 16580 [PDF-preprint]
K.W. Wachter and D.A. Freedman. The fifth cell: Correlation bias in U.S. census adjustment. Evaluation Review, vol. 24 (2000) pp. 191211 [PDF-preprint]
K.W. Wachter and D.A. Freedman. Measuring local heterogeneity with 1990 U.S. census data. Demographic Research, vol. 3 (2000) art. 10 [Demographic Research] [PDF]
D.H. Kaye and D.A. Freedman. Reference guide on statistics. 2nd ed. Federal Judicial Center, Washington, D.C. (2000) [PDF]
L.D. Brown, M.L. Eaton, D.A. Freedman, S.P. Klein, R.A. Olshen, K.W. Wachter, M.T. Wells, and D. Ylvisaker. Statistical controversies in Census 2000. Jurimetrics, vol. 39 (1999) pp. 34775 [PDF-preprint]
D.A. Freedman. From association to causation: Some remarks on the history of statistics. Statistical Science, vol. 14 (1999) pp. 24358. Reprinted in Journal de la Société Francaise de Statistique, vol. 140 (1999) pp. 532 and in Stochastic Musings: Perspectives from the Pioneers of the Late 20th Century. Lawrence Erlbaum Associates (2003) pp. 4571. J. Panaretos, ed. [PDF-preprint]
D.A. Freedman and P.B. Stark. The swine flu vaccine and Guillain-Barré syndrome. Evaluation Review, vol. 23 (1999) pp. 61947 [PDF-preprint]
D.A. Freedman and P. Humphreys. Are there algorithms that discover causal structure? Synthese, vol. 121 (1999) pp. 2954 [PDF-preprint]
D.A. Freedman. On the Bernstein-von Mises theorem with infinite dimensional parameters. Annals of Statistics, vol. 27 (1999) pp. 111940 [PDF-preprint]
D.A. Freedman and P. Diaconis. Iterated random functions. SIAM Review, vol. 41 (1999) pp. 4567 [SIAM]
D.A. Freedman, S.P. Klein, M. Ostland, and M.R. Roberts. Review of A Solution to the Ecological Inference Problem. Journal of the American Statistical Association, vol. 93 (1998) pp. 151822; [PDF-preprint] with discussion, vol. 94 (1999) pp. 35257. [PDF-preprint]
P. Diaconis and D.A. Freedman. Consistency of Bayes estimates for nonparametric regression: Normal theory. Bernoulli Journal, vol. 4 (1998) pp. 41144.
D.A. Freedman. De Finettis theorem in continuous time. In Statistics, Probability and Game Theory: Papers in Honor of David Blackwell. Institute of Mathematical Statistics Monograph 30 (1997) pp. 8398. T.S. Ferguson, L.S. Shapley, and J.B. MacQueen, eds.
T.H. Lin, L.S. Gold, and D.A. Freedman. Concordance between rats and mice in bioassays for carcinogenesis. Journal of Regulatory Toxicology and Pharmacology, vol. 23 (1996) pp. 22532. [preprint-PDF]
P. Humphreys and D.A. Freedman. The grand leap. British Journal of the Philosophy of Science, vol. 47 (1996) pp.11323. [Br J Phil Sci] [JSTOR]
K.W. Wachter and D.A. Freedman. Planning for the Census in the year 2000. Evaluation Review, vol. 20 (1996) pp. 355377 [PDF-preprint]
D.A. Freedman. Some issues in the foundation of statistics. Foundations of Science, vol. 1 (1995) pp.1983. Reprinted in Some Issues in the Foundation of Statistics, Kluwer, Dordrecht (1997). Bas C. van Fraassen, ed. [PDF-preprint]
S.P. Klein, D.A. Freedman, and R. Bolus, A statistical analysis of charging decisions in death-eligible federal cases: 19952000. [PDF] [NIJ website] [RAND website]
First Lectures in Statistics 215 [PDF]
What is a Random Variable? [PDF]
What is the Error Term in a Regression Equation? [PDF]
General Formulas for Bias and Variance in OLS [PDF]
Another Proof of the Gauss-Markov theorem [PDF] Yet Another Proof [PDF]
Notes on Regression Asymptotics [PDF]
If the Assumptions Break Down, OLS Can Be Biased and Nominal SEs Can Be Wrong [PDF]
Orthogonality Does Not Imply Asymptotic Normality [PDF]
Comments on Standardizing Path Diagrams: What Are the Parameters? [PDF]
Direct and Indirect Effects [PDF]
Replicating Gibson on McCarthy: Weighted Regression [PDF]
An Example to Illustrate the Asymptotics of IVLS [PDF]
Endogeneity Bias Is Contagious [PDF]
Can Exogeneity Be Determined from the Joint Distribution of Observables? [PDF]
An Example of Under-Identification [PDF]
The Neyman-Scott Paradox [PDF]
Notes on the MLE [PDF]
More on the MLE: The Likelihood Function Can Be Bimodal [PDF]
Hierarchical Linear Regression [PDF]
The Odds Ratio [PDF]
Notes on Ratio Estimators and the Delta Method [PDF]
On Kangaroos and Cookies: Causal Models for Paired Designs [PDF]
Greenwoods Formula [PDF]
How To Make Power Calculations [PDF]
Transcript: zipped text files [zip]
Extracts from the Current Population Survey: ASCII files [ftp]