American Statistical Association

Taken from E. P. King, J. Am. Statist. Assoc., 1958,48, 531, by permission of the American Statistical Association. 

Green, B. F. (1952). Latent structure class analysis and its relation to factor analysis. Journal of American Statistical Association, 47, 71-76. 

Addams, F.G. and Griffin, J.M. (1972) Economic-linear programming model of the U.S. petroleum refining industry. Journal of American Statistics Association, 67, 542. 

Heyse J, Cook J. A new measure of cost-effectiveness in comparative clinical trials. American Statistical Association Annual Meeting 1992 Aug 9-13 Boston, MA. 

Kaplan EL and Meier P (1958) Non-parametric estimation from incomplete observations Journal of the American Statistical Association, 53, 457-M81 Kaul S and Diamond GA (2006) Good enough a primer on the analysis and interpretation of non-inferiority trials Annals of Internal Medicine, 145, 62-69 Kay R (1995) Some fundamental statistical concepts in clinical trials and their application in herpes zoster Antiviral Chemistry and Chemotherapy, 6, Supplement 1, 28-33 Kay R (2004) An explanation of the hazard ratio Pharmaceutical Statistics, 3, 295-297... 

Ziegel, E. R., Discussion of the paper Designs for Mixture Experiments Involving Process Variables , Presented at the Annual Meeting of the American Statistical Association, Chicago, IL, 1977. 

Metropolis N, Ulam S (1949) The Monte Carlo method. Journal of the American Statistical Association, 44(247) 335-341. 

Efron, B., Tibshirani, R., Storey, J. D., and Tusher, V. (2001). Empirical Bayes analysis of a microarray experiment. Journal of the American Statistical Association, 96, 1151-1161. Firmer, H. and Roter, M. (2001). On the false discovery rate and expected type I errors. Biometrical Journal, 43, 985-1005. 

Currin, C., Mitchell, T., Morris, M., and Ylvisaker, D. (1991). Bayesian prediction of deterministic functions, with applications to the design and analysis of computer experiments. Journal of the American Statistical Association, 86, 953-963. 

Leber, P. (1988). The emperor s clothes revisited. In Proceedings of the American Statistical Association, Biopharmaceuticals Section (pp. 9—14). Alexandria, VA ASA (New Orleans). 

Hwang, J. T. G. Ding, A. A. (1997). Prediction intervals for artificial neural networks. American Statistical Association 92,748-57. 

Ginevan, M.E. (1993). Bounding the mean concentration for environmental contaminants when all observations are below the limit of detection, in 1993 Proceedings of the Section on Statistics and the Environment, San Francisco, CA, USA, 8-12 August, 1993, American Statistical Association, Alexandria, VA, USA, pp. 123-128. 

The ASA-SIAM Series on Statistics and Applied Probabiiity is pubiished jointly by the American Statistical Association and the Society for Industriai and Applied Mathematics. The series consists of a broad spectrum of books on topics in statistics and applied probability. The purpose of the series is to provide inexpensive, quaiity publications of interest to the intersecting membership of the two societies. 

Copyright 2001 by the American Statistical Association and the Society for Industrial and Applied Mathematics. 

Efron, B. (1983). Estimating the Error Rate of a Prediction Rule Improvement on Cross-Validation. Journal of American Statistical Association, 78, 316-331. 

Rand, W. (1971). Objective Criteria for the Evaluation of Clustering Algorithms, Journal of the American Statistical Association 66, 846-850. 

Huque MF. Experiences with meta-analysis in NDA submissions. Proceedings of the Biopharmaceutical Section of the American Statistical Association 1988 2 28-33. 

Geyer, C. J., Markov chain Monte Carlo maximum likelihood. In Computing Science and Statistics Proceedings of the 23rd Symposium on the Interface. American Statistical Association, New York, 1991, pp. 156-163. 

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