Download e-book for kindle: Breakthroughs in Statistics I: Foundations and basic theory by Samuel Kotz, Norman Lloyd Johnson

By Samuel Kotz, Norman Lloyd Johnson

ISBN-10: 0387940375

ISBN-13: 9780387940373

It is a quantity choice of seminal papers within the statistical sciences written in past times a hundred years. those papers have every one had an excellent impression at the improvement of statistical thought and perform during the last century. every one paper is preceded by way of an creation written by means of an expert within the box supplying history info and assessing its effect. Readers will get pleasure from a clean outlook on now well-established positive aspects of statistical thoughts and philosophy through changing into accustomed to the methods they've been built. it really is was hoping that a few readers should be inspired to review many of the references supplied within the Introductions (and additionally within the papers themselves) and so reach a deeper heritage wisdom of the root in their paintings.

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J. & Thompson, E. A. (1992). Constrained Monte Carlo maximum likelihood for dependent data (with discussion), Journal of the Royal Statistical Society, Series B 38: 657-699. Geyer, C. & M¢ller, J. (1994). Simulation procedures and likelihood inference for spatial point processes, Scandinavian Journal of Statistics 21: 359-373. Gilks, W. , Richardson, S. & Spiegelhalter, D. J. (eds) (1996). Markov Chain Monte Carlo in Practice, Chapman and Hall, London. Gilks, W. & Wild, P. (1992). Adaptive rejection sampling for Gibbs sampling, Applied Statistics 41: 37-348.

F. M. (1990). Sampling-based approaches to calculating marginal densities, Journal of the American Statistical Association 85: 398-409. Gelman, A. & Meng, X. L. (1998). Simulating normalising constants: From importance sampling to bridge sampling to path sampling, Statistical Science 13: 163-185. , Roberts, G. 0. & Gilks, W. (1996). Efficient Metropolis jumping rules, Bayesian Statistics V pp. 599-608. Gelman, A. & Rubin, D. (1992). Inference from iterative simulation using multiple sequences, Statistical Science 7: 457-472.

1999). Markov chain Monte Carlo and spatial point processes, in 0. E. Barndorff-Nielsen, W. S. Kendall & M. N. M. van Lieshout (eds), Stochastic Geometry: Likelihood and Computations, number 80 in Monographs on Statistics and Applied Probability, Chapman and Hall/CRC, Boca Raton, pp. 141-172. Neal, R. M. (2002). Slice sampling, with discussion, Annals of Statistics . To appear. Newton, M. A. & Raftery, A. E. (1994). Approximate Bayesian inference by the weighted likelihood bootstrap (with discussion), Journal of the Royal Statistical Society, Series B 56: 1-48.

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Breakthroughs in Statistics I: Foundations and basic theory by Samuel Kotz, Norman Lloyd Johnson


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