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Author : Alexander Luhm,Helmut Pruscha
Publisher : Unknown
Page : 16 pages
File Size : 48,7 Mb
Release : 1997
Category : Electronic
ISBN : OCLC:245859737
Author : Alexander Luhm
Publisher : Herbert Utz Verlag
Page : 184 pages
File Size : 55,7 Mb
Release : 1999
Category : Electronic
ISBN : 3896755900
Author : Alexander Luhm
Publisher : Unknown
Page : 0 pages
File Size : 53,8 Mb
Release : 1997
Category : Electronic
ISBN : OCLC:245859737
Author : Jesper Moller,Rasmus Plenge Waagepetersen
Publisher : CRC Press
Page : 320 pages
File Size : 41,5 Mb
Release : 2003-09-25
Category : Mathematics
ISBN : 0203496930
Spatial point processes play a fundamental role in spatial statistics and today they are an active area of research with many new applications. Although other published works address different aspects of spatial point processes, most of the classical literature deals only with nonparametric methods, and a thorough treatment of the theory and applications of simulation-based inference is difficult to find. Written by researchers at the top of the field, this book collects and unifies recent theoretical advances and examples of applications. The authors examine Markov chain Monte Carlo algorithms and explore one of the most important recent developments in MCMC: perfect simulation procedures.
Author : Alan Karr
Publisher : Routledge
Page : 509 pages
File Size : 50,7 Mb
Release : 2017-09-06
Category : Mathematics
ISBN : 9781351423830
Maintaining the excellent features that made the first edition so popular, this outstanding reference/text presents the only comprehensive treatment of the theory of point processes and statistical inference for point processes-highlighting both pointprocesses on the real line and sp;,.tial point processes. Thoroughly updated and revised to reflect changes since publication of the firstedition, the expanded Second EdiLion now contains a better organized and easierto-understand treatment of stationary point processes ... expanded treatment ofthe multiplicative intensity model ... expanded treatment of survival analysis . ..broadened consideration of applications ... an expanded and extended bibliographywith over 1,000 references ... and more than 3('() end-of-chapter exercises.
Author : Anonim
Publisher : Unknown
Page : 472 pages
File Size : 43,6 Mb
Release : 1998
Category : Mathematical statistics
ISBN : STANFORD:36105113052752
Author : International Conference on Mathematical Statistics
Publisher : Unknown
Page : 164 pages
File Size : 43,6 Mb
Release : 2000
Category : Electronic
ISBN : UOM:39015069223652
Author : Anonim
Publisher : Elsevier
Page : 1062 pages
File Size : 41,6 Mb
Release : 2005-11-29
Category : Mathematics
ISBN : 9780080461175
This volume describes how to develop Bayesian thinking, modelling and computation both from philosophical, methodological and application point of view. It further describes parametric and nonparametric Bayesian methods for modelling and how to use modern computational methods to summarize inferences using simulation. The book covers wide range of topics including objective and subjective Bayesian inferences with a variety of applications in modelling categorical, survival, spatial, spatiotemporal, Epidemiological, software reliability, small area and micro array data. The book concludes with a chapter on how to teach Bayesian thoughts to nonstatisticians. Critical thinking on causal effects Objective Bayesian philosophy Nonparametric Bayesian methodology Simulation based computing techniques Bioinformatics and Biostatistics
Author : M.S. Nikulin,N. Balakrishnan,Mounir Mesbah,Nikolaos Limnios
Publisher : Springer Science & Business Media
Page : 566 pages
File Size : 47,7 Mb
Release : 2013-11-11
Category : Mathematics
ISBN : 9780817682064
Parametric and semiparametric models are tools with a wide range of applications to reliability, survival analysis, and quality of life. This self-contained volume examines these tools in survey articles written by experts currently working on the development and evaluation of models and methods. While a number of chapters deal with general theory, several explore more specific connections and recent results in "real-world" reliability theory, survival analysis, and related fields. Specific topics covered include: * cancer prognosis using survival forests * short-term health problems related to air pollution: analysis using semiparametric generalized additive models * semiparametric models in the studies of aging and longevity This book will be of use as a reference text for general statisticians, theoreticians, graduate students, reliability engineers, health researchers, and biostatisticians working in applied probability and statistics.
Author : David Ruppert,M. P. Wand,R. J. Carroll
Publisher : Cambridge University Press
Page : 408 pages
File Size : 54,8 Mb
Release : 2003-07-14
Category : Mathematics
ISBN : 0521785162
Even experts on semiparametric regression should find something new here.
Author : Atanu Biswas,Sujay Datta,Jason P. Fine,Mark R. Segal
Publisher : John Wiley & Sons
Page : 623 pages
File Size : 50,5 Mb
Release : 2007-12-14
Category : Mathematics
ISBN : 9780470181195
The Most Comprehensive and Cutting-Edge Guide to Statistical Applications in Biomedical Research With the increasing use of biotechnology in medical research and the sophisticated advances in computing, it has become essential for practitioners in the biomedical sciences to be fully educated on the role statistics plays in ensuring the accurate analysis of research findings. Statistical Advances in the Biomedical Sciences explores the growing value of statistical knowledge in the management and comprehension of medical research and, more specifically, provides an accessible introduction to the contemporary methodologies used to understand complex problems in the four major areas of modern-day biomedical science: clinical trials, epidemiology, survival analysis, and bioinformatics. Composed of contributions from eminent researchers in the field, this volume discusses the application of statistical techniques to various aspects of modern medical research and illustrates how these methods ultimately prove to be an indispensable part of proper data collection and analysis. A structural uniformity is maintained across all chapters, each beginning with an introduction that discusses general concepts and the biomedical problem under focus and is followed by specific details on the associated methods, algorithms, and applications. In addition, each chapter provides a summary of the main ideas and offers a concluding remarks section that presents novel ideas, approaches, and challenges for future research. Complete with detailed references and insight on the future directions of biomedical research, Statistical Advances in the Biomedical Sciences provides vital statistical guidance to practitioners in the biomedical sciences while also introducing statisticians to new, multidisciplinary frontiers of application. This text is an excellent reference for graduate- and PhD-level courses in various areas of biostatistics and the medical sciences and also serves as a valuable tool for medical researchers, statisticians, public health professionals, and biostatisticians.
Author : Anonim
Publisher : Unknown
Page : 764 pages
File Size : 45,6 Mb
Release : 2007
Category : Electronic journals
ISBN : UCSD:31822036042414
A scientific and educational journal not only for professional statisticians but also for economists, business executives, research directors, government officials, university professors, and others who are seriously interested in the application of statistical methods to practical problems, in the development of more useful methods, and in the improvement of basic statistical data.
Author : Torben Martinussen,Thomas H. Scheike
Publisher : Springer Science & Business Media
Page : 470 pages
File Size : 53,7 Mb
Release : 2007-11-24
Category : Medical
ISBN : 9780387339603
This book studies and applies modern flexible regression models for survival data with a special focus on extensions of the Cox model and alternative models with the aim of describing time-varying effects of explanatory variables. Use of the suggested models and methods is illustrated on real data examples, using the R-package timereg developed by the authors, which is applied throughout the book with worked examples for the data sets.
Author : Alan F. Karr
Publisher : Unknown
Page : 512 pages
File Size : 46,8 Mb
Release : 1986
Category : Mathematics
ISBN : MINN:31951D000177426
Author : Ludwig Fahrmeir,Thomas Kneib,Stefan Lang,Brian D. Marx
Publisher : Springer Nature
Page : 759 pages
File Size : 41,7 Mb
Release : 2022-03-15
Category : Mathematics
ISBN : 9783662638828
Now in its second edition, this textbook provides an applied and unified introduction to parametric, nonparametric and semiparametric regression that closes the gap between theory and application. The most important models and methods in regression are presented on a solid formal basis, and their appropriate application is shown through numerous examples and case studies. The most important definitions and statements are concisely summarized in boxes, and the underlying data sets and code are available online on the book’s dedicated website. Availability of (user-friendly) software has been a major criterion for the methods selected and presented. The chapters address the classical linear model and its extensions, generalized linear models, categorical regression models, mixed models, nonparametric regression, structured additive regression, quantile regression and distributional regression models. Two appendices describe the required matrix algebra, as well as elements of probability calculus and statistical inference. In this substantially revised and updated new edition the overview on regression models has been extended, and now includes the relation between regression models and machine learning, additional details on statistical inference in structured additive regression models have been added and a completely reworked chapter augments the presentation of quantile regression with a comprehensive introduction to distributional regression models. Regularization approaches are now more extensively discussed in most chapters of the book. The book primarily targets an audience that includes students, teachers and practitioners in social, economic, and life sciences, as well as students and teachers in statistics programs, and mathematicians and computer scientists with interests in statistical modeling and data analysis. It is written at an intermediate mathematical level and assumes only knowledge of basic probability, calculus, matrix algebra and statistics.