Breakthroughs in Statistics

Häftad, Engelska, 1997

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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. 0-387-94037-5 Breakthroughs in Statistics Volume II: Methodology and Distribution Samuel Kotz and Norman L. Johnson, Editors 1993. 600 pp. Softcover. 0-387-94039-1

Produktinformation

Utgivningsdatum1997-08-07
Mått155 x 235 x 32 mm
Vikt885 g
FormatHäftad
SpråkEngelska
SerieSpringer Series in Statistics
Antal sidor561
Upplaga1997
FörlagSpringer-Verlag New York Inc.
ISBN9780387949895

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of Volume III.- • Introduction by R. E. Fancher.- Galton, F.(1889). Co-Relations and Their Measurement, Chiefly from Anthropometric Data.- • Introduction by K. Dietz.- McKendrick, A.G. (1926). Applications of Mathematics to Medical Problems.- • Introduction by D.R. Cox.- Yates, F. and Cochran, W.G. (1938). The Analysis of Groups of Experiments.- • Introduction by P.K. Sen.- Rao, C.R. (1948). Large Sample Tests of Statistical Hypotheses Concerning Several Parameters with Applications to Problems of Estimation.- • 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.- • Introduction by P.J. Huber.- Metropolis, N., Rosenbluth, A.W., Rosenbiuth, M.N., Teller, A.H., and Teller, E. (1953). Equation of State Calculations by Fast Computing Machines and Geman, S. and Geman, D. (1984). Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images.- • Introduction by M. Calder and R. A. Davis.- Whittle, P. (1953). The Analysis of Multiple Stationary Time Series.- • Introduction by E. Ronchetti.- Daniels, H.E. (1954). Saddlepoint Approximations in Statistics.- • Introduction by I.J. Good.- Cooley, J.W. and Tukey, J.W. (1965). An Algorithm for the Machine Calculation of Complex Fourier Series.- • Introduction by R.J. Reran.- Hájek, J. (1970). A Characterization of Limiting Distributions of Regular Estimates.- • Introduction by T.L. Lai.- Hastings, W.K. (1970). Monte Carlo Sampling Methods Using Markov Chains and Their Applications.- • Introduction by J.J. Deely.- Lindley, D.V. and Smith, A.F.M. (1972). Bayes Estimates for the Linear Model.- • Introduction by R.L. Smith.- Besag, J. (1974). Spatial Interaction and theStatistical Analysis of Lattice Systems.- • Introduction by J.F. Pfanzagl.- Levit, B.Ya. (1974). On Optimality of Some Statistical Estimates.- • Introduction by I. W. McKeague.- Aalen, O. (1978). Nonparametric Estimation of Partial Transition Probabilities in Multiple Decrement Models.- • Introduction by G. G. Letac.- Morris, C.N. (1982). Natural Exponential Families with Quadratic Variance Functions.- • Introduction by T. DiCiccio.- Barndorff-Nielsen, O.E. (1983). On a Formula for the Distribution of the Maximum Likelihood Estimator.- • Introduction by D. G. Simpson.- Rousseeuw, P.J. (1984). Least Median of Squares Regression.- • Introduction by P.J. Dig gle.- Liang, K.-Y. and Zeger, S.L. (1986). Longitudinal Data Analysis Using Generalized Linear Models.- • Introduction by E. Mammen.- Hall, P. (1988). Theoretical Comparison of Bootstrap Confidence Intervals.- • Introduction by D. M. Titterington.- Gelfand, A.E. and Smith, A.F.M. (1990). Sampling-Based Approaches to Calculating Marginal Densities.

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