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Books in Statistics and probability

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61-70 of 233 results in All results

Computational and Statistical Methods for Analysing Big Data with Applications

  • 1st Edition
  • November 20, 2015
  • Shen Liu + 3 more
  • English
  • Hardback
    9 7 8 - 0 - 1 2 - 8 0 3 7 3 2 - 4
  • eBook
    9 7 8 - 0 - 0 8 - 1 0 0 6 5 1 - 1
Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to explore, model and draw inferences from big data. This book aims to introduce suitable approaches for such endeavours, providing applications and case studies for the purpose of demonstration. Computational and Statistical Methods for Analysing Big Data with Applications starts with an overview of the era of big data. It then goes onto explain the computational and statistical methods which have been commonly applied in the big data revolution. For each of these methods, an example is provided as a guide to its application. Five case studies are presented next, focusing on computer vision with massive training data, spatial data analysis, advanced experimental design methods for big data, big data in clinical medicine, and analysing data collected from mobile devices, respectively. The book concludes with some final thoughts and suggested areas for future research in big data.

A New Concept for Tuning Design Weights in Survey Sampling

  • 1st Edition
  • November 11, 2015
  • Sarjinder Singh + 4 more
  • English
  • Hardback
    9 7 8 - 0 - 0 8 - 1 0 0 5 9 4 - 1
  • eBook
    9 7 8 - 0 - 0 8 - 1 0 0 5 9 5 - 8
A New Concept for Tuning Design Weights in Survey Sampling: Jackknifing in Theory and Practice introduces the new concept of tuning design weights in survey sampling by presenting three concepts: calibration, jackknifing, and imputing where needed. This new methodology allows survey statisticians to develop statistical software for analyzing data in a more precisely and friendly way than with existing techniques.

Exact Statistical Inference for Categorical Data

  • 1st Edition
  • October 30, 2015
  • Guogen Shan
  • English
  • Paperback
    9 7 8 - 0 - 0 8 - 1 0 0 6 8 1 - 8
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 3 9 4 8 - 9
Exact Statistical Inference for Categorical Data discusses the way asymptotic approaches have been often used in practice to make statistical inference. This book introduces both conditional and unconditional exact approaches for the data in 2 by 2, or 2 by k contingency tables, and is an ideal reference for users who are interested in having the convenience of applying asymptotic approaches, with less computational time. In addition to the existing conditional exact inference, some efficient, unconditional exact approaches could be used in data analysis to improve the performance of the testing procedure.

Hidden Semi-Markov Models

  • 1st Edition
  • October 22, 2015
  • Shun-Zheng Yu
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 2 7 6 7 - 7
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 2 7 7 1 - 4
Hidden semi-Markov models (HSMMs) are among the most important models in the area of artificial intelligence / machine learning. Since the first HSMM was introduced in 1980 for machine recognition of speech, three other HSMMs have been proposed, with various definitions of duration and observation distributions. Those models have different expressions, algorithms, computational complexities, and applicable areas, without explicitly interchangeable forms. Hidden Semi-Markov Models: Theory, Algorithms and Applications provides a unified and foundational approach to HSMMs, including various HSMMs (such as the explicit duration, variable transition, and residential time of HSMMs), inference and estimation algorithms, implementation methods and application instances. Learn new developments and state-of-the-art emerging topics as they relate to HSMMs, presented with examples drawn from medicine, engineering and computer science.

The Birnbaum-Saunders Distribution

  • 1st Edition
  • October 22, 2015
  • Victor Leiva
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 3 7 6 9 - 0
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 3 8 2 7 - 7
The Birnbaum-Saunders Distribution presents the statistical theory, methodology, and applications of the Birnbaum-Saunders distribution, a very flexible distribution for modeling different types of data (mainly lifetime data). The book describes the most recent theoretical developments of this model, including properties, transformations and related distributions, lifetime analysis, and shape analysis. It discusses methods of inference based on uncensored and censored data, goodness-of-fit tests, and random number generation algorithms for the Birnbaum-Saunders distribution, also presenting existing and future applications.

Introduction to Statistical Machine Learning

  • 1st Edition
  • September 25, 2015
  • Masashi Sugiyama
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 2 1 2 1 - 7
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 2 3 5 0 - 1
Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice. The accompanying MATLAB/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks.

An Introduction to Stochastic Orders

  • 1st Edition
  • September 21, 2015
  • Felix Belzunce + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 3 7 6 8 - 3
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 3 8 2 6 - 0
An Introduction to Stochastic Orders discusses this powerful tool that can be used in comparing probabilistic models in different areas such as reliability, survival analysis, risks, finance, and economics. The book provides a general background on this topic for students and researchers who want to use it as a tool for their research. In addition, users will find detailed proofs of the main results and applications to several probabilistic models of interest in several fields, and discussions of fundamental properties of several stochastic orders, in the univariate and multivariate cases, along with applications to probabilistic models.

Biostatistics for Medical and Biomedical Practitioners

  • 1st Edition
  • September 3, 2015
  • Julien I. E. Hoffman
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 2 3 8 7 - 7
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 2 6 0 7 - 6
Biostatistics for Practitioners: An Interpretative Guide for Medicine and Biology deals with several aspects of statistics that are indispensable for researchers and students across the biomedical sciences. The book features a step-by-step approach, focusing on standard statistical tests, as well as discussions of the most common errors. The book is based on the author’s 40+ years of teaching statistics to medical fellows and biomedical researchers across a wide range of fields.

Stochastic Calculus for Quantitative Finance

  • 1st Edition
  • August 19, 2015
  • Alexander A Gushchin
  • English
  • Hardback
    9 7 8 - 1 - 7 8 5 4 8 - 0 3 4 - 8
  • eBook
    9 7 8 - 0 - 0 8 - 1 0 0 4 7 6 - 0
In 1994 and 1998 F. Delbaen and W. Schachermayer published two breakthrough papers where they proved continuous-time versions of the Fundamental Theorem of Asset Pricing. This is one of the most remarkable achievements in modern Mathematical Finance which led to intensive investigations in many applications of the arbitrage theory on a mathematically rigorous basis of stochastic calculus.Mathematical Basis for Finance: Stochastic Calculus for Finance provides detailed knowledge of all necessary attributes in stochastic calculus that are required for applications of the theory of stochastic integration in Mathematical Finance, in particular, the arbitrage theory. The exposition follows the traditions of the Strasbourg school. This book covers the general theory of stochastic processes, local martingales and processes of bounded variation, the theory of stochastic integration, definition and properties of the stochastic exponential; a part of the theory of Lévy processes. Finally, the reader gets acquainted with some facts concerning stochastic differential equations.

Related subjects

Probability theory and stochastic processes

Statistics