Core Statistics

Author: Simon Wood

Publisher: Cambridge University Press

ISBN: 1107071054

Category: Business & Economics

Page: 258

View: 1899

Core Statistics is a compact starter course on the theory, models, and computational tools needed to make informed use of powerful statistical methods.

Basic Concepts in Computational Physics

Author: Benjamin A. Stickler,Ewald Schachinger

Publisher: Springer

ISBN: 3319272659

Category: Science

Page: 409

View: 775

This new edition is a concise introduction to the basic methods of computational physics. Readers will discover the benefits of numerical methods for solving complex mathematical problems and for the direct simulation of physical processes. The book is divided into two main parts: Deterministic methods and stochastic methods in computational physics. Based on concrete problems, the first part discusses numerical differentiation and integration, as well as the treatment of ordinary differential equations. This is extended by a brief introduction to the numerics of partial differential equations. The second part deals with the generation of random numbers, summarizes the basics of stochastics, and subsequently introduces Monte-Carlo (MC) methods. Specific emphasis is on MARKOV chain MC algorithms. The final two chapters discuss data analysis and stochastic optimization. All this is again motivated and augmented by applications from physics. In addition, the book offers a number of appendices to provide the reader with information on topics not discussed in the main text. Numerous problems with worked-out solutions, chapter introductions and summaries, together with a clear and application-oriented style support the reader. Ready to use C++ codes are provided online.

A Course in Mathematical Statistics and Large Sample Theory

Author: Rabi Bhattacharya,Lizhen Lin,Victor Patrangenaru

Publisher: Springer

ISBN: 1493940325

Category: Mathematics

Page: 389

View: 3057

This graduate-level textbook is primarily aimed at graduate students of statistics, mathematics, science, and engineering who have had an undergraduate course in statistics, an upper division course in analysis, and some acquaintance with measure theoretic probability. It provides a rigorous presentation of the core of mathematical statistics. Part I of this book constitutes a one-semester course on basic parametric mathematical statistics. Part II deals with the large sample theory of statistics - parametric and nonparametric, and its contents may be covered in one semester as well. Part III provides brief accounts of a number of topics of current interest for practitioners and other disciplines whose work involves statistical methods.

Theoretical Statistics

Topics for a Core Course

Author: Robert W. Keener

Publisher: Springer Science & Business Media

ISBN: 9780387938394

Category: Mathematics

Page: 538

View: 5273

Intended as the text for a sequence of advanced courses, this book covers major topics in theoretical statistics in a concise and rigorous fashion. The discussion assumes a background in advanced calculus, linear algebra, probability, and some analysis and topology. Measure theory is used, but the notation and basic results needed are presented in an initial chapter on probability, so prior knowledge of these topics is not essential. The presentation is designed to expose students to as many of the central ideas and topics in the discipline as possible, balancing various approaches to inference as well as exact, numerical, and large sample methods. Moving beyond more standard material, the book includes chapters introducing bootstrap methods, nonparametric regression, equivariant estimation, empirical Bayes, and sequential design and analysis. The book has a rich collection of exercises. Several of them illustrate how the theory developed in the book may be used in various applications. Solutions to many of the exercises are included in an appendix.

Theory and Methods of Statistics

Author: P.K. Bhattacharya,Prabir Burman

Publisher: Academic Press

ISBN: 0128041234

Category: Mathematics

Page: 544

View: 9656

Theory and Methods of Statistics covers essential topics for advanced graduate students and professional research statisticians. This comprehensive resource covers many important areas in one manageable volume, including core subjects such as probability theory, mathematical statistics, and linear models, and various special topics, including nonparametrics, curve estimation, multivariate analysis, time series, and resampling. The book presents subjects such as "maximum likelihood and sufficiency," and is written with an intuitive, heuristic approach to build reader comprehension. It also includes many probability inequalities that are not only useful in the context of this text, but also as a resource for investigating convergence of statistical procedures. Codifies foundational information in many core areas of statistics into a comprehensive and definitive resource Serves as an excellent text for select master’s and PhD programs, as well as a professional reference Integrates numerous examples to illustrate advanced concepts Includes many probability inequalities useful for investigating convergence of statistical procedures

Handbook of Statistics

Computational Statistics with R

Author: N.A

Publisher: Elsevier

ISBN: 044463441X

Category: Mathematics

Page: 412

View: 9702

R is open source statistical computing software. Since the R core group was formed in 1997, R has been extended by a very large number of packages with extensive documentation along with examples freely available on the internet. It offers a large number of statistical and numerical methods and graphical tools and visualization of extraordinarily high quality. R was recently ranked in 14th place by the Transparent Language Popularity Index and 6th as a scripting language, after PHP, Python, and Perl. The book is designed so that it can be used right away by novices while appealing to experienced users as well. Each article begins with a data example that can be downloaded directly from the R website. Data analysis questions are articulated following the presentation of the data. The necessary R commands are spelled out and executed and the output is presented and discussed. Other examples of data sets with a different flavor and different set of commands but following the theme of the article are presented as well. Each chapter predents a hands-on-experience. R has superb graphical outlays and the book brings out the essentials in this arena. The end user can benefit immensely by applying the graphics to enhance research findings. The core statistical methodologies such as regression, survival analysis, and discrete data are all covered. Addresses data examples that can be downloaded directly from the R website No other source is needed to gain practical experience Focus on the essentials in graphical outlays

Statistics and Scientific Method

An Introduction for Students and Researchers

Author: Peter J. Diggle,Amanda G. Chetwynd

Publisher: Oxford University Press

ISBN: 0199543186

Category: Mathematics

Page: 172

View: 4592

An antidote to technique-orientated approaches, this text avoids the recipe-book style, giving the reader a clear understanding of how core statistical ideas of experimental design, modelling, and data analysis are integral to the scientific method. No prior knowledge of statistics is required and a range of scientific disciplines are covered.

Probability, Statistics and Econometrics

Author: Oliver Linton

Publisher: Academic Press

ISBN: 0128104961

Category: Business & Economics

Page: 388

View: 4479

Probability, Statistics and Econometrics provides a concise, yet rigorous, treatment of the field that is suitable for graduate students studying econometrics, very advanced undergraduate students, and researchers seeking to extend their knowledge of the trinity of fields that use quantitative data in economic decision-making. The book covers much of the groundwork for probability and inference before proceeding to core topics in econometrics. Authored by one of the leading econometricians in the field, it is a unique and valuable addition to the current repertoire of econometrics textbooks and reference books. Synthesizes three substantial areas of research, ensuring success in a subject matter than can be challenging to newcomers Focused and modern coverage that provides relevant examples from economics and finance Contains some modern frontier material, including bootstrap and lasso methods not treated in similar-level books Collects the necessary material for first semester Economics PhD students into a single text

Lectures on the Poisson Process

Author: Günter Last,Mathew Penrose

Publisher: Cambridge University Press

ISBN: 1107088011

Category: Mathematics

Page: 308

View: 6466

A modern introduction to the Poisson process, with general point processes and random measures, and applications to stochastic geometry.

Bayesian Core: A Practical Approach to Computational Bayesian Statistics

Author: Jean-Michel Marin,Christian Robert

Publisher: Springer Science & Business Media

ISBN: 0387389792

Category: Computers

Page: 255

View: 8381

This Bayesian modeling book is intended for practitioners and applied statisticians looking for a self-contained entry to computational Bayesian statistics. Focusing on standard statistical models, it provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical justifications.

Statistik-Workshop für Programmierer

Author: Allen B. Downey

Publisher: O'Reilly Germany

ISBN: 3868993436

Category: Computers

Page: 160

View: 7137

Wenn Sie programmieren können, beherrschen Sie bereits Techniken, um aus Daten Wissen zu extrahieren. Diese kompakte Einführung in die Statistik zeigt Ihnen, wie Sie rechnergestützt, anstatt auf mathematischem Weg Datenanalysen mit Python durchführen können. Praktischer Programmier-Workshop statt grauer Theorie: Das Buch führt Sie anhand eines durchgängigen Fallbeispiels durch eine vollständige Datenanalyse -- von der Datensammlung über die Berechnung statistischer Kennwerte und Identifikation von Mustern bis hin zum Testen statistischer Hypothesen. Gleichzeitig werden Sie mit statistischen Verteilungen, den Regeln der Wahrscheinlichkeitsrechnung, Visualisierungsmöglichkeiten und vielen anderen Arbeitstechniken und Konzepten vertraut gemacht. Statistik-Konzepte zum Ausprobieren: Entwickeln Sie über das Schreiben und Testen von Code ein Verständnis für die Grundlagen von Wahrscheinlichkeitsrechnung und Statistik: Überprüfen Sie das Verhalten statistischer Merkmale durch Zufallsexperimente, zum Beispiel indem Sie Stichproben aus unterschiedlichen Verteilungen ziehen. Nutzen Sie Simulationen, um Konzepte zu verstehen, die auf mathematischem Weg nur schwer zugänglich sind. Lernen Sie etwas über Themen, die in Einführungen üblicherweise nicht vermittelt werden, beispielsweise über die Bayessche Schätzung. Nutzen Sie Python zur Bereinigung und Aufbereitung von Rohdaten aus nahezu beliebigen Quellen. Beantworten Sie mit den Mitteln der Inferenzstatistik Fragestellungen zu realen Daten.

Introduction to Statistical Analysis of Laboratory Data

Author: Alfred Bartolucci,Karan P. Singh,Sejong Bae

Publisher: John Wiley & Sons

ISBN: 1118736834

Category: Mathematics

Page: 256

View: 4075

Introduction to Statistical Analysis of Laboratory Data presents a detailed discussion of important statistical concepts and methods of data presentation and analysis Provides detailed discussions on statistical applications including a comprehensive package of statistical tools that are specific to the laboratory experiment process Introduces terminology used in many applications such as the interpretation of assay design and validation as well as “fit for purpose” procedures including real world examples Includes a rigorous review of statistical quality control procedures in laboratory methodologies and influences on capabilities Presents methodologies used in the areas such as method comparison procedures, limit and bias detection, outlier analysis and detecting sources of variation Analysis of robustness and ruggedness including multivariate influences on response are introduced to account for controllable/uncontrollable laboratory conditions

Business Statistics Made Easy in SAS

Author: Gregory Lee

Publisher: SAS Institute

ISBN: 162960044X

Category: Computers

Page: 384

View: 721

Learn or refresh core statistical methods for business with SAS® and approach real business analytics issues and techniques using a practical approach that avoids complex mathematics and instead employs easy-to-follow explanations. Business Statistics Made Easy in SAS® is designed as a user-friendly, practice-oriented, introductory text to teach businesspeople, students, and others core statistical concepts and applications. It begins with absolute core principles and takes you through an overview of statistics, data and data collection, an introduction to SAS®, and basic statistics (descriptive statistics and basic associational statistics). The book also provides an overview of statistical modeling, effect size, statistical significance and power testing, basics of linear regression, introduction to comparison of means, basics of chi-square tests for categories, extrapolating statistics to business outcomes, and some topical issues in statistics, such as big data, simulation, machine learning, and data warehousing. The book steers away from complex mathematical-based explanations, and it also avoids basing explanations on the traditional build-up of distributions, probability theory and the like, which tend to lose the practice-oriented reader. Instead, it teaches the core ideas of statistics through methods such as careful, intuitive written explanations, easy-to-follow diagrams, step-by-step technique implementation, and interesting metaphors. With no previous SAS experience necessary, Business Statistics Made Easy in SAS® is an ideal introduction for beginners. It is suitable for introductory undergraduate classes, postgraduate courses such as MBA refresher classes, and for the business practitioner. It is compatible with SAS® University Edition.

Statistical Modeling and Inference for Social Science

Author: Sean Gailmard

Publisher: Cambridge University Press

ISBN: 1107003148

Category: Business & Economics

Page: 388

View: 6609

Written specifically for graduate students and practitioners beginning social science research, Statistical Modeling and Inference for Social Science covers the essential statistical tools, models and theories that make up the social scientist's toolkit. Assuming no prior knowledge of statistics, this textbook introduces students to probability theory, statistical inference and statistical modeling, and emphasizes the connection between statistical procedures and social science theory. Sean Gailmard develops core statistical theory as a set of tools to model and assess relationships between variables - the primary aim of social scientists - and demonstrates the ways in which social scientists express and test substantive theoretical arguments in various models. Chapter exercises guide students in applying concepts to data, extending their grasp of core theoretical concepts. Students gain the ability to create, read and critique statistical applications in their fields of interest.

Applied Statistics for Engineers and Physical Scientists

Author: Robert V. Hogg,Johannes Ledolter

Publisher: Pearson Higher Ed

ISBN: 0321831470

Category: Computers

Page: 608

View: 1771

This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. This hugely anticipated revision has held true to its core strengths, while bringing the book fully up to date with modern engineering statistics. Written by two leading statisticians, Statistics for Engineers and Physical Scientists, Third Edition, provides the necessary bridge between basic statistical theory and interesting applications. Students solve the same problems that engineers and scientists face, and have the opportunity to analyze real data sets. Larger-scale projects are a unique feature of this book, which let students analyze and interpret real data, while also encouraging them to conduct their own studies and compare approaches and results. This book assumes a calculus background. It is appropriate for undergraduate and graduate engineering or physical science courses or for students taking an introductory course applied statistics.

Introduction to Malliavin Calculus

Author: David Nualart,Eulalia Nualart

Publisher: Cambridge University Press

ISBN: 1107039126

Category: Business & Economics

Page: 246

View: 8990

This textbook offers a compact introductory course on Malliavin calculus, an active and powerful area of research. It covers recent applications, including density formulas, regularity of probability laws, central and non-central limit theorems for Gaussian functionals, convergence of densities and non-central limit theorems for the local time of Brownian motion. The book also includes a self-contained presentation of Brownian motion and stochastic calculus, as well as Lvy processes and stochastic calculus for jump processes. Accessible to non-experts, the book can be used by graduate students and researchers to develop their mastery of the core techniques necessary for further study.

Computational Analysis and Understanding of Natural Languages: Principles, Methods and Applications

Author: N.A

Publisher: Elsevier

ISBN: 0444640436

Category: Mathematics

Page: 537

View: 1070

Computational Analysis and Understanding of Natural Languages: Principles, Methods and Applications, Volume 38, the latest release in this monograph that provides a cohesive and integrated exposition of these advances and associated applications, includes new chapters on Linguistics: Core Concepts and Principles, Grammars, Open-Source Libraries, Application Frameworks, Workflow Systems, Mathematical Essentials, Probability, Inference and Prediction Methods, Random Processes, Bayesian Methods, Machine Learning, Artificial Neural Networks for Natural Language Processing, Information Retrieval, Language Core Tasks, Language Understanding Applications, and more. The synergistic confluence of linguistics, statistics, big data, and high-performance computing is the underlying force for the recent and dramatic advances in analyzing and understanding natural languages, hence making this series all the more important. Provides a thorough treatment of open-source libraries, application frameworks and workflow systems for natural language analysis and understanding Presents new chapters on Linguistics: Core Concepts and Principles, Grammars, Open-Source Libraries, Application Frameworks, Workflow Systems, Mathematical Essentials, Probability, and more

A Kinetic View of Statistical Physics

Author: Pavel L. Krapivsky,Sidney Redner,Eli Ben-Naim

Publisher: Cambridge University Press

ISBN: 1139493345

Category: Science

Page: N.A

View: 1342

Aimed at graduate students, this book explores some of the core phenomena in non-equilibrium statistical physics. It focuses on the development and application of theoretical methods to help students develop their problem-solving skills. The book begins with microscopic transport processes: diffusion, collision-driven phenomena, and exclusion. It then presents the kinetics of aggregation, fragmentation and adsorption, where the basic phenomenology and solution techniques are emphasized. The following chapters cover kinetic spin systems, both from a discrete and a continuum perspective, the role of disorder in non-equilibrium processes, hysteresis from the non-equilibrium perspective, the kinetics of chemical reactions, and the properties of complex networks. The book contains 200 exercises to test students' understanding of the subject. A link to a website hosted by the authors, containing supplementary material including solutions to some of the exercises, can be found at www.cambridge.org/9780521851039.

Principles of statistical data handling

Author: Fred Davidson

Publisher: Sage Publications, Inc

ISBN: 9780761901020

Category: Education

Page: 319

View: 9603

This book will help the reader understand the principles of data handling and make better use of computer data in research or study. It demonstrates how to input, manipulate and debug data to make substantive analysis easier and more accurate. Using a series of principles, universal concepts that apply no matter what the data-gathering context or computer software, Fred Davidson presents a situation or a problem, suggests how it might be resolved and demonstrates the implementation of each principle as it appears in the command languages of SAS and SPSS.