David A. Freedman

Broschiertes Buch

Statistical Models and Causal Inference

Beschreibung

David A. Freedman presents here a definitive synthesis of his views on the foundations and limitations of statistical modeling in the social sciences, He maintains that many new technical approaches to statistical modeling constitute not progress, but regress, and he shows why these methods are not reliable.

Autor

David A. Freedman (1938-2008) was Professor of Statistics at the University of California, Berkeley. He was a distinguished mathematical statistician whose theoretical research included the analysis of martingale inequalities, Markov processes, de Finetti's theorem, consistency of Bayes estimators, sampling, the bootstrap, and procedures for testing and evaluating models of methods for causal inference. Freedman published widely on the application - and misapplication - of statistics in works within a variety of social sciences, including epidemiology, demography, political science, public policy, and law. He emphasized exposing and checking the assumptions that underlie standard methods, as well as understanding how those methods behave when the assumptions are false - for example, how regression models behave when fitted to data from randomized experiments. He had a remarkable talent for integrating carefully honed statistical arguments with compelling empirical applications and illustrations. Freedman was a member of the American Academy of Arts and Sciences, and in 2003 he received the National Academy of Science's John J. Carty Award, for his 'profound contributions to the theory and practice of statistics'.

Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 416
• Erscheinungstermin: 23. März 2015
• Englisch
• Abmessung: 234mm x 156mm x 23mm
• Gewicht: 610g
• ISBN-13: 9780521123907
• ISBN-10: 0521123909
• Artikelnr.: 28022927