Journal of Statistical Planning and Inference PDF Download
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Author: North-Holland Publishing Company
Publisher:
ISBN:
Category :
Languages : en
Pages : 1304
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Book Description
Author: North-Holland Publishing Company
Publisher:
ISBN:
Category :
Languages : en
Pages : 1304
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Book Description
Author: Ishwar V. Basawa
Publisher:
ISBN:
Category :
Languages : en
Pages : 217
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Book Description
Author: Subir Ghosh
Publisher: Wiley
ISBN: 9781119962786
Category : Science
Languages : en
Pages : 512
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Book Description
This book introduces statistical planning and inference, presenting both classical theory and the major developments in the field. Each chapter presents problems and their solutions along with illustrative examples to introduce concepts and methods, and is supported by a supplementary website featuring guidance on how to implement methods using R.
Author:
Publisher:
ISBN:
Category : Mathematical statistics
Languages : en
Pages : 870
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Book Description
Author: Elsevier Science (Firm)
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Nils Lid Hjort
Publisher: Cambridge University Press
ISBN: 1139484605
Category : Mathematics
Languages : en
Pages : 309
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Book Description
Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is the perfect guide to what can seem a forbidding landscape. Tutorial chapters by Ghosal, Lijoi and Prünster, Teh and Jordan, and Dunson advance from theory, to basic models and hierarchical modeling, to applications and implementation, particularly in computer science and biostatistics. These are complemented by companion chapters by the editors and Griffin and Quintana, providing additional models, examining computational issues, identifying future growth areas, and giving links to related topics. This coherent text gives ready access both to underlying principles and to state-of-the-art practice. Specific examples are drawn from information retrieval, NLP, machine vision, computational biology, biostatistics, and bioinformatics.
Author: Friedrich Pukelsheim
Publisher: SIAM
ISBN: 0898716047
Category : Mathematics
Languages : en
Pages : 527
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Book Description
Optimal Design of Experiments offers a rare blend of linear algebra, convex analysis, and statistics. The optimal design for statistical experiments is first formulated as a concave matrix optimization problem. Using tools from convex analysis, the problem is solved generally for a wide class of optimality criteria such as D-, A-, or E-optimality. The book then offers a complementary approach that calls for the study of the symmetry properties of the design problem, exploiting such notions as matrix majorization and the Kiefer matrix ordering. The results are illustrated with optimal designs for polynomial fit models, Bayes designs, balanced incomplete block designs, exchangeable designs on the cube, rotatable designs on the sphere, and many other examples.
Author: Sam Efromovich
Publisher: CRC Press
ISBN: 1351679848
Category : Mathematics
Languages : en
Pages : 448
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Book Description
This book presents a systematic and unified approach for modern nonparametric treatment of missing and modified data via examples of density and hazard rate estimation, nonparametric regression, filtering signals, and time series analysis. All basic types of missing at random and not at random, biasing, truncation, censoring, and measurement errors are discussed, and their treatment is explained. Ten chapters of the book cover basic cases of direct data, biased data, nondestructive and destructive missing, survival data modified by truncation and censoring, missing survival data, stationary and nonstationary time series and processes, and ill-posed modifications. The coverage is suitable for self-study or a one-semester course for graduate students with a prerequisite of a standard course in introductory probability. Exercises of various levels of difficulty will be helpful for the instructor and self-study. The book is primarily about practically important small samples. It explains when consistent estimation is possible, and why in some cases missing data should be ignored and why others must be considered. If missing or data modification makes consistent estimation impossible, then the author explains what type of action is needed to restore the lost information. The book contains more than a hundred figures with simulated data that explain virtually every setting, claim, and development. The companion R software package allows the reader to verify, reproduce and modify every simulation and used estimators. This makes the material fully transparent and allows one to study it interactively. Sam Efromovich is the Endowed Professor of Mathematical Sciences and the Head of the Actuarial Program at the University of Texas at Dallas. He is well known for his work on the theory and application of nonparametric curve estimation and is the author of Nonparametric Curve Estimation: Methods, Theory, and Applications. Professor Sam Efromovich is a Fellow of the Institute of Mathematical Statistics and the American Statistical Association.
Author: Haim Abramovich
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110537575
Category : Technology & Engineering
Languages : en
Pages : 318
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Book Description
Advanced Aerospace Materials is intended for engineers and students of aerospace, materials, and mechanical engineering. It covers the transition from aluminum to composite materials for aerospace structures and will include essential and advanced analyses used in today’s aerospace industries. Various aspects of design, failure and monitoring of structural components will be derived and presented accompanied by relevant formulas and analyses.
Author: Yves Tille
Publisher: John Wiley & Sons
ISBN: 0470682051
Category : Mathematics
Languages : en
Pages : 447
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Book Description
A much-needed reference on survey sampling and its applications that presents the latest advances in the field Seeking to show that sampling theory is a living discipline with a very broad scope, this book examines the modern development of the theory of survey sampling and the foundations of survey sampling. It offers readers a critical approach to the subject and discusses putting theory into practice. It also explores the treatment of non-sampling errors featuring a range of topics from the problems of coverage to the treatment of non-response. In addition, the book includes real examples, applications, and a large set of exercises with solutions. Sampling and Estimation from Finite Populations begins with a look at the history of survey sampling. It then offers chapters on: population, sample, and estimation; simple and systematic designs; stratification; sampling with unequal probabilities; balanced sampling; cluster and two-stage sampling; and other topics on sampling, such as spatial sampling, coordination in repeated surveys, and multiple survey frames. The book also includes sections on: post-stratification and calibration on marginal totals; calibration estimation; estimation of complex parameters; variance estimation by linearization; and much more. Provides an up-to-date review of the theory of sampling Discusses the foundation of inference in survey sampling, in particular, the model-based and design-based frameworks Reviews the problems of application of the theory into practice Also deals with the treatment of non sampling errors Sampling and Estimation from Finite Populations is an excellent book for methodologists and researchers in survey agencies and advanced undergraduate and graduate students in social science, statistics, and survey courses.
