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Mathematical Statistics with Applications
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TLDR
Step-by-step procedure to solve real problems, making the topic more accessible, and exercises blend theory and modern applications.Abstract:
Mathematical Statistics with Applications provides a calculus-based theoretical introduction to mathematical statistics while emphasizing interdisciplinary applications as well as exposure to modern statistical computational and simulation concepts that are not covered in other textbooks. Includes the Jackknife, Bootstrap methods, the EM algorithms and Markov chain Monte Carlo methods. Prior probability or statistics knowledge is not required. ? Step-by-step procedure to solve real problems, making the topic more accessible ? Exercises blend theory and modern applications ? Practical, real-world chapter projects ? Provides an optional section in each chapter on using Minitab, SPSS and SAS commands ? Student solutions manual, instructors manual and data disk availableread more
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Journal ArticleDOI
Probability and Statistical Inference
TL;DR: The Foundations of Statistics By Prof. Leonard J. Savage as mentioned in this paper, p. 48s. (Wiley Publications in Statistics.) Pp. xv + 294. (New York; John Wiley and Sons, Inc., London: Chapman and Hall, Ltd., 1954).
Book ChapterDOI
CHAPTER 12 – Random Effects Analysis
W.D. Penny,A.J. Holmes +1 more
TL;DR: In this article, the statistical inferences obtained from functional imaging studies involving many subjects are discussed and implemented using the computationally efficient summary statistic approach, which is mathematically equivalent to the more computationally demanding maximum likelihood procedure.
Journal ArticleDOI
Processed nerve allografts for peripheral nerve reconstruction: a multicenter study of utilization and outcomes in sensory, mixed, and motor nerve reconstructions.
Darrell Brooks,Renata V. Weber,Jerome D. Chao,Brian Rinker,Jozef Zoldos,Michael R. Robichaux,Sebastian B. Ruggeri,Kurt A. Anderson,Ekkehard E. Bonatz,Scott M. Wisotsky,Mickey S. Cho,Christopher Wilson,Ellis O. Cooper,John V. Ingari,Bauback Safa,Brian M. Parrett,Gregory M. Buncke +16 more
TL;DR: The outcomes for safety and meaningful recovery observed in this study compare favorably to those reported in the literature for nerve autograft and are higher than those reported for nerve conduits.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
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Equation of state calculations by fast computing machines
TL;DR: In this article, a modified Monte Carlo integration over configuration space is used to investigate the properties of a two-dimensional rigid-sphere system with a set of interacting individual molecules, and the results are compared to free volume equations of state and a four-term virial coefficient expansion.
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Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more
TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
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Monte Carlo Sampling Methods Using Markov Chains and Their Applications
TL;DR: A generalization of the sampling method introduced by Metropolis et al. as mentioned in this paper is presented along with an exposition of the relevant theory, techniques of application and methods and difficulties of assessing the error in Monte Carlo estimates.
Book
Bootstrap Methods and Their Application
Anthony C. Davison,David Hinkley +1 more
TL;DR: In this paper, a broad and up-to-date coverage of bootstrap methods, with numerous applied examples, developed in a coherent way with the necessary theoretical basis, is given, along with a disk of purpose-written S-Plus programs for implementing the methods described in the text.