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Author: C.C. Heyde Publisher: Springer Science & Business Media ISBN: 1461301793 Category : Mathematics Languages : en Pages : 505
Book Description
Written by leading statisticians and probabilists, this volume consists of 104 biographical articles on eminent contributors to statistical and probabilistic ideas born prior to the 20th Century. Among the statisticians covered are Fermat, Pascal, Huygens, Neumann, Bernoulli, Bayes, Laplace, Legendre, Gauss, Poisson, Pareto, Markov, Bachelier, Borel, and many more.
Author: C.C. Heyde Publisher: Springer Science & Business Media ISBN: 1461301793 Category : Mathematics Languages : en Pages : 505
Book Description
Written by leading statisticians and probabilists, this volume consists of 104 biographical articles on eminent contributors to statistical and probabilistic ideas born prior to the 20th Century. Among the statisticians covered are Fermat, Pascal, Huygens, Neumann, Bernoulli, Bayes, Laplace, Legendre, Gauss, Poisson, Pareto, Markov, Bachelier, Borel, and many more.
Author: Li-Chun Zhang Publisher: CRC Press ISBN: 1000540936 Category : Mathematics Languages : en Pages : 128
Book Description
Many technological, socio-economic, environmental, biomedical phenomena exhibit an underlying graph structure. Valued graph allows one to incorporate the connections or links among the population units in addition. The links may provide effectively access to the part of population that is the primary target, which is the case for many unconventional sampling methods, such as indirect, network, line-intercept or adaptive cluster sampling. Or, one may be interested in the structure of the connections, in terms of the corresponding graph properties or parameters, such as when various breadth- or depth-first non-exhaustive search algorithms are applied to obtain compressed views of large often dynamic graphs. Graph sampling provides a statistical approach to study real graphs from either of these perspectives. It is based on exploring the variation over all possible sample graphs (or subgraphs) which can be taken from the given population graph, by means of the relevant known sampling probabilities. The resulting design-based inference is valid whatever the unknown properties of the given real graphs. One-of-a-kind treatise of multidisciplinary topics relevant to statistics, mathematics and data science. Probabilistic treatment of breadth-first and depth-first non-exhaustive search algorithms in graphs. Presenting cutting-edge theory and methods based on latest research. Pathfinding for future research on sampling from real graphs. Graph Sampling can primarily be used as a resource for researchers working with sampling or graph problems, and as the basis of an advanced course for post-graduate students in statistics, mathematics and data science.
Author: Yadolah Dodge Publisher: Oxford University Press, USA ISBN: 9780199206131 Category : Business & Economics Languages : en Pages : 516
Book Description
This is the new-in-paperback edition of The Oxford Dictionary of Statistical Terms, the much-awaited sixth edition of the acclaimed standard reference work in statistics, published on behalf of the International Statistical Institute. The first edition, known as the Dictionary of Statistical Terms, was edited in 1957 by the late Sir Maurice Kendall and the late Dr W.R. Buckland. As one of the first dictionaries of statistics it set high standards for the subject, and became a well-respected reference. This edition has been carefully updated and extended to include the most recent terminology and techniques in statistics. Significant revision and expansion from an international editorial board of senior statisticians has resulted in a comprehenisive reference text which includes 30% more material than previous editions. Ideal for all who use statistics in the workplace and in research including all scientists and social scientists, especially in law, politics, finance, business, and history, it is an indispensable reference.
Author: Paul Rosenbaum Publisher: Harvard University Press ISBN: 067497557X Category : Mathematics Languages : en Pages : 395
Book Description
A daily glass of wine prolongs life—yet alcohol can cause life-threatening cancer. Some say raising the minimum wage will decrease inequality while others say it increases unemployment. Scientists once confidently claimed that hormone replacement therapy reduced the risk of heart disease but now they equally confidently claim it raises that risk. What should we make of this endless barrage of conflicting claims? Observation and Experiment is an introduction to causal inference by one of the field’s leading scholars. An award-winning professor at Wharton, Paul Rosenbaum explains key concepts and methods through lively examples that make abstract principles accessible. He draws his examples from clinical medicine, economics, public health, epidemiology, clinical psychology, and psychiatry to explain how randomized control trials are conceived and designed, how they differ from observational studies, and what techniques are available to mitigate their bias. “Carefully and precisely written...reflecting superb statistical understanding, all communicated with the skill of a master teacher.” —Stephen M. Stigler, author of The Seven Pillars of Statistical Wisdom “An excellent introduction...Well-written and thoughtful...from one of causal inference’s noted experts.” —Journal of the American Statistical Association “Rosenbaum is a gifted expositor...an outstanding introduction to the topic for anyone who is interested in understanding the basic ideas and approaches to causal inference.” —Psychometrika “A very valuable contribution...Highly recommended.” —International Statistical Review
Author: D.A. Sprott Publisher: Springer Science & Business Media ISBN: 0387950192 Category : Mathematics Languages : en Pages : 254
Book Description
A treatment of the problems of inference associated with experiments in science, with the emphasis on techniques for dividing the sample information into various parts, such that the diverse problems of inference that arise from repeatable experiments may be addressed. A particularly valuable feature is the large number of practical examples, many of which use data taken from experiments published in various scientific journals. This book evolved from the authors own courses on statistical inference, and assumes an introductory course in probability, including the calculation and manipulation of probability functions and density functions, transformation of variables and the use of Jacobians. While this is a suitable text book for advanced undergraduate, Masters, and Ph.D. statistics students, it may also be used as a reference book.
Author: Ivo D. Dinov Publisher: Springer Nature ISBN: 3031174836 Category : Computers Languages : en Pages : 940
Book Description
This textbook integrates important mathematical foundations, efficient computational algorithms, applied statistical inference techniques, and cutting-edge machine learning approaches to address a wide range of crucial biomedical informatics, health analytics applications, and decision science challenges. Each concept in the book includes a rigorous symbolic formulation coupled with computational algorithms and complete end-to-end pipeline protocols implemented as functional R electronic markdown notebooks. These workflows support active learning and demonstrate comprehensive data manipulations, interactive visualizations, and sophisticated analytics. The content includes open problems, state-of-the-art scientific knowledge, ethical integration of heterogeneous scientific tools, and procedures for systematic validation and dissemination of reproducible research findings. Complementary to the enormous challenges related to handling, interrogating, and understanding massive amounts of complex structured and unstructured data, there are unique opportunities that come with access to a wealth of feature-rich, high-dimensional, and time-varying information. The topics covered in Data Science and Predictive Analytics address specific knowledge gaps, resolve educational barriers, and mitigate workforce information-readiness and data science deficiencies. Specifically, it provides a transdisciplinary curriculum integrating core mathematical principles, modern computational methods, advanced data science techniques, model-based machine learning, model-free artificial intelligence, and innovative biomedical applications. The book’s fourteen chapters start with an introduction and progressively build foundational skills from visualization to linear modeling, dimensionality reduction, supervised classification, black-box machine learning techniques, qualitative learning methods, unsupervised clustering, model performance assessment, feature selection strategies, longitudinal data analytics, optimization, neural networks, and deep learning. The second edition of the book includes additional learning-based strategies utilizing generative adversarial networks, transfer learning, and synthetic data generation, as well as eight complementary electronic appendices. This textbook is suitable for formal didactic instructor-guided course education, as well as for individual or team-supported self-learning. The material is presented at the upper-division and graduate-level college courses and covers applied and interdisciplinary mathematics, contemporary learning-based data science techniques, computational algorithm development, optimization theory, statistical computing, and biomedical sciences. The analytical techniques and predictive scientific methods described in the book may be useful to a wide range of readers, formal and informal learners, college instructors, researchers, and engineers throughout the academy, industry, government, regulatory, funding, and policy agencies. The supporting book website provides many examples, datasets, functional scripts, complete electronic notebooks, extensive appendices, and additional materials.
Author: J. K. Ghosh Publisher: Wiley-Interscience ISBN: Category : Biography & Autobiography Languages : en Pages : 316
Book Description
Selected Papers of C.R. Rao (in five volumes) The statistical career of C.R. Rao, the most famous statistician from India, a Fellow of the Royal Society and founder of the Research-and Training School of the Indian Statistical Institute spans nearly half a century. The Selected papers reflect the trends of research in statistical methodology and data analysis during the last fifty years. These volumes constitute a rich and valuable source of reference for all statisticians who are interested in research, education or practice of statistical methods in any area of application.
Author: Deborah Nolan Publisher: Oxford University Press ISBN: 0192607502 Category : Science Languages : en Pages : 400
Book Description
Communication is a critical yet often overlooked part of data science. Communicating with Data aims to help students and researchers write about their insights in a way that is both compelling and faithful to the data. General advice on science writing is also provided, including how to distill findings into a story and organize and revise the story, and how to write clearly, concisely, and precisely. This is an excellent resource for students who want to learn how to write about scientific findings, and for instructors who are teaching a science course in communication or a course with a writing component. Communicating with Data consists of five parts. Part I helps the novice learn to write by reading the work of others. Part II delves into the specifics of how to describe data at a level appropriate for publication, create informative and effective visualizations, and communicate an analysis pipeline through well-written, reproducible code. Part III demonstrates how to reduce a data analysis to a compelling story and organize and write the first draft of a technical paper. Part IV addresses revision; this includes advice on writing about statistical findings in a clear and accurate way, general writing advice, and strategies for proof reading and revising. Part V offers advice about communication strategies beyond the page, which include giving talks, building a professional network, and participating in online communities. This book also provides 22 portfolio prompts that extend the guidance and examples in the earlier parts of the book and help writers build their portfolio of data communication.
Author: Iddo Gal Publisher: ISBN: 9784274901584 Category : Mathematics Languages : en Pages : 300
Book Description
This book discusses conceptual and pragmatic issues in the assessment of statistical knowledge and reasoning skills among students at the college and precollege levels, and the use of assessments to improve instruction. It is designed primarily for academic audiences involved in teaching statistics and mathematics, and in teacher education and training. The book is divided in four sections: (I) Assessment goals and frameworks, (2) Assessing conceptual understanding of statistical ideas, (3) Innovative models for classroom assessments, and (4) Assessing understanding of probability.
Author: C.C. Heyde
Author: C.C. Heyde
Author: United States. Bureau of the Census
Author: Li-Chun Zhang
Author: Yadolah Dodge
Author: Paul Rosenbaum
Author: D.A. Sprott
Author: Ivo D. Dinov
Author: J. K. Ghosh
Author: Deborah Nolan
Author: Iddo Gal