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Books in Statistics

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111-120 of 145 results in All results

Data Mining and Data Visualization

  • 1st Edition
  • Volume 24
  • May 2, 2005
  • C.R. Rao
  • English
  • Hardback
    9 7 8 - 0 - 4 4 4 - 5 1 1 4 1 - 6
  • eBook
    9 7 8 - 0 - 0 8 - 0 4 5 9 4 0 - 0
Data Mining and Data Visualization focuses on dealing with large-scale data, a field commonly referred to as data mining. The book is divided into three sections. The first deals with an introduction to statistical aspects of data mining and machine learning and includes applications to text analysis, computer intrusion detection, and hiding of information in digital files. The second section focuses on a variety of statistical methodologies that have proven to be effective in data mining applications. These include clustering, classification, multivariate density estimation, tree-based methods, pattern recognition, outlier detection, genetic algorithms, and dimensionality reduction. The third section focuses on data visualization and covers issues of visualization of high-dimensional data, novel graphical techniques with a focus on human factors, interactive graphics, and data visualization using virtual reality. This book represents a thorough cross section of internationally renowned thinkers who are inventing methods for dealing with a new data paradigm.

Advances in Survival Analysis

  • 1st Edition
  • Volume 23
  • January 30, 2004
  • Narayanaswamy Balakrishnan + 1 more
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 4 9 5 1 1 - 8
Handbook of Statistics: Advances in Survival Analysis covers all important topics in the area of Survival Analysis. Each topic has been covered by one or more chapters written by internationally renowned experts. Each chapter provides a comprehensive and up-to-date review of the topic. Several new illustrative examples have been used to demonstrate the methodologies developed. The book also includes an exhaustive list of important references in the area of Survival Analysis.

Recent Advances and Trends in Nonparametric Statistics

  • 1st Edition
  • October 31, 2003
  • M.G. Akritas + 1 more
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 5 4 0 3 7 - 5
The advent of high-speed, affordable computers in the last two decades has given a new boost to the nonparametric way of thinking. Classical nonparametric procedures, such as function smoothing, suddenly lost their abstract flavour as they became practically implementable. In addition, many previously unthinkable possibilities became mainstream; prime examples include the bootstrap and resampling methods, wavelets and nonlinear smoothers, graphical methods, data mining, bioinformatics, as well as the more recent algorithmic approaches such as bagging and boosting. This volume is a collection of short articles - most of which having a review component - describing the state-of-the art of Nonparametric Statistics at the beginning of a new millennium.Key features:• algorithic approaches• wavelets and nonlinear smoothers• graphical methods and data mining• biostatistics and bioinformatics• bagging and boosting• support vector machines• resampling methods

Introductory Statistics for Engineering Experimentation

  • 1st Edition
  • August 14, 2003
  • Peter R. Nelson + 2 more
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 4 9 1 6 5 - 3
The Accreditation Board for Engineering and Technology (ABET) introduced a criterion starting with their 1992-1993 site visits that "Students must demonstrate a knowledge of the application of statistics to engineering problems." Since most engineering curricula are filled with requirements in their own discipline, they generally do not have time for a traditional two semesters of probability and statistics. Attempts to condense that material into a single semester often results in so much time being spent on probability that the statistics useful for designing and analyzing engineering/scientific experiments is never covered. In developing a one-semester course whose purpose was to introduce engineering/scientific students to the most useful statistical methods, this book was created to satisfy those needs.

Statistics and Probability for Engineering Applications

  • 1st Edition
  • May 14, 2003
  • William DeCoursey
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 4 8 9 7 5 - 9
Statistics and Probability for Engineering Applications provides a complete discussion of all the major topics typically covered in a college engineering statistics course. This textbook minimizes the derivations and mathematical theory, focusing instead on the information and techniques most needed and used in engineering applications. It is filled with practical techniques directly applicable on the job. Written by an experienced industry engineer and statistics professor, this book makes learning statistical methods easier for today's student. This book can be read sequentially like a normal textbook, but it is designed to be used as a handbook, pointing the reader to the topics and sections pertinent to a particular type of statistical problem. Each new concept is clearly and briefly described, whenever possible by relating it to previous topics. Then the student is given carefully chosen examples to deepen understanding of the basic ideas and how they are applied in engineering. The examples and case studies are taken from real-world engineering problems and use real data. A number of practice problems are provided for each section, with answers in the back for selected problems. This book will appeal to engineers in the entire engineering spectrum (electronics/electrical, mechanical, chemical, and civil engineering); engineering students and students taking computer science/computer engineering graduate courses; scientists needing to use applied statistical methods; and engineering technicians and technologists.

Statistical Methods

  • 2nd Edition
  • January 7, 2003
  • Rudolf J. Freund + 1 more
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 4 9 8 2 2 - 5
This broad text provides a complete overview of most standard statistical methods, including multiple regression, analysis of variance, experimental design, and sampling techniques. Assuming a background of only two years of high school algebra, this book teaches intelligent data analysis and covers the principles of good data collection.

Applying Contemporary Statistical Techniques

  • 1st Edition
  • December 23, 2002
  • Rand R. Wilcox
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 5 2 7 5 1 - 2
Applying Contemporary Statistical Techniques explains why traditional statistical methods are often inadequate or outdated when applied to modern problems. Wilcox demonstrates how new and more powerful techniques address these problems far more effectively, making these modern robust methods understandable, practical, and easily accessible.

An Introduction to Probability and Statistical Inference

  • 1st Edition
  • December 19, 2002
  • George G. Roussas
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 4 9 5 7 5 - 0
Roussas introduces readers with no prior knowledge in probability or statistics, to a thinking process to guide them toward the best solution to a posed question or situation. An Introduction to Probability and Statistical Inference provides a plethora of examples for each topic discussed, giving the reader more experience in applying statistical methods to different situations. "The text is wonderfully written and has the mostcomprehensive range of exercise problems that I have ever seen." — Tapas K. Das, University of South Florida"The exposition is great; a mixture between conversational tones and formal mathematics; the appropriate combination for a math text at [this] level. In my examination I could find no instance where I could improve the book." — H. Pat Goeters, Auburn, University, Alabama

Handbook of Applied Multivariate Statistics and Mathematical Modeling

  • 1st Edition
  • April 26, 2000
  • Howard E.A. Tinsley + 1 more
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 5 3 3 5 6 - 8
Multivariate statistics and mathematical models provide flexible and powerful tools essential in most disciplines. Nevertheless, many practicing researchers lack an adequate knowledge of these techniques, or did once know the techniques, but have not been able to keep abreast of new developments. The Handbook of Applied Multivariate Statistics and Mathematical Modeling explains the appropriate uses of multivariate procedures and mathematical modeling techniques, and prescribe practices that enable applied researchers to use these procedures effectively without needing to concern themselves with the mathematical basis. The Handbook emphasizes using models and statistics as tools. The objective of the book is to inform readers about which tool to use to accomplish which task. Each chapter begins with a discussion of what kinds of questions a particular technique can and cannot answer. As multivariate statistics and modeling techniques are useful across disciplines, these examples include issues of concern in biological and social sciences as well as the humanities.

Kohonen Maps

  • 1st Edition
  • July 2, 1999
  • E. Oja + 1 more
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 5 3 5 2 9 - 6
The Self-Organizing Map, or Kohonen Map, is one of the most widely used neural network algorithms, with thousands of applications covered in the literature. It was one of the strong underlying factors in the popularity of neural networks starting in the early 80's. Currently this method has been included in a large number of commercial and public domain software packages. In this book, top experts on the SOM method take a look at the state of the art and the future of this computing paradigm.The 30 chapters of this book cover the current status of SOM theory, such as connections of SOM to clustering, classification, probabilistic models, and energy functions. Many applications of the SOM are given, with data mining and exploratory data analysis the central topic, applied to large databases of financial data, medical data, free-form text documents, digital images, speech, and process measurements. Biological models related to the SOM are also discussed.