Описание: Bayesian inference uses probability distributions and Bayes` theorem to build flexible models. The book uses PyMC3 to abstract all the mathematical and computational details from this process allowing readers to solve a wide range of problems in data science.
Incorporating the latest R packages as well as new case studies and applications, Using R and RStudio for Data Management, Statistical Analysis, and Graphics, Second Edition covers the aspects of R most often used by statistical analysts. New users of R will find the book's simple approach easy to understand while more sophisticated users will appreciate the invaluable source of task-oriented information.
New to the Second Edition
The use of RStudio, which increases the productivity of R users and helps users avoid error-prone cut-and-paste workflows
New chapter of case studies illustrating examples of useful data management tasks, reading complex files, making and annotating maps, "scraping" data from the web, mining text files, and generating dynamic graphics
New chapter on special topics that describes key features, such as processing by group, and explores important areas of statistics, including Bayesian methods, propensity scores, and bootstrapping
New chapter on simulation that includes examples of data generated from complex models and distributions
A detailed discussion of the philosophy and use of the knitr and markdown packages for R
New packages that extend the functionality of R and facilitate sophisticated analyses
Reorganized and enhanced chapters on data input and output, data management, statistical and mathematical functions, programming, high-level graphics plots, and the customization of plots
Easily Find Your Desired Task
Conveniently organized by short, clear descriptive entries, this edition continues to show users how to easily perform an analytical task in R. Users can quickly find and implement the material they need through the extensive indexing, cross-referencing, and worked examples in the text. Datasets and code are available for download on a supplementary website.
Автор: Pardo Scott Название: Empirical Modeling and Data Analysis for Engineers and Appli ISBN: 3319327674 ISBN-13(EAN): 9783319327679 Издательство: Springer Рейтинг: Цена: 9362.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This textbook teaches advanced undergraduate and first-year graduate students in Engineering and Applied Sciences to gather and analyze empirical observations (data) in order to aid in making design decisions.
While science is about discovery, the primary paradigm of engineering and 'applied science' is design. Scientists are in the discovery business and want, in general, to understand the natural world rather than to alter it. In contrast, engineers and applied scientists design products, processes, and solutions to problems.
That said, statistics, as a discipline, is mostly oriented toward the discovery paradigm. Young engineers come out of their degree programs having taken courses such as 'Statistics for Engineers and Scientists' without any clear idea as to how they can use statistical methods to help them design products or processes. Many seem to think that statistics is only useful for demonstrating that a device or process actually does what it was designed to do. Statistics courses emphasize creating predictive or classification models - predicting nature or classifying individuals, and statistics is often used to prove or disprove phenomena as opposed to aiding in the design of a product or process. In industry however, Chemical Engineers use designed experiments to optimize petroleum extraction; Manufacturing Engineers use experimental data to optimize machine operation; Industrial Engineers might use data to determine the optimal number of operators required in a manual assembly process. This text teaches engineering and applied science students to incorporate empirical investigation into such design processes.
Much of the discussion in this book is about models, not whether the models truly represent reality but whether they adequately represent reality with respect to the problems at hand; many ideas focus on how to gather data in the most efficient way possible to construct adequate models.Includes chapters on subjects not often seen together in a single text (e.g., measurement systems, mixture experiments, logistic regression, Taguchi methods, simulation)Techniques and concepts introduced present a wide variety of design situations familiar to engineers and applied scientists and inspire incorporation of experimentation and empirical investigation into the design process.Software is integrally linked to statistical analyses with fully worked examples in each chapter; fully worked using several packages: SAS, R, JMP, Minitab, and MS Excel - also including discussion questions at the end of each chapter.
The fundamental learning objective of this textbook is for the reader to understand how experimental data can be used to make design decisions and to be familiar with the most common types of experimental designs and analysis methods.
Автор: Jamie D. Riggs Название: Handbook for Applied Modeling: Non-Gaussian and Correlated Data ISBN: 1316601056 ISBN-13(EAN): 9781316601051 Издательство: Cambridge Academ Рейтинг: Цена: 6019.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Designed for the applied practitioner, this book is a compact, entry-level guide to modeling and analyzing data that fail idealized assumptions. It explains and demonstrates core techniques, common pitfalls and data issues, and interpretation of model results, all with a focus on application, utility, and real-life data.
Автор: Pankaj Choudhary; Chaitra H. Nagaraja; Hon Keung T Название: Ordered Data Analysis, Modeling and Health Research Methods ISBN: 3319254316 ISBN-13(EAN): 9783319254319 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume presents an eclectic mix of original research articles in areas covering the analysis of ordered data, stochastic modeling and biostatistics. These areas were featured in a conference held at the University of Texas at Dallas from March 7 to 9, 2014 in honor of Professor H. N. Nagaraja’s 60th birthday and his distinguished contributions to statistics. The articles were written by leading experts who were invited to contribute to the volume from among the conference participants. The volume is intended for all researchers with an interest in order statistics, distribution theory, analysis of censored data, stochastic modeling, time series analysis, and statistical methods for the health sciences, including statistical genetics.
Introduces the latest developments in forecasting in advanced quantitative data analysis
This book presents advanced univariate multiple regressions, which can directly be used to forecast their dependent variables, evaluate their in-sample forecast values, and compute forecast values beyond the sample period. Various alternative multiple regressions models are presented based on a single time series, bivariate, and triple time-series, which are developed by taking into account specific growth patterns of each dependent variables, starting with the simplest model up to the most advanced model. Graphs of the observed scores and the forecast evaluation of each of the models are offered to show the worst and the best forecast models among each set of the models of a specific independent variable.
Advanced Time Series Data Analysis: Forecasting Using EViews provides readers with a number of modern, advanced forecast models not featured in any other book. They include various interaction models, models with alternative trends (including the models with heterogeneous trends), and complete heterogeneous models for monthly time series, quarterly time series, and annually time series. Each of the models can be applied by all quantitative researchers.
Presents models that are all classroom tested
Contains real-life data samples
Contains over 350 equation specifications of various time series models
Contains over 200 illustrative examples with special notes and comments
Applicable for time series data of all quantitative studies
Advanced Time Series Data Analysis: Forecasting Using EViews will appeal to researchers and practitioners in forecasting models, as well as those studying quantitative data analysis. It is suitable for those wishing to obtain a better knowledge and understanding on forecasting, specifically the uncertainty of forecast values.
Описание: Focusing on the analysis and modeling of thermal systems in engineering, this volume covers the mathematics, data interpretation and decision analysis required for researchers and engineers to scrutinize their methodologies and arrive at robust conclusions.
Автор: Carlo Bertoluzza; Maria A. Gil; Dan A. Ralescu Название: Statistical Modeling, Analysis and Management of Fuzzy Data ISBN: 3790825018 ISBN-13(EAN): 9783790825015 Издательство: Springer Рейтинг: Цена: 23058.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The contributions in this book state the complementary rather than competitive relationship between Probability and Fuzzy Set Theory and allow solutions to real life problems with suitable combinations of both theories.
Автор: Donatella Vicari; Akinori Okada; Giancarlo Ragozin Название: Analysis and Modeling of Complex Data in Behavioral and Social Sciences ISBN: 3319066919 ISBN-13(EAN): 9783319066912 Издательство: Springer Рейтинг: Цена: 11179.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Time-frequency Filtering for Seismic Waves Clustering.- Modeling Longitudinal Data by Latent Markov Models with Application to Educational and Psychological Measurement.- Clustering of Stratified Aggregated Data using the Aggregate Association Index: Analysis of New Zealand Voter Turnout (1893 - 1919).- Estimating a Rasch Model via Fuzzy Empirical Probability Functions.- Scale Reliability Evaluation for a-priori Clustered Data.- An Evaluation of Performance of Territorial Services Center (TSC) by a Nonparametric Combination Ranking Method. The IQuEL Italian Project.- A New Index for the Comparison of Different Measurement Scales.- Asymmetries in Organizational Structures.- A Generalized Additive Model for Binary Rare Events Data: an Application to Credit Defaults.- The Meaning of forma in Thomas Aquinas. Hierarchical Clustering from the Index Thomisticus Treebank.- The Estimation of the Parameters in Multi-Criteria Classification Problem: the Case of the Electre Tri Method.- Dynamic Clustering of Financial Assets.- A Comparison of metrics for the Assessment of Relational Similarities in Affiliation Networks.- Influence Diagnostics for Meta-Analysis of Individual Patient Data using Generalized Linear Mixed Models.- Social Networks as Symbolic Data.- Statistical Assessment for Risk Prediction of Endoleak Formation after TEVAR Based on Linear Discriminant Analysis.- Fuzzy c-means for Web Mining: The Italian Tourist Forum Case.- On Joint Dimension Reduction and Clustering of Categorical Data.- A SVM Applied Text Categorization of Academia-Industry Collaborative Research and Development Documents on the Web.- Dynamic Customer Satisfaction and Measure of Trajectories: a Banking Case.- The Analysis of Partnership Networks in Social Planning Processes.- Evaluating the Effect of New Brand by Asymmetric Multidimensional Scaling.- Statistical Characterization of the Virtual Water Trade Network.- A Pre-Specified Blockmodeling to Analyze Structural Dynamics in Innovation Networks.- The RCI as a Measure of Monotonic Dependence.- A Value Added Approach in Upper Secondary Schools of Lombardy by OECD-PISA 2009 Data.- Algorithmic-Type Imputation Techniques with Different Data Structures: Alternative Approaches in Comparison.- Changes in Japanese EFL Learners' Proficiency: An Application of Latent Rank Theory.- Robustness and Stability Analysis of Factor PD-Clustering on Large Social Data Sets.- A Box-plot and Outliers Detection Proposal for Histogram Data: New Tools for Data Stream Analysis.- Assessing Cooperation in Open Systems: an Empirical Test in Healthcare.
Автор: H. Bozdogan; Arjun K. Gupta Название: Multivariate Statistical Modeling and Data Analysis ISBN: 9401082642 ISBN-13(EAN): 9789401082648 Издательство: Springer Рейтинг: Цена: 20537.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The areas covered included topics in the analysis of repeated measurements, cluster analysis, discriminant analysis, canonical cor- relations, distribution theory and testing, bivariate densi ty estimation, factor analysis, principle component analysis, multidimensional scaling, multivariate linear models, nonparametric regression, etc.
Описание: The volume is intended for all researchers with an interest in order statistics, distribution theory, analysis of censored data, stochastic modeling, time series analysis, and statistical methods for the health sciences, including statistical genetics.
Distribution-free resampling methods--permutation tests, decision trees, and the bootstrap--are used today in virtually every research area. A Practitioner's Guide to Resampling for Data Analysis, Data Mining, and Modeling explains how to use the bootstrap to estimate the precision of sample-based estimates and to determine sample size, data permutations to test hypotheses, and the readily-interpreted decision tree to replace arcane regression methods.
Highlights
Each chapter contains dozens of thought provoking questions, along with applicable R and Stata code
Methods are illustrated with examples from agriculture, audits, bird migration, clinical trials, epidemiology, image processing, immunology, medicine, microarrays and gene selection
Lists of commercially available software for the bootstrap, decision trees, and permutation tests are incorporated in the text
Access to APL, MATLAB, and SC code for many of the routines is provided on the author's website
The text covers estimation, two-sample and k-sample univariate, and multivariate comparisons of means and variances, sample size determination, categorical data, multiple hypotheses, and model building
Statistics practitioners will find the methods described in the text easy to learn and to apply in a broad range of subject areas from A for Accounting, Agriculture, Anthropology, Aquatic science, Archaeology, Astronomy, and Atmospheric science to V for Virology and Vocational Guidance, and Z for Zoology.
Practitioners and research workers and in the biomedical, engineering and social sciences, as well as advanced students in biology, business, dentistry, medicine, psychology, public health, sociology, and statistics will find an easily-grasped guide to estimation, testing hypotheses and model building.
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