Breakthroughs in Statistics (Springer Series in Statistics)

Kotz, Samuel [Editor]; Johnson, Norman L. [Editor];

ISBN 10: 0387949895 / ISBN 13: 9780387949895

Editore: Springer, 1997

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Dati bibliografici

• Titolo: Breakthroughs in Statistics (Springer Series in Statistics)
• Editore: Springer
• Data di pubblicazione: 1997
• Lingua: Inglese
• ISBN 10: 0387949895
• ISBN 13: 9780387949895
• Rilegatura: paperback
• Condizione: New
• Peso: 64 once

Descrizione articolo

Riassunto

This is author-approved bcc: This is the third volume of a collection of seminal papers in the statistical sciences written during the past 110 years. These papers have each had an outstanding influence on the development of statistical theory and practice over the last century. Each paper is preceded by an introduction written by an authority in the field providing background information and assessing its influence. Volume III concerntrates on articles from the 1980's while including some earlier articles not included in Volume I and II. Samuel Kotz is Professor of Statistics in the College of Business and Management at the University of Maryland. Norman L. Johnson is Professor Emeritus of Statistics at the University of North Carolina. Also available: Breakthroughs in Statistics Volume I: Foundations and Basic Theory Samuel Kotz and Norman L. Johnson, Editors 1993. 631 pp. Softcover. ISBN 0-387-94037-5 Breakthroughs in Statistics Volume II: Methodology and Distribution Samuel Kotz and Norman L. Johnson, Editors 1993. 600 pp. Softcover. ISBN 0-387-94039-1

Contenuti

1. Introduction by R.E.Fancher: Galton,F. (1889) Co-relations and their measurement, chiefly from anthropometric data; 2. Introduction by K. Dietz: McKendrick, A.G.(1926) Applications of mathematics to medical problems; 3.Introduction by D.R. Cox: Yates, F.and Cochran, W.G.(1938) The analysis of groups of experiments; 4. Introduction by P.K. Sen: Rao, C.R.(1948) Large sample tests of statistical hypotheses concerning several parameters with application to problems of estimation; 5. Introduction by M.H. Gail: Cornfield, J.(1951) A method of estimating comparative rates from clinical data. Applications to cancer of the lung, breast and cervix; 6. Introduction by M. Calder and R.A. Davis: Whittle, P.(1953) The analysis of multiple stationary time series; 7. Introduction by E. Ronchetti: Daniels, H.E.(1954) Saddlepoint approximations in statistics; 8. Introduction by I.J. Good: Cooley, J.W. and Turkey, J.W.(1965) An algorithm for the machine calculation of complex Fourier series; 9. Introduction by R.J. Beran: Hajek, J.(1970) A characterization of limiting distributions of regular estimates; 10. Introduction by T.L. Lai: Hastings, W.K.(1970) Monte Carlo sampling methods using Markov chains and their applications; 11. Introduction by J.J. Deely: Lindley, D.V.and Smith, A.F.M.(1972) Bayes estimates for the linear model; 12. Introduction by R.L. Smith: Besag, J.(1974) Spatial interaction and the statistical analysis of lattice systems; 13. Introduction by J.F. Pfanzagl: Levit, B.Ya.(1974) On optimality of some statistical estimates; 14. Introduction by I.W. McKeague: Aalen, O.(1978) Nonparametric estimation of partial transition probabilities in multiple decrement models; 15. Introduction by G.G. Letac: Morris, C.N.(1982) Natural exponential families with quadratic variance functions; 16. Introduction by T.H. DiCiccio: Barndorff-Nielsen, O.E.(1983) On a formula for the distribution of the maximum likelihood estimator; 17. Introduction by P.J. Huber: Geman, S. and Geman, D.(1984) Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images; 18. Introduction by D.G. Simpson: Rousseeuw, P.J.(1984) Least median of squares regressions; 19. Introduction by P.J. Diggle. Liang, K.Y.and Zeger, S.L.(1986) Longitudinal data analysis using generalized linear models; 20. Introduction by E. Mammen. Hall, P.(1988) Theoretical comparison of bootstrap confidence intervals; 21. Introduction by D.M. Timerington: Gelfand, A.E.and Smith, A.F.M.(1990) Sampling-based approaches to calculating marginal densities.