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Author: J.A. John
Publisher: CRC Press
ISBN: 9780412575808
Category : Mathematics
Languages : en
Pages : 272
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Book Description
Cyclic and Computer Generated Designs is a much-expanded and updated version of the well-received monograph, Cyclic Designs . The book is primarily concerned with the construction and analysis of designs with a number of different blocking structures, such as revolvable designs, row-column designs, and Latinized designs. It describes how appropriate and efficient designs can be constructed through the use of cyclic methods and recently developed computer algorithms. In this new edition, a greater emphasis is given to the construction and properties of resolvable block and row-column designs. A general theory for single, fractional and multiple replicate factorial designs is presented. Cyclic methods are used to construct most of these designs. Some new work on the use of computer algorithms for setting out factorial experiments in row-column designs is described. All the designs discussed can be analyzed using the generalized least squares theory given in the book. Two experiments, with analyses, are described in detail.
Author: J.A. John
Publisher: CRC Press
ISBN: 9780412575808
Category : Mathematics
Languages : en
Pages : 272
Get Book Here
Book Description
Cyclic and Computer Generated Designs is a much-expanded and updated version of the well-received monograph, Cyclic Designs . The book is primarily concerned with the construction and analysis of designs with a number of different blocking structures, such as revolvable designs, row-column designs, and Latinized designs. It describes how appropriate and efficient designs can be constructed through the use of cyclic methods and recently developed computer algorithms. In this new edition, a greater emphasis is given to the construction and properties of resolvable block and row-column designs. A general theory for single, fractional and multiple replicate factorial designs is presented. Cyclic methods are used to construct most of these designs. Some new work on the use of computer algorithms for setting out factorial experiments in row-column designs is described. All the designs discussed can be analyzed using the generalized least squares theory given in the book. Two experiments, with analyses, are described in detail.
Author: Byron Jones
Publisher: CRC Press
ISBN: 1420036092
Category : Mathematics
Languages : en
Pages : 390
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Book Description
The first edition of Design and Analysis of Cross-Over Trials quickly became the standard reference on the subject and has remained so for more than 12 years. In that time, however, the use of cross-over trials has grown rapidly, particularly in the pharmaceutical arena, and researchers have made a number of advances in both the theory and methods applicable to these trials. Completely revised and updated, the long-awaited second edition of this classic text retains its predecessor's careful balance of theory and practice while incorporating new approaches, more data sets, and a broader scope. Enhancements in the second edition include: A new chapter on bioequivalence Recently developed methods for analyzing longitudinal continuous and categorical data Real-world examples using the SAS system A comprehensive catalog of designs, datasets, and SAS programs available on a companion Web site at www.crcpress.com The authors' exposition gives a clear, unified account of the design and analysis of cross-over trials from a statistical perspective along with their methodological underpinnings. With SAS programs and a thorough treatment of design issues, Design and Analysis of Cross-Over Trials, Second Edition sets a new standard for texts in this area and undoubtedly will be of direct practical value for years to come.
Author: A.D. Gordon
Publisher: CRC Press
ISBN: 9781584888536
Category : Mathematics
Languages : en
Pages : 274
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Book Description
As the amount of information recorded and stored electronically grows ever larger, it becomes increasingly useful, if not essential, to develop better and more efficient ways to summarize and extract information from these large, multivariate data sets. The field of classification does just that-investigates sets of "objects" to see if they can be summarized into a small number of classes comprising similar objects. Researchers have made great strides in the field over the last twenty years, and classification is no longer perceived as being concerned solely with exploratory analyses. The second edition of Classification incorporates many of the new and powerful methodologies developed since its first edition. Like its predecessor, this edition describes both clustering and graphical methods of representing data, and offers advice on how to decide which methods of analysis best apply to a particular data set. It goes even further, however, by providing critical overviews of recent developments not widely known, including efficient clustering algorithms, cluster validation, consensus classifications, and the classification of symbolic data. The author has taken an approach accessible to researchers in the wide variety of disciplines that can benefit from classification analysis and methods. He illustrates the methodologies by applying them to data sets-smaller sets given in the text, larger ones available through a Web site. Large multivariate data sets can be difficult to comprehend-the sheer volume and complexity can prove overwhelming. Classification methods provide efficient, accurate ways to make them less unwieldy and extract more information. Classification, Second Edition offers the ideal vehicle for gaining the background and learning the methodologies-and begin putting these techniques to use.
Author: Trevor F. Cox
Publisher: CRC Press
ISBN: 9781420036121
Category : Mathematics
Languages : en
Pages : 332
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Book Description
Multidimensional scaling covers a variety of statistical techniques in the area of multivariate data analysis. Geared toward dimensional reduction and graphical representation of data, it arose within the field of the behavioral sciences, but now holds techniques widely used in many disciplines. Multidimensional Scaling, Second Edition extends the popular first edition and brings it up to date. It concisely but comprehensively covers the area, summarizing the mathematical ideas behind the various techniques and illustrating the techniques with real-life examples. A computer disk containing programs and data sets accompanies the book.
Author: Thomas P. Hettmansperger
Publisher: CRC Press
ISBN: 1439809097
Category : Mathematics
Languages : en
Pages : 554
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Book Description
Presenting an extensive set of tools and methods for data analysis, Robust Nonparametric Statistical Methods, Second Edition covers univariate tests and estimates with extensions to linear models, multivariate models, times series models, experimental designs, and mixed models. It follows the approach of the first edition by developing rank-based m
Author: Ryan Martin
Publisher: CRC Press
ISBN: 1439886512
Category : Mathematics
Languages : en
Pages : 274
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Book Description
A New Approach to Sound Statistical ReasoningInferential Models: Reasoning with Uncertainty introduces the authors' recently developed approach to inference: the inferential model (IM) framework. This logical framework for exact probabilistic inference does not require the user to input prior information. The authors show how an IM produces meaning
Author: Paul Gustafson
Publisher: CRC Press
ISBN: 1439869405
Category : Mathematics
Languages : en
Pages : 196
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Book Description
Bayesian Inference for Partially Identified Models: Exploring the Limits of Limited Data shows how the Bayesian approach to inference is applicable to partially identified models (PIMs) and examines the performance of Bayesian procedures in partially identified contexts. Drawing on his many years of research in this area, the author presents a thorough overview of the statistical theory, properties, and applications of PIMs. The book first describes how reparameterization can assist in computing posterior quantities and providing insight into the properties of Bayesian estimators. It next compares partial identification and model misspecification, discussing which is the lesser of the two evils. The author then works through PIM examples in depth, examining the ramifications of partial identification in terms of how inferences change and the extent to which they sharpen as more data accumulate. He also explains how to characterize the value of information obtained from data in a partially identified context and explores some recent applications of PIMs. In the final chapter, the author shares his thoughts on the past and present state of research on partial identification. This book helps readers understand how to use Bayesian methods for analyzing PIMs. Readers will recognize under what circumstances a posterior distribution on a target parameter will be usefully narrow versus uselessly wide.
Author: Patrick Laurie Davies
Publisher: CRC Press
ISBN: 148221587X
Category : Mathematics
Languages : en
Pages : 318
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Book Description
The First Detailed Account of Statistical Analysis That Treats Models as ApproximationsThe idea of truth plays a role in both Bayesian and frequentist statistics. The Bayesian concept of coherence is based on the fact that two different models or parameter values cannot both be true. Frequentist statistics is formulated as the problem of estimating
Author: Jose Casals
Publisher: CRC Press
ISBN: 131536025X
Category : Mathematics
Languages : en
Pages : 286
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Book Description
The state-space approach provides a formal framework where any result or procedure developed for a basic model can be seamlessly applied to a standard formulation written in state-space form. Moreover, it can accommodate with a reasonable effort nonstandard situations, such as observation errors, aggregation constraints, or missing in-sample values. Exploring the advantages of this approach, State-Space Methods for Time Series Analysis: Theory, Applications and Software presents many computational procedures that can be applied to a previously specified linear model in state-space form. After discussing the formulation of the state-space model, the book illustrates the flexibility of the state-space representation and covers the main state estimation algorithms: filtering and smoothing. It then shows how to compute the Gaussian likelihood for unknown coefficients in the state-space matrices of a given model before introducing subspace methods and their application. It also discusses signal extraction, describes two algorithms to obtain the VARMAX matrices corresponding to any linear state-space model, and addresses several issues relating to the aggregation and disaggregation of time series. The book concludes with a cross-sectional extension to the classical state-space formulation in order to accommodate longitudinal or panel data. Missing data is a common occurrence here, and the book explains imputation procedures necessary to treat missingness in both exogenous and endogenous variables. Web Resource The authors’ E4 MATLAB® toolbox offers all the computational procedures, administrative and analytical functions, and related materials for time series analysis. This flexible, powerful, and free software tool enables readers to replicate the practical examples in the text and apply the procedures to their own work.
Author: Ruth M. Pfeiffer
Publisher: CRC Press
ISBN: 1466561688
Category : Mathematics
Languages : en
Pages : 227
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Book Description
Absolute Risk: Methods and Applications in Clinical Management and Public Health provides theory and examples to demonstrate the importance of absolute risk in counseling patients, devising public health strategies, and clinical management. The book provides sufficient technical detail to allow statisticians, epidemiologists, and clinicians to build, test, and apply models of absolute risk. Features: Provides theoretical basis for modeling absolute risk, including competing risks and cause-specific and cumulative incidence regression Discusses various sampling designs for estimating absolute risk and criteria to evaluate models Provides details on statistical inference for the various sampling designs Discusses criteria for evaluating risk models and comparing risk models, including both general criteria and problem-specific expected losses in well-defined clinical and public health applications Describes many applications encompassing both disease prevention and prognosis, and ranging from counseling individual patients, to clinical decision making, to assessing the impact of risk-based public health strategies Discusses model updating, family-based designs, dynamic projections, and other topics Ruth M. Pfeiffer is a mathematical statistician and Fellow of the American Statistical Association, with interests in risk modeling, dimension reduction, and applications in epidemiology. She developed absolute risk models for breast cancer, colon cancer, melanoma, and second primary thyroid cancer following a childhood cancer diagnosis. Mitchell H. Gail developed the widely used "Gail model" for projecting the absolute risk of invasive breast cancer. He is a medical statistician with interests in statistical methods and applications in epidemiology and molecular medicine. He is a member of the National Academy of Medicine and former President of the American Statistical Association. Both are Senior Investigators in the Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health.
