Analysis Of Distributional Data

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Analysis Of Distributional Data

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Author : Paula Brito
language : en
Publisher: CRC Press
Release Date : 2022-04-26

Analysis Of Distributional Data written by Paula Brito and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-26 with Mathematics categories.

In a time when increasingly larger and complex data collections are being produced, it is clear that new and adaptive forms of data representation and analysis have to be conceived and implemented. Distributional data, i.e., data where a distribution rather than a single value is recorded for each descriptor, on each unit, come into this framework. Distributional data may result from the aggregation of large amounts of open/collected/generated data, or it may be directly available in a structured or unstructured form, describing the variability of some features. This book provides models and methods for the representation, analysis, interpretation, and organization of distributional data, taking into account its specific nature, and not relying on a reduction to single values, to be conform to classical paradigms. Conceived as an edited book, gathering contributions from multiple authors, the book presents alternative representations and analysis’ methods for distributional data of different types, and in particular, -Uni- and bi-variate descriptive statistics for distributional data -Clustering and classification methodologies -Methods for the representation in low-dimensional spaces -Regression models and forecasting approaches for distribution-valued variables Furthermore, the different chapters -Feature applications to show how the proposed methods work in practice, and how results are to be interpreted, -Often provide information about available software. The methodologies presented in this book constitute cutting-edge developments for stakeholders from all domains who produce and analyse large amounts of complex data, to be analysed in the form of distributions. The book is hence of interest for companies operating not only in the area of data analytics, but also on logistics, energy and finance. It also concerns national statistical institutes and other institutions at European and international level, where microdata is aggregated to preserve confidentiality and allow for analysis at the appropriate regional level. Academics will find in the analysis of distributional data a challenging up-to-date field of research.

Relative Distribution Methods In The Social Sciences

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Author : Mark S. Handcock
language : en
Publisher:
Release Date : 2014-01-15

Relative Distribution Methods In The Social Sciences written by Mark S. Handcock and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-15 with categories.

Essential First Steps To Data Analysis

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Author : Carol S. Parke
language : en
Publisher: SAGE
Release Date : 2013

Essential First Steps To Data Analysis written by Carol S. Parke and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Reference categories.

The purpose of this book is to provide instruction and guidance on preparing quantitative data sets prior to answering a study's research questions. Preparation may involve data management and manipulation tasks, data organization, structural changes to data files, or conducting preliminary analysis such as examining the scale of a variable, the validity of assumptions or the nature and extent of missing data. The oresultso from these essential first steps can also help guide a researcher in selecting the most appropriate statistical tests for his/her study. The book is intended to serve as a supplemental text in statistics or research courses offered in graduate programs in education, counseling, school psychology, behavioral sciences, and social sciences as well as undergraduate programs that contain a heavy emphasis on statistics. The content and issues covered are also beneficial for faculty and researchers who are knowledgeable about research design and able to use a statistical software package, but are unsure of the first steps to take with their data. Increasingly, faculty are forming partnerships with schools, clinics, and other institutions to help them analyze data in their extensive databases. This book can serve as a reference for helping them get existing data files in an appropriate form to run statistical analysis. This book is not a replacement for a statistics textbook. It assumes that readers have some knowledge of basic statistical concepts and use of statistical software, or that they will be learning these concepts and skills concurrently throughout the course. SPSS was chosen to illustrate the preparation, evaluation, and manipulation of data. However, students or researchers who do not use SPSS will benefit from the content since the overall structure and pedagogical approach of the book focuses heavily on the data issues and decisions to be made.

Statistics 101

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Author : David Borman
language : en
Publisher: Simon and Schuster
Release Date : 2018-12-18

Statistics 101 written by David Borman and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-18 with Mathematics categories.

A comprehensive guide to statistics—with information on collecting, measuring, analyzing, and presenting statistical data—continuing the popular 101 series. Data is everywhere. In the age of the internet and social media, we’re responsible for consuming, evaluating, and analyzing data on a daily basis. From understanding the percentage probability that it will rain later today, to evaluating your risk of a health problem, or the fluctuations in the stock market, statistics impact our lives in a variety of ways, and are vital to a variety of careers and fields of practice. Unfortunately, most statistics text books just make us want to take a snooze, but with Statistics 101, you’ll learn the basics of statistics in a way that is both easy-to-understand and apply. From learning the theory of probability and different kinds of distribution concepts, to identifying data patterns and graphing and presenting precise findings, this essential guide can help turn statistical math from scary and complicated, to easy and fun. Whether you are a student looking to supplement your learning, a worker hoping to better understand how statistics works for your job, or a lifelong learner looking to improve your grasp of the world, Statistics 101 has you covered.

Beyond The Worst Case Analysis Of Algorithms

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Author : Tim Roughgarden
language : en
Publisher: Cambridge University Press
Release Date : 2021-01-14

Beyond The Worst Case Analysis Of Algorithms written by Tim Roughgarden and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-14 with Computers categories.

Introduces exciting new methods for assessing algorithms for problems ranging from clustering to linear programming to neural networks.

Distributional Analysis With L Moment Statistics Using The R Environment For Statistical Computing

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Author : William H. Asquith
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2011

Distributional Analysis With L Moment Statistics Using The R Environment For Statistical Computing written by William H. Asquith and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with L-moments categories.

This monograph (2nd printing) is the most complete account to date of L-moment statistics in the context of distributional analysis using an open-source programming environment-the R environment for statistical computing. The target audience are engineers/scientists with limited backgrounds in statistics and computer programming but with responsibilities in analyzing highly non-Normal, skewed, or heavy-tailed data. The monograph is written in continuous narrative and is oriented around the software package "lmomco" previously written by the author but tremendously expanded and refined for the monograph. The monograph covers an introduction to R and cites the extensive book-literature on computational and statistical analysis using R. Note, an errata can be found in the text file ERRATA_FOR_ISBN9781463508418.txt that is distributed with the lmomco package.The monograph covers, by a large-scale coupling of source code to typeset mathematics, a myriad of topics including quantile functions, order statistics, product moments, probability-weighted moments (PWMs), censored PWMs, L-moments (censored/trimmed), L-comoments, and numerous probability distributions including the two-parameter Cauchy, Exponential, Normal, Gamma, Gumbel, reverse Gumbel, Kumaraswamy, Rayleigh, and Rice; the three-parameter Generalized Extreme Value, Generalized Logistic, Generalized Normal, Generalized Pareto (GPA), right-censored (RC) GPA, trimmed GPA, Pearson Type III, and Weibull; four- and more parameter distributions including the Kappa, Generalized Lambda (GLD), trimmed GLD, and Wakeby; and the method of L-moments and method of PWMs for these distributions.The monograph thoroughly describes L-moment ratio diagrams. Venerable statistics such as Sen weighted mean and Gini mean difference also are considered as are emergent statistical functions such as copulas. Extensive simulation studies are shown through code examples and the results are often depicted in figures; these studies demonstrate the reliability of the examples and lmomco by demonstrating consistency with results with the literature. Topical case studies of regional distributional analysis of hydrometeorologic data are shown to guide readers.The monograph presents new developments by the author or following prior literature results that include censored PWMs and L-moments by censoring fraction, threshold, and indicator; the Cauchy, Kumaraswamy, Rayleigh, Rice, trimmed GPA, and RC-GPA distributions; L-comoments in context of copulas; and theoretical (non-sample) computation of L-moments.The monograph provides more than 245 code examples, about 515 numbered equations, a thorough topical index, and an index of about 425 R functions used in the examples. Approximately 100 figures are provided and virtually all of the figures can be created from the code in the text.

Introduction To Data Science

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Author : Rafael A. Irizarry
language : en
Publisher: CRC Press
Release Date : 2019-11-12

Introduction To Data Science written by Rafael A. Irizarry and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-12 with Mathematics categories.

Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. A complete solutions manual is available to registered instructors who require the text for a course.

Bayesian Data Analysis Third Edition

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Author : Andrew Gelman
language : en
Publisher: CRC Press
Release Date : 2013-11-01

Bayesian Data Analysis Third Edition written by Andrew Gelman and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-01 with Mathematics categories.

Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

Data Analysis With R Second Edition

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Author : Tony Fischetti
language : en
Publisher: Packt Publishing
Release Date : 2018-03-28

Data Analysis With R Second Edition written by Tony Fischetti and has been published by Packt Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-28 with Computers categories.

Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone's career as a data analyst.

Geostatistical Functional Data Analysis

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Author : Jorge Mateu
language : en
Publisher: John Wiley & Sons
Release Date : 2021-12-13

Geostatistical Functional Data Analysis written by Jorge Mateu and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-13 with Social Science categories.

Geostatistical Functional Data Analysis Explore the intersection between geostatistics and functional data analysis with this insightful new reference Geostatistical Functional Data Analysis presents a unified approach to modelling functional data when spatial and spatio-temporal correlations are present. The Editors link together the wide research areas of geostatistics and functional data analysis to provide the reader with a new area called geostatistical functional data analysis that will bring new insights and new open questions to researchers coming from both scientific fields. This book provides a complete and up-to-date account to deal with functional data that is spatially correlated, but also includes the most innovative developments in different open avenues in this field. Containing contributions from leading experts in the field, this practical guide provides readers with the necessary tools to employ and adapt classic statistical techniques to handle spatial regression. The book also includes: A thorough introduction to the spatial kriging methodology when working with functions A detailed exposition of more classical statistical techniques adapted to the functional case and extended to handle spatial correlations Practical discussions of ANOVA, regression, and clustering methods to explore spatial correlation in a collection of curves sampled in a region In-depth explorations of the similarities and differences between spatio-temporal data analysis and functional data analysis Aimed at mathematicians, statisticians, postgraduate students, and researchers involved in the analysis of functional and spatial data, Geostatistical Functional Data Analysis will also prove to be a powerful addition to the libraries of geoscientists, environmental scientists, and economists seeking insightful new knowledge and questions at the interface of geostatistics and functional data analysis.