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Books in Statistics and probability

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111-120 of 233 results in All results

Laboratory Statistics

  • 1st Edition
  • September 6, 2013
  • Anders Kallner
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 4 1 6 9 7 1 - 5
  • eBook
    9 7 8 - 0 - 1 2 - 4 1 6 9 7 3 - 9
Laboratory Statistics: Handbook of Formulas and Terms presents common strategies for comparing and evaluating numerical laboratory data. In particular, the text deals with the type of data and problems that laboratory scientists and students in analytical chemistry, clinical chemistry, epidemiology, and clinical research face on a daily basis. This book takes the mystery out of statistics and provides simple, hands-on instructions in the format of everyday formulas. As far as possible, spreadsheet shortcuts and functions are included, along with many simple worked examples. This book is a must-have guide to applied statistics in the lab that will result in improved experimental design and analysis.

Machine Learning: Theory and Applications

  • 1st Edition
  • Volume 31
  • May 16, 2013
  • C.R. Rao + 1 more
  • English
  • Hardback
    9 7 8 - 0 - 4 4 4 - 5 3 8 5 9 - 8
  • eBook
    9 7 8 - 0 - 4 4 4 - 5 3 8 6 6 - 6
Statistical learning and analysis techniques have become extremely important today, given the tremendous growth in the size of heterogeneous data collections and the ability to process it even from physically distant locations. Recent advances made in the field of machine learning provide a strong framework for robust learning from the diverse corpora and continue to impact a variety of research problems across multiple scientific disciplines. The aim of this handbook is to familiarize beginners as well as experts with some of the recent techniques in this field.The Handbook is divided in two sections: Theory and Applications, covering machine learning, data analytics, biometrics, document recognition and security.

Chi-Squared Goodness of Fit Tests with Applications

  • 1st Edition
  • January 24, 2013
  • Narayanaswamy Balakrishnan + 2 more
  • English
  • Hardback
    9 7 8 - 0 - 1 2 - 3 9 7 1 9 4 - 4
  • eBook
    9 7 8 - 0 - 1 2 - 3 9 7 7 8 3 - 0
Chi-Squared Goodness of Fit Tests with Applications provides a thorough and complete context for the theoretical basis and implementation of Pearson’s monumental contribution and its wide applicability for chi-squared goodness of fit tests. The book is ideal for researchers and scientists conducting statistical analysis in processing of experimental data as well as to students and practitioners with a good mathematical background who use statistical methods. The historical context, especially Chapter 7, provides great insight into importance of this subject with an authoritative author team. This reference includes the most recent application developments in using these methods and models.

Analysis and Probability

  • 1st Edition
  • January 11, 2013
  • Aurel Spataru
  • English
  • Hardback
    9 7 8 - 0 - 1 2 - 4 0 1 6 6 5 - 1
  • eBook
    9 7 8 - 0 - 1 2 - 4 0 1 7 2 7 - 6
Probability theory is a rapidly expanding field and is used in many areas of science and technology. Beginning from a basis of abstract analysis, this mathematics book develops the knowledge needed for advanced students to develop a complex understanding of probability. The first part of the book systematically presents concepts and results from analysis before embarking on the study of probability theory. The initial section will also be useful for those interested in topology, measure theory, real analysis and functional analysis. The second part of the book presents the concepts, methodology and fundamental results of probability theory. Exercises are included throughout the text, not just at the end, to teach each concept fully as it is explained, including presentations of interesting extensions of the theory. The complete and detailed nature of the book makes it ideal as a reference book or for self-study in probability and related fields.

R and Data Mining

  • 1st Edition
  • December 11, 2012
  • Yanchang Zhao
  • English
  • Hardback
    9 7 8 - 0 - 1 2 - 3 9 6 9 6 3 - 7
  • eBook
    9 7 8 - 0 - 1 2 - 3 9 7 2 7 1 - 2
R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more.Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis.

Statistics in Spectroscopy

  • 1st Edition
  • December 2, 2012
  • Howard Mark + 1 more
  • English
  • eBook
    9 7 8 - 0 - 3 2 3 - 1 3 7 9 1 - 1
This tutorial offers a basic hands-on approach to statistical analysis for chemists and spectroscopists. Without involving complicated mathematics, this book is designed to provide the reader with the basic principles underlying the use of common mathematical and statistical tools. Particular emphasis has been given to problem-solving applications and the proper use and interpretation of spectroscopic data. With exercises throughout, this book is also suitable for use as a textbook in analytical chemistry, instrumental analysis, and statistics in chemistry courses.

Stochastic Wave Propagation

  • 1st Edition
  • December 2, 2012
  • K. Sobczyk
  • English
  • eBook
    9 7 8 - 0 - 4 4 4 - 5 9 8 0 4 - 2
This is a concise, unified exposition of the existing methods of analysis of linear stochastic waves with particular reference to the most recent results. Both scalar and vector waves are considered. Principal attention is concentrated on wave propagation in stochastic media and wave scattering at stochastic surfaces. However, discussion extends also to various mathematical aspects of stochastic wave equations and problems of modelling stochastic media.

Foundations of Estimation Theory

  • 1st Edition
  • November 13, 2012
  • L. Kubacek
  • English
  • eBook
    9 7 8 - 0 - 4 4 4 - 5 9 8 0 8 - 0
The application of estimation theory renders the processing of experimental results both rational and effective, and thus helps not only to make our knowledge more precise but to determine the measure of its reliability. As a consequence, estimation theory is indispensable in the analysis of the measuring processes and of experiments in general.The knowledge necessary for studying this book encompasses the disciplines of probability and mathematical statistics as studied in the third or fourth year at university. For readers interested in applications, comparatively detailed chapters on linear and quadratic estimations, and normality of observation vectors have been included. Chapter 2 includes selected items of information from algebra, functional analysis and the theory of probability, intended to facilitate the reading of the text proper and to save the reader looking up individual theorems in various textbooks and papers; it is mainly devoted to the reproducing kernel Hilbert spaces, helpful in solving many estimation problems. The text proper of the book begins with Chapter 3. This is divided into two parts: the first deals with sufficient statistics, complete sufficient statistics, minimal sufficient statistics and relations between them; the second contains the mostimportant inequalities of estimation theory for scalar and vector valued parameters and presents properties of the exponential family of distributions.The fourth chapter is an introduction to asymptotic methods of estimation. The method of statistical moments and the maximum-likelihood method are investigated. The sufficient conditions for asymptotical normality of the estimators are given for both methods. The linear and quadratic methods of estimation are dealt with in the fifth chapter. The method of least squares estimation is treated. Five basic regular versions of the regression model and the unified linear model of estimation are described. Unbiased estimators for unit dispersion (factor of the covariance matrix) are given for all mentioned cases. The equivalence of the least-squares method to the method of generalized minimum norm inversion of the design matrix of the regression model is studied in detail. The problem of estimating the covariance components in the mixed model is mentioned as well. Statistical properties of linear and quadratic estimators developed in the fifth chapter in the case of normally distributed errors of measurement are given in Chapter 6. Further, the application of tensor products of Hilbert spaces generated by the covariance matrix of random error vector of observations is demonstrated. Chapter 7 reviews some further important methods of estimation theory. In the first part Wald's method of decision functions is applied to the construction of estimators. The method of contracted estimators and the method of Hoerl and Kennard are presented in the second part. The basic ideas of robustness and Bahadur's approach to estimation theory are presented in the third and fourth parts of this last chapter.

Sample Size Methodology

  • 1st Edition
  • November 12, 2012
  • M. M. Desu
  • English
  • eBook
    9 7 8 - 0 - 3 2 3 - 1 3 9 5 6 - 4
One of the most important problems in designing an experiment or a survey is sample size determination and this book presents the currently available methodology. It includes both random sampling from standard probability distributions and from finite populations. Also discussed is sample size determination for estimating parameters in a Bayesian setting by considering the posterior distribution of the parameter and specifying the necessary requirements. The determination of the sample size is considered for ranking and selection problems as well as for the design of clinical trials. Appropriate techniques for attacking the general question of sample size determination in problems of estimation, tests of hypotheses, selection, and clinical trial design are all presented, and will help the reader in formulating an appropriate problem of sample size and in obtaining the solution. The book can be used as a text in a senior-level or a graduate course on sample size methodology.

The Single Server Queue

  • 2nd Edition
  • Volume 8
  • November 11, 2012
  • J.W. Cohen
  • English
  • eBook
    9 7 8 - 0 - 4 4 4 - 5 9 6 2 4 - 6
This classic work, now available in paperback, concentrates on the basic models of queueing theory. It has a dual aim: to describe relevant mathematical techniques and to analyse the single server queue and its most important variants.

Related subjects

Probability theory and stochastic processes

Statistics