Numerical Methods of Statistics

Numerical Methods of Statistics
Author: John F. Monahan
Publisher: Cambridge University Press
Total Pages: 465
Release: 2011-04-18
Genre: Computers
ISBN: 1139498002

Download Numerical Methods of Statistics Book in PDF, Epub and Kindle

This book explains how computer software is designed to perform the tasks required for sophisticated statistical analysis. For statisticians, it examines the nitty-gritty computational problems behind statistical methods. For mathematicians and computer scientists, it looks at the application of mathematical tools to statistical problems. The first half of the book offers a basic background in numerical analysis that emphasizes issues important to statisticians. The next several chapters cover a broad array of statistical tools, such as maximum likelihood and nonlinear regression. The author also treats the application of numerical tools; numerical integration and random number generation are explained in a unified manner reflecting complementary views of Monte Carlo methods. Each chapter contains exercises that range from simple questions to research problems. Most of the examples are accompanied by demonstration and source code available from the author's website. New in this second edition are demonstrations coded in R, as well as new sections on linear programming and the Nelder–Mead search algorithm.

Numerical Analysis for Statisticians

Numerical Analysis for Statisticians
Author: Kenneth Lange
Publisher: Springer Science & Business Media
Total Pages: 606
Release: 2010-05-17
Genre: Business & Economics
ISBN: 1441959459

Download Numerical Analysis for Statisticians Book in PDF, Epub and Kindle

Numerical analysis is the study of computation and its accuracy, stability and often its implementation on a computer. This book focuses on the principles of numerical analysis and is intended to equip those readers who use statistics to craft their own software and to understand the advantages and disadvantages of different numerical methods.

Numerical Methods of Statistics

Numerical Methods of Statistics
Author: John F. Monahan
Publisher: Cambridge University Press
Total Pages: 446
Release: 2001-02-05
Genre: Computers
ISBN: 9780521791687

Download Numerical Methods of Statistics Book in PDF, Epub and Kindle

This 2001 book provides a basic background in numerical analysis and its applications in statistics.

Computational Methods for Numerical Analysis with R

Computational Methods for Numerical Analysis with R
Author: James P Howard, II
Publisher: CRC Press
Total Pages: 257
Release: 2017-07-12
Genre: Mathematics
ISBN: 1498723640

Download Computational Methods for Numerical Analysis with R Book in PDF, Epub and Kindle

Computational Methods for Numerical Analysis with R is an overview of traditional numerical analysis topics presented using R. This guide shows how common functions from linear algebra, interpolation, numerical integration, optimization, and differential equations can be implemented in pure R code. Every algorithm described is given with a complete function implementation in R, along with examples to demonstrate the function and its use. Computational Methods for Numerical Analysis with R is intended for those who already know R, but are interested in learning more about how the underlying algorithms work. As such, it is suitable for statisticians, economists, and engineers, and others with a computational and numerical background.

Assessment of Treatment Plant Performance and Water Quality Data: A Guide for Students, Researchers and Practitioners

Assessment of Treatment Plant Performance and Water Quality Data: A Guide for Students, Researchers and Practitioners
Author: Marcos von Sperling
Publisher: IWA Publishing
Total Pages: 668
Release: 2020-01-15
Genre: Science
ISBN: 1780409311

Download Assessment of Treatment Plant Performance and Water Quality Data: A Guide for Students, Researchers and Practitioners Book in PDF, Epub and Kindle

This book presents the basic principles for evaluating water quality and treatment plant performance in a clear, innovative and didactic way, using a combined approach that involves the interpretation of monitoring data associated with (i) the basic processes that take place in water bodies and in water and wastewater treatment plants and (ii) data management and statistical calculations to allow a deep interpretation of the data. This book is problem-oriented and works from practice to theory, covering most of the information you will need, such as (a) obtaining flow data and working with the concept of loading, (b) organizing sampling programmes and measurements, (c) connecting laboratory analysis to data management, (e) using numerical and graphical methods for describing monitoring data (descriptive statistics), (f) understanding and reporting removal efficiencies, (g) recognizing symmetry and asymmetry in monitoring data (normal and log-normal distributions), (h) evaluating compliance with targets and regulatory standards for effluents and water bodies, (i) making comparisons with the monitoring data (tests of hypothesis), (j) understanding the relationship between monitoring variables (correlation and regression analysis), (k) making water and mass balances, (l) understanding the different loading rates applied to treatment units, (m) learning the principles of reaction kinetics and reactor hydraulics and (n) performing calibration and verification of models. The major concepts are illustrated by 92 fully worked-out examples, which are supported by 75 freely-downloadable Excel spreadsheets. Each chapter concludes with a checklist for your report. If you are a student, researcher or practitioner planning to use or already using treatment plant and water quality monitoring data, then this book is for you! 75 Excel spreadsheets are available to download.

Numerical Methods for Nonlinear Estimating Equations

Numerical Methods for Nonlinear Estimating Equations
Author: Christopher G. Small
Publisher: Oxford University Press
Total Pages: 330
Release: 2003
Genre: Mathematics
ISBN: 9780198506881

Download Numerical Methods for Nonlinear Estimating Equations Book in PDF, Epub and Kindle

Non linearity arises in statistical inference in various ways, with varying degrees of severity, as an obstacle to statistical analysis. More entrenched forms of nonlinearity often require intensive numerical methods to construct estimators, and the use of root search algorithms, or one-step estimators, is a standard method of solution. This book provides a comprehensive study of nonlinear estimating equations and artificial likelihood's for statistical inference. It provides extensive coverage and comparison of hill climbing algorithms, which when started at points of nonconcavity often have very poor convergence properties, and for additional flexibility proposes a number of modification to the standard methods for solving these algorithms. The book also extends beyond simple root search algorithms to include a discussion of the testing of roots for consistency, and the modification of available estimating functions to provide greater stability in inference. A variety of examples from practical applications are included to illustrate the problems and possibilities thus making this text ideal for the research statistician and graduate student.

Advances in Numerical Analysis Emphasizing Interval Data

Advances in Numerical Analysis Emphasizing Interval Data
Author: Tofigh Allahviranloo
Publisher: CRC Press
Total Pages: 135
Release: 2022-02-18
Genre: Technology & Engineering
ISBN: 1000540316

Download Advances in Numerical Analysis Emphasizing Interval Data Book in PDF, Epub and Kindle

Numerical analysis forms a cornerstone of numeric computing and optimization, in particular recently, interval numerical computations play an important role in these topics. The interest of researchers in computations involving uncertain data, namely interval data opens new avenues in coping with real-world problems and deliver innovative and efficient solutions. This book provides the basic theoretical foundations of numerical methods, discusses key technique classes, explains improvements and improvements, and provides insights into recent developments and challenges. The theoretical parts of numerical methods, including the concept of interval approximation theory, are introduced and explained in detail. In general, the key features of the book include an up-to-date and focused treatise on error analysis in calculations, in particular the comprehensive and systematic treatment of error propagation mechanisms, considerations on the quality of data involved in numerical calculations, and a thorough discussion of interval approximation theory. Moreover, this book focuses on approximation theory and its development from the perspective of linear algebra, and new and regular representations of numerical integration and their solutions are enhanced by error analysis as well. The book is unique in the sense that its content and organization will cater to several audiences, in particular graduate students, researchers, and practitioners.

A Handbook of Numerical and Statistical Techniques

A Handbook of Numerical and Statistical Techniques
Author: J. H. Pollard
Publisher: CUP Archive
Total Pages: 372
Release: 1977
Genre: Mathematics
ISBN: 9780521297509

Download A Handbook of Numerical and Statistical Techniques Book in PDF, Epub and Kindle

This handbook is designed for experimental scientists, particularly those in the life sciences. It is for the non-specialist, and although it assumes only a little knowledge of statistics and mathematics, those with a deeper understanding will also find it useful. The book is directed at the scientist who wishes to solve his numerical and statistical problems on a programmable calculator, mini-computer or interactive terminal. The volume is also useful for the user of full-scale computer systems in that it describes how the large computer solves numerical and statistical problems. The book is divided into three parts. Part I deals with numerical techniques and Part II with statistical techniques. Part III is devoted to the method of least squares which can be regarded as both a statistical and numerical method. The handbook shows clearly how each calculation is performed. Each technique is illustrated by at least one example and there are worked examples and exercises throughout the volume.

Elements of Statistical Computing

Elements of Statistical Computing
Author: R.A. Thisted
Publisher: Routledge
Total Pages: 448
Release: 2017-10-19
Genre: Mathematics
ISBN: 1351452754

Download Elements of Statistical Computing Book in PDF, Epub and Kindle

Statistics and computing share many close relationships. Computing now permeates every aspect of statistics, from pure description to the development of statistical theory. At the same time, the computational methods used in statistical work span much of computer science. Elements of Statistical Computing covers the broad usage of computing in statistics. It provides a comprehensive account of the most important computational statistics. Included are discussions of numerical analysis, numerical integration, and smoothing. The author give special attention to floating point standards and numerical analysis; iterative methods for both linear and nonlinear equation, such as Gauss-Seidel method and successive over-relaxation; and computational methods for missing data, such as the EM algorithm. Also covered are new areas of interest, such as the Kalman filter, projection-pursuit methods, density estimation, and other computer-intensive techniques.

Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors

Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors
Author: Rudolf Frühwirth
Publisher: Springer Nature
Total Pages: 208
Release: 2021
Genre: Electronic books
ISBN: 303065771X

Download Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors Book in PDF, Epub and Kindle

This open access book is a comprehensive review of the methods and algorithms that are used in the reconstruction of events recorded by past, running and planned experiments at particle accelerators such as the LHC, SuperKEKB and FAIR. The main topics are pattern recognition for track and vertex finding, solving the equations of motion by analytical or numerical methods, treatment of material effects such as multiple Coulomb scattering and energy loss, and the estimation of track and vertex parameters by statistical algorithms. The material covers both established methods and recent developments in these fields and illustrates them by outlining exemplary solutions developed by selected experiments. The clear presentation enables readers to easily implement the material in a high-level programming language. It also highlights software solutions that are in the public domain whenever possible. It is a valuable resource for PhD students and researchers working on online or offline reconstruction for their experiments.