Decision Processes by Using Bivariate Normal Quantile Pairs
Decision Processes by Using Bivariate Normal Quantile Pairs

Author: N. C. Das

Publisher: Springer

Published: 2015-10-07

Total Pages: 661

ISBN-13: 8132223640

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This book discusses equi-quantile values and their use in generating decision alternatives under the twofold complexities of uncertainty and dependence, offering scope for surrogating between two alternative portfolios when they are correlated. The book begins with a discussion on components of rationality and learning models as indispensable concepts in decision-making processes. It identifies three-fold complexities in such processes: uncertainty, dependence and dynamism. The book is a novel attempt to seek tangible solutions for such decision problems. To do so, four hundred tables of bi-quantile pairs are presented for carefully chosen grids. In fact, it is a two-variable generalization of the inverse normal integral table, which is used in obtaining bivariate normal quantile pairs for the given values of probability and correlation. When making decisions, only two of them have to be taken at a time. These tables are essential tools for decision-making under risk and dependence, and offer scope for delving up to a single step of dynamism. The book subsequently addresses averments dealing with applications and advantages. The content is useful to empirical scientists and risk-oriented decision makers who are often required to make choices on the basis of pairs of variables. The book also helps simulators seeking valid confidence intervals for their estimates, and particle physicists looking for condensed confidence intervals for Higgs–Boson utilizing the Bose–Einstein correlation given the magnitude of such correlations. Entrepreneurs and investors as well as students of management, statistics, economics and econometrics, psychology, psychometrics and psychographics, social sciences, geographic information system, geology, agricultural and veterinary sciences, medical sciences and diagnostics, and remote sensing will also find the book very useful.

An Author and Permuted Title Index to Selected Statistical Journals
An Author and Permuted Title Index to Selected Statistical Journals

Author: Brian L. Joiner

Publisher:

Published: 1970

Total Pages: 512

ISBN-13:

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All articles, notes, queries, corrigenda, and obituaries appearing in the following journals during the indicated years are indexed: Annals of mathematical statistics, 1961-1969; Biometrics, 1965-1969#3; Biometrics, 1951-1969; Journal of the American Statistical Association, 1956-1969; Journal of the Royal Statistical Society, Series B, 1954-1969,#2; South African statistical journal, 1967-1969,#2; Technometrics, 1959-1969.--p.iv.

Statistical Methods in Water Resources
Statistical Methods in Water Resources

Author: D.R. Helsel

Publisher: Elsevier

Published: 1993-03-03

Total Pages: 539

ISBN-13: 0080875084

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Data on water quality and other environmental issues are being collected at an ever-increasing rate. In the past, however, the techniques used by scientists to interpret this data have not progressed as quickly. This is a book of modern statistical methods for analysis of practical problems in water quality and water resources.The last fifteen years have seen major advances in the fields of exploratory data analysis (EDA) and robust statistical methods. The 'real-life' characteristics of environmental data tend to drive analysis towards the use of these methods. These advances are presented in a practical and relevant format. Alternate methods are compared, highlighting the strengths and weaknesses of each as applied to environmental data. Techniques for trend analysis and dealing with water below the detection limit are topics covered, which are of great interest to consultants in water-quality and hydrology, scientists in state, provincial and federal water resources, and geological survey agencies.The practising water resources scientist will find the worked examples using actual field data from case studies of environmental problems, of real value. Exercises at the end of each chapter enable the mechanics of the methodological process to be fully understood, with data sets included on diskette for easy use. The result is a book that is both up-to-date and immediately relevant to ongoing work in the environmental and water sciences.

Introduction to Probability
Introduction to Probability

Author: Joseph K. Blitzstein

Publisher: CRC Press

Published: 2014-07-24

Total Pages: 599

ISBN-13: 1466575573

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Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics, medicine, computer science, and information theory. The print book version includes a code that provides free access to an eBook version. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment.

An Introduction to Applied Multivariate Analysis with R
An Introduction to Applied Multivariate Analysis with R

Author: Brian Everitt

Publisher: Springer Science & Business Media

Published: 2011-04-23

Total Pages: 284

ISBN-13: 1441996508

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The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.