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Applied Stochastic Processes
by null Ming LiaoApplied Stochastic Processes presents a concise, graduate-level treatment of the subject, emphasizing applications and practical computation. It also establishes the complete mathematical theory in an accessible way. After reviewing basic probability, the text covers Poisson processes, renewal processes, discrete- and continuous-time Markov chains,
Applied Structural Equation Modeling using AMOS: Basic to Advanced Techniques
by Joel E. CollierThis is an essential how-to guide on the application of structural equation modeling (SEM) techniques with the AMOS software, focusing on the practical applications of both simple and advanced topics. Written in an easy-to-understand conversational style, the book covers everything from data collection and screening to confirmatory factor analysis, structural model analysis, mediation, moderation, and more advanced topics such as mixture modeling, censored date, and non-recursive models. Through step-by-step instructions, screen shots, and suggested guidelines for reporting, Collier cuts through abstract definitional perspectives to give insight on how to actually run analysis. Unlike other SEM books, the examples used will often start in SPSS and then transition to AMOS so that the reader can have full confidence in running the analysis from beginning to end. Best practices are also included on topics like how to determine if your SEM model is formative or reflective, making it not just an explanation of SEM topics, but a guide for researchers on how to develop a strong methodology while studying their respective phenomenon of interest. With a focus on practical applications of both basic and advanced topics, and with detailed work-through examples throughout, this book is ideal for experienced researchers and beginners across the behavioral and social sciences.
Applied Summability Methods (SpringerBriefs in Mathematics)
by M. MursaleenThis short monograph is the first book to focus exclusively on the study of summability methods, which have become active areas of research in recent years. The book provides basic definitions of sequence spaces, matrix transformations, regular matrices and some special matrices, making the material accessible to mathematicians who are new to the subject Among the core items covered are the proof of the Prime Number Theorem using Lambert's summability and Wiener's Tauberian theorem, some results on summability tests for singular points of an analytic function, and analytic continuation through Lototski summability Almost summability is introduced to prove Korovkin-type approximation theorems and the last chapters feature statistical summability, statistical approximation, and some applications of summability methods in fixed point theorems.
Applied Surrogate Endpoint Evaluation Methods with SAS and R (Chapman & Hall/CRC Biostatistics Series)
by Ariel Alonso Theophile Bigirumurame Tomasz Burzykowski Marc Buyse Geert Molenberghs Leacky Muchene Nolen Joy Perualila Ziv Shkedy Wim Van der ElstAn important factor that affects the duration, complexity and cost of a clinical trial is the endpoint used to study the treatment’s efficacy. When a true endpoint is difficult to use because of such factors as long follow-up times or prohibitive cost, it is sometimes possible to use a surrogate endpoint that can be measured in a more convenient or cost-effective way. This book focuses on the use of surrogate endpoint evaluation methods in practice, using SAS and R.
Applied Survey Data Analysis (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)
by Brady T. West Patricia A. Berglund Steven G. HeeringaHighly recommended by the Journal of Official Statistics, The American Statistician, and other journals, Applied Survey Data Analysis, Second Edition provides an up-to-date overview of state-of-the-art approaches to the analysis of complex sample survey data. Building on the wealth of material on practical approaches to descriptive analysis and regression modeling from the first edition, this second edition expands the topics covered and presents more step-by-step examples of modern approaches to the analysis of survey data using the newest statistical software. Designed for readers working in a wide array of disciplines who use survey data in their work, this book continues to provide a useful framework for integrating more in-depth studies of the theory and methods of survey data analysis. An example-driven guide to the applied statistical analysis and interpretation of survey data, the second edition contains many new examples and practical exercises based on recent versions of real-world survey data sets. Although the authors continue to use Stata for most examples in the text, they also continue to offer SAS, SPSS, SUDAAN, R, WesVar, IVEware, and Mplus software code for replicating the examples on the book’s updated website.
Applied Survival Analysis
by Susanne May Stanley Lemeshow David W. Hosmer Jr.THE MOST PRACTICAL, UP-TO-DATE GUIDE TO MODELLING AND ANALYZING TIME-TO-EVENT DATA--NOW IN A VALUABLE NEW EDITIONSince publication of the first edition nearly a decade ago, analyses using time-to-event methods have increase considerably in all areas of scientific inquiry mainly as a result of model-building methods available in modern statistical software packages. However, there has been minimal coverage in the available literature to9 guide researchers, practitioners, and students who wish to apply these methods to health-related areas of study. Applied Survival Analysis, Second Edition provides a comprehensive and up-to-date introduction to regression modeling for time-to-event data in medical, epidemiological, biostatistical, and other health-related research.This book places a unique emphasis on the practical and contemporary applications of regression modeling rather than the mathematical theory. It offers a clear and accessible presentation of modern modeling techniques supplemented with real-world examples and case studies. Key topics covered include: variable selection, identification of the scale of continuous covariates, the role of interactions in the model, assessment of fit and model assumptions, regression diagnostics, recurrent event models, frailty models, additive models, competing risk models, and missing data.Features of the Second Edition include:Expanded coverage of interactions and the covariate-adjusted survival functionsThe use of the Worchester Heart Attack Study as the main modeling data set for illustrating discussed concepts and techniquesNew discussion of variable selection with multivariable fractional polynomialsFurther exploration of time-varying covariates, complex with examplesAdditional treatment of the exponential, Weibull, and log-logistic parametric regression modelsIncreased emphasis on interpreting and using results as well as utilizing multiple imputation methods to analyze data with missing valuesNew examples and exercises at the end of each chapterAnalyses throughout the text are performed using Stata® Version 9, and an accompanying FTP site contains the data sets used in the book. Applied Survival Analysis, Second Edition is an ideal book for graduate-level courses in biostatistics, statistics, and epidemiologic methods. It also serves as a valuable reference for practitioners and researchers in any health-related field or for professionals in insurance and government.
Applied Technologies: Third International Conference, Icat 2021, Quito, Ecuador, October 27-29, 2021, Proceedings (Communications In Computer And Information Science Series #1535)
by Miguel Botto-Tobar Marcelo Zambrano Vizuete Sergio Montes León Pablo Torres-Carrión Benjamin Durakovic<p>This volume constitutes the refereed proceedings of the Third International Conference on Applied Technologies, ICAT 2021, held in Quito, Ecuador, in October 2021.<p> <p>The 40 papers were carefully reviewed and selected from 201 submissions. The papers are organized according to the following topics: communication; computing; e-government and e-participation; e-learning; electronics; general track; intelligent systems; machine vision; security; technology trends.<p>
Applied Technologies: Third International Conference, Icat 2021, Quito, Ecuador, October 27-29, 2021, Proceedings (Communications In Computer And Information Science Series #1535)
by Miguel Botto-Tobar Marcelo Zambrano Vizuete Sergio Montes León Pablo Torres-Carrión Benjamin Durakovic<p>This volume constitutes the refereed proceedings of the Third International Conference on Applied Technologies, ICAT 2021, held in Quito, Ecuador, in October 2021.<p> <p>The 40 papers were carefully reviewed and selected from 201 submissions. The papers are organized according to the following topics: communication; computing; e-government and e-participation; e-learning; electronics; general track; intelligent systems; machine vision; security; technology trends.<p>
Applied Time Series Analysis and Forecasting with Python (Statistics and Computing)
by Changquan Huang Alla PetukhinaThis textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time. Its data-driven approach to analyzing and modeling time series data helps new learners to visualize and interpret both the raw data and its computed results. Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equally appeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems.
Applied Time Series Analysis and Innovative Computing (Lecture Notes In Electrical Engineering #59)
by Sio-Iong AoApplied Time Series Analysis and Innovative Computing contains the applied time series analysis and innovative computing paradigms, with frontier application studies for the time series problems based on the recent works at the Oxford University Computing Laboratory, University of Oxford, the University of Hong Kong, and the Chinese University of Hong Kong. The monograph was drafted when the author was a post-doctoral fellow in Harvard School of Engineering and Applied Sciences, Harvard University. It provides a systematic introduction to the use of innovative computing paradigms as an investigative tool for applications in time series analysis. Applied Time Series Analysis and Innovative Computing offers the state of art of tremendous advances in applied time series analysis and innovative computing paradigms and also serves as an excellent reference work for researchers and graduate students working on applied time series analysis and innovative computing paradigms.
Applied Time Series Analysis with R
by Wayne A. Woodward Henry L. Gray Alan C. ElliottVirtually any random process developing chronologically can be viewed as a time series. In economics closing prices of stocks, the cost of money, the jobless rate, and retail sales are just a few examples of many. Developed from course notes and extensively classroom-tested, Applied Time Series Analysis with R, Second Edition includes examples across a variety of fields, develops theory, and provides an R-based software package to aid in addressing time series problems in a broad spectrum of fields. The material is organized in an optimal format for graduate students in statistics as well as in the natural and social sciences to learn to use and understand the tools of applied time series analysis. Features Gives readers the ability to actually solve significant real-world problems Addresses many types of nonstationary time series and cutting-edge methodologies Promotes understanding of the data and associated models rather than viewing it as the output of a "black box" Provides the R package tswge available on CRAN which contains functions and over 100 real and simulated data sets to accompany the book. Extensive help regarding the use of tswge functions is provided in appendices and on an associated website. Over 150 exercises and extensive support for instructors The second edition includes additional real-data examples, uses R-based code that helps students easily analyze data, generate realizations from models, and explore the associated characteristics. It also adds discussion of new advances in the analysis of long memory data and data with time-varying frequencies (TVF).
Applied Univariate, Bivariate and Multivariate Statistics
by Daniel J. DenisA clear and efficient balance between theory and application of statistical modeling techniques in the social and behavioral sciences Written as a general and accessible introduction, Applied Univariate, Bivariate, and Multivariate Statistics provides an overview of statistical modeling techniques used in fields in the social and behavioral sciences. Blending statistical theory and methodology, the book surveys both the technical and theoretical aspects of good data analysis. Featuring applied resources at various levels, the book includes statistical techniques such as t-tests and correlation as well as more advanced procedures such as MANOVA, factor analysis, and structural equation modeling. To promote a more in-depth interpretation of statistical techniques across the sciences, the book surveys some of the technical arguments underlying formulas and equations. Applied Univariate, Bivariate, and Multivariate Statistics also features Demonstrations of statistical techniques using software packages such as R and SPSS® Examples of hypothetical and real data with subsequent statistical analyses Historical and philosophical insights into many of the techniques used in modern social science A companion website that includes further instructional details, additional data sets, solutions to selected exercises, and multiple programming options An ideal textbook for courses in statistics and methodology at the upper-undergraduate and graduate-levels in psychology, political science, biology, sociology, education, economics, communications, law, and survey research, Applied Univariate, Bivariate, and Multivariate Statistics is also a useful reference for practitioners and researchers in their field of application.
Applied Univariate, Bivariate, and Multivariate Statistics: Understanding Statistics for Social and Natural Scientists, With Applications in SPSS and R
by Daniel J. DenisAN UPDATED GUIDE TO STATISTICAL MODELING TECHNIQUES USED IN THE SOCIAL AND BEHAVIORAL SCIENCES The revised and updated second edition of Applied Univariate, Bivariate, and Multivariate Statistics: Understanding Statistics for Social and Natural Scientists, with Applications in SPSS and R contains an accessible introduction to statistical modeling techniques commonly used in the social and behavioral sciences. The text offers a blend of statistical theory and methodology and reviews both the technical and theoretical aspects of good data analysis. Featuring applied resources at various levels, the book includes statistical techniques using software packages such as R and SPSS®. To promote a more in-depth interpretation of statistical techniques across the sciences, the book surveys some of the technical arguments underlying formulas and equations. The thoroughly updated edition includes new chapters on nonparametric statistics and multidimensional scaling, and expanded coverage of time series models. The second edition has been designed to be more approachable by minimizing theoretical or technical jargon and maximizing conceptual understanding with easy-to-apply software examples. This important text: Offers demonstrations of statistical techniques using software packages such as R and SPSS® Contains examples of hypothetical and real data with statistical analyses Provides historical and philosophical insights into many of the techniques used in modern social science Includes a companion website that includes further instructional details, additional data sets, solutions to selected exercises, and multiple programming options Written for students of social and applied sciences, Applied Univariate, Bivariate, and Multivariate Statistics, Second Edition offers a text to statistical modeling techniques used in social and behavioral sciences.
Applied Univariate, Bivariate, and Multivariate Statistics Using Python: A Beginner's Guide to Advanced Data Analysis
by Daniel J. DenisApplied Univariate, Bivariate, and Multivariate Statistics Using Python A practical, “how-to” reference for anyone performing essential statistical analyses and data management tasks in PythonApplied Univariate, Bivariate, and Multivariate Statistics Using Python delivers a comprehensive introduction to a wide range of statistical methods performed using Python in a single, one-stop reference. The book contains user-friendly guidance and instructions on using Python to run a variety of statistical procedures without getting bogged down in unnecessary theory. Throughout, the author emphasizes a set of computational tools used in the discovery of empirical patterns, as well as several popular statistical analyses and data management tasks that can be immediately applied.Most of the datasets used in the book are small enough to be easily entered into Python manually, though they can also be downloaded for free from www.datapsyc.com. Only minimal knowledge of statistics is assumed, making the book perfect for those seeking an easily accessible toolkit for statistical analysis with Python. Applied Univariate, Bivariate, and Multivariate Statistics Using Python represents the fastest way to learn how to analyze data with Python.Readers will also benefit from the inclusion of:A review of essential statistical principles, including types of data, measurement, significance tests, significance levels, and type I and type II errorsAn introduction to Python, exploring how to communicate with PythonA treatment of exploratory data analysis, basic statistics and visual displays, including frequencies and descriptives, q-q plots, box-and-whisker plots, and data managementAn introduction to topics such as ANOVA, MANOVA and discriminant analysis, regression, principal components analysis, factor analysis, cluster analysis, among others, exploring the nature of what these techniques can vs. cannot do on a methodological levelPerfect for undergraduate and graduate students in the social, behavioral, and natural sciences, Applied Univariate, Bivariate, and Multivariate Statistics Using Python will also earn a place in the libraries of researchers and data analysts seeking a quick go-to resource for univariate, bivariate, and multivariate analysis in Python.
Applying Analytics: A Practical Introduction
by E. S. LevineNewcomers to quantitative analysis need practical guidance on how to analyze data in the real world yet most introductory books focus on lengthy derivations and justifications instead of practical techniques. Covering the technical and professional skills needed by analysts in the academic, private, and public sectors, Applying Analytics: A Practic
Applying Fuzzy Logic for the Digital Economy and Society (Fuzzy Management Methods)
by Andreas Meier Edy Portmann Luis TeránThis edited book presents the state-of-the-art of applying fuzzy logic to managerial decision-making processes in areas such as fuzzy-based portfolio management, recommender systems, performance assessment and risk analysis, among others. Presenting the latest research, with a strong focus on applications and case studies, it is a valuable resource for researchers, practitioners, project leaders and managers wanting to apply or improve their fuzzy-based skills.
Applying Graph Theory in Ecological Research
by Dale Mark R. T.Graph theory can be applied to ecological questions in many ways, and more insights can be gained by expanding the range of graph theoretical concepts applied to a specific system. But how do you know which methods might be used? And what do you do with the graph once it has been obtained? This book provides a broad introduction to the application of graph theory in different ecological systems, providing practical guidance for researchers in ecology and related fields. Readers are guided through the creation of an appropriate graph for the system being studied, including the application of spatial, spatio-temporal, and more abstract structural process graphs. Simple figures accompany the explanations to add clarity, and a broad range of ecological phenomena from many ecological systems are covered. This is the ideal book for graduate students and researchers looking to apply graph theoretical methods in their work.
Applying Math with Python: Practical recipes for solving computational math problems using Python programming and its libraries
by Sam MorleyThis book is for professional programmers and students looking to solve mathematical problems computationally using Python. Advanced mathematics knowledge is not a requirement, but a basic knowledge of mathematics will help you to get the most out of this book. The book assumes familiarity with Python concepts of data structures.
Applying Math with Python: Over 70 practical recipes for solving real-world computational math problems, 2nd Edition
by Sam MorleyDiscover easy-to-follow solutions and techniques to help you to implement applied mathematical concepts such as probability, calculus, and equations using Python's numeric and scientific librariesKey FeaturesCompute complex mathematical problems using programming logic with the help of step-by-step recipesLearn how to use Python libraries for computation, mathematical modeling, and statisticsDiscover simple yet effective techniques for solving mathematical equations and apply them in real-world statisticsBook DescriptionThe updated edition of Applying Math with Python will help you solve complex problems in a wide variety of mathematical fields in simple and efficient ways. Old recipes have been revised for new libraries and several recipes have been added to demonstrate new tools such as JAX.You'll start by refreshing your knowledge of several core mathematical fields and learn about packages covered in Python's scientific stack, including NumPy, SciPy, and Matplotlib. As you progress, you'll gradually get to grips with more advanced topics of calculus, probability, and networks (graph theory). Once you've developed a solid base in these topics, you'll have the confidence to set out on math adventures with Python as you explore Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code.By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.What you will learnBecome familiar with basic Python packages, tools, and libraries for solving mathematical problemsExplore real-world applications of mathematics to reduce a problem in optimizationUnderstand the core concepts of applied mathematics and their application in computer scienceFind out how to choose the most suitable package, tool, or technique to solve a problemImplement basic mathematical plotting, change plot styles, and add labels to plots using MatplotlibGet to grips with probability theory with the Bayesian inference and Markov Chain Monte Carlo (MCMC) methodsWho this book is forWhether you are a professional programmer or a student looking to solve mathematical problems computationally using Python, this is the book for you. Advanced mathematics proficiency is not a prerequisite, but basic knowledge of mathematics will help you to get the most out of this Python math book. Familiarity with the concepts of data structures in Python is assumed.
Applying Mathematics: Tests (Mathematics For Christian Living)
by Glenn Auker Seth Rudolph Keith KriederApplying Mathematics Grade 8 Math Chapter Tests
Applying Mathematics: Grade 8 (Mathematics For Christian Living Series)
by John Mark Shenk Marian Baltozer Amy Herr Christine Collins Glenn Auker Seth Rudolph Keith KriederThe teacher's manual comes in two volumes. Each lesson has full-size pupil's pages, with answers filled in. Extra pages guide the teacher in lesson preparation and include answer keys for quizzes, speed teats, and chapter tests.
Applying Maths in Construction
by Antoinette Tourret John HumphreysThis book and its accompanying Teacher's Pack are the result of a project, supported by the Nuffield Foundation, to provide flexible learning materials for the Basic Application of Number core skill for both the NVQs and GNVQ in construction and the construction crafts. The student book uses a unique approach to explain how mathematical principles apply to construction tasks. Each chapter forms an individual construction project and uses the full range of number skills from the fundamentals of addition and subtraction to statistics, trigonometry and technical drawing. Successfully completed projects provide the student with the required portfolio of evidence for their course. Notes throughout the text refer the student to the relevant module in the Teacher's Pack, which contains assessments, tests and detailed explanations of the number skills needed to complete the projects.
Applying Particle Swarm Optimization: New Solutions and Cases for Optimized Portfolios (International Series in Operations Research & Management Science #306)
by Burcu Adıgüzel MercangözThis book explains the theoretical structure of particle swarm optimization (PSO) and focuses on the application of PSO to portfolio optimization problems. The general goal of portfolio optimization is to find a solution that provides the highest expected return at each level of portfolio risk. According to H. Markowitz’s portfolio selection theory, as new assets are added to an investment portfolio, the total risk of the portfolio’s decreases depending on the correlations of asset returns, while the expected return on the portfolio represents the weighted average of the expected returns for each asset.The book explains PSO in detail and demonstrates how to implement Markowitz’s portfolio optimization approach using PSO. In addition, it expands on the Markowitz model and seeks to improve the solution-finding process with the aid of various algorithms. In short, the book provides researchers, teachers, engineers, managers and practitioners with many tools they need to apply the PSO technique to portfolio optimization.
Applying Power Series to Differential Equations: An Exploration through Questions and Projects (Problem Books in Mathematics)
by James Sochacki Anthony TongenThis book is aimed to undergraduate STEM majors and to researchers using ordinary differential equations. It covers a wide range of STEM-oriented differential equation problems that can be solved using computational power series methods. Many examples are illustrated with figures and each chapter ends with discovery/research questions most of which are accessible to undergraduate students, and almost all of which may be extended to graduate level research. Methodologies implemented may also be useful for researchers to solve their differential equations analytically or numerically. The textbook can be used as supplementary for undergraduate coursework, graduate research, and for independent study.
Applying Quantitative Bias Analysis to Epidemiologic Data (Statistics for Biology and Health)
by Matthew P. Fox Timothy L. Lash Aliza K. FinkThis text provides the first-ever compilation of bias analysis methods for use with epidemiologic data. It guides the reader through the planning stages of bias analysis, including the design of validation studies and the collection of validity data from other sources. Three chapters present methods for corrections to address selection bias, uncontrolled confounding, and classification errors. Subsequent chapters extend these methods to multidimensional bias analysis, probabilistic bias analysis, and multiple bias analysis. The text concludes with a chapter on presentation and interpretation of bias analysis results. Although techniques for bias analysis have been available for decades, these methods are considered difficult to implement. This text not only gathers the methods into one cohesive and organized presentation, it also explains the methods in a consistent fashion and provides customizable spreadsheets to implement the solutions. By downloading the spreadsheets (available at links provided in the text), readers can follow the examples in the text and then modify the spreadsheet to complete their own bias analyses. Readers without experience using quantitative bias analysis will be able to design, implement, and understand bias analyses that address the major threats to the validity of epidemiologic research. More experienced analysts will value the compilation of bias analysis methods and links to software tools that facilitate their projects.