Complex Survey Data Analysis with SAS, Lewis Taylor H.
Автор: Lewis Название: Complex Survey Data Analysis with SAS ISBN: 1498776779 ISBN-13(EAN): 9781498776776 Издательство: Taylor&Francis Рейтинг: Цена: 14545.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Complex Survey Data Analysis with SAS (R) is an invaluable resource for applied researchers analyzing data generated from a sample design involving any combination of stratification, clustering, unequal weights, or finite population correction factors.
Автор: Heeringa Название: Applied Survey Data Analysis, Second Edition ISBN: 1498761607 ISBN-13(EAN): 9781498761604 Издательство: Taylor&Francis Рейтинг: Цена: 13779.00 р. Наличие на складе: Нет в наличии.
Описание: This book provides an 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 examples the analysis of survey data.
Автор: Michael C. Whitlock, Dolph Schluter Название: The Analysis of Biological Data ISBN: 1319325343 ISBN-13(EAN): 9781319325343 Издательство: Macmillan Learning Рейтинг: Цена: 14452.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The evolution of a classicThe new 12th edition of Introduction to Genetic Analysis takes this cornerstone textbook to the next level.
Автор: Guerrero, Hector Название: Excel data analysis ISBN: 3030012786 ISBN-13(EAN): 9783030012786 Издательство: Springer Рейтинг: Цена: 12196.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book offers a comprehensive and readable introduction to modern business and data analytics.
Автор: Ahmed Название: Big and Complex Data Analysis ISBN: 3319415727 ISBN-13(EAN): 9783319415727 Издательство: Springer Рейтинг: Цена: 13415.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data.
Автор: Wilhelm Название: Analysis of Large and Complex Data ISBN: 3319252240 ISBN-13(EAN): 9783319252247 Издательство: Springer Рейтинг: Цена: 15855.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book offers a snapshot of the state-of-the-art in classification at the interface between statistics, computer science and application fields. The contributions span a broad spectrum, from theoretical developments to practical applications; they all share a strong computational component. The topics addressed are from the following fields: Statistics and Data Analysis; Machine Learning and Knowledge Discovery; Data Analysis in Marketing; Data Analysis in Finance and Economics; Data Analysis in Medicine and the Life Sciences; Data Analysis in the Social, Behavioural, and Health Care Sciences; Data Analysis in Interdisciplinary Domains; Classification and Subject Indexing in Library and Information Science. The book presents selected papers from the Second European Conference on Data Analysis, held at Jacobs University Bremen in July 2014. This conference unites diverse researchers in the pursuit of a common topic, creating truly unique synergies in the process.
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: 9756.00 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: Pierre Duchesne; Bruno R?millard Название: Statistical Modeling and Analysis for Complex Data Problems ISBN: 144193751X ISBN-13(EAN): 9781441937513 Издательство: Springer Рейтинг: Цена: 14635.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Statistical Modeling and Analysis for Complex Data Problems treats some of today's more complex problems and it reflects some of the important research directions in the field. Twenty-nine authors - largely from Montreal's GERAD Multi-University Research Center and who work in areas of theoretical statistics, applied statistics, probability theory, and stochastic processes - present survey chapters on various theoretical and applied problems of importance and interest to researchers and students across a number of academic domains.
Автор: Francesca Greselin; Laura Deldossi; Luca Bagnato; Название: Statistical Learning of Complex Data ISBN: 3030211398 ISBN-13(EAN): 9783030211394 Издательство: Springer Рейтинг: Цена: 14635.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book of peer-reviewed contributions presents the latest findings in classification, statistical learning, data analysis and related areas, including supervised and unsupervised classification, clustering, statistical analysis of mixed-type data, big data analysis, statistical modeling, graphical models and social networks. It covers both methodological aspects as well as applications to a wide range of fields such as economics, architecture, medicine, data management, consumer behavior and the gender gap. In addition, it describes the basic features of the software behind the data analysis results, and provides links to the corresponding codes and data sets where necessary. This book is intended for researchers and practitioners who are interested in the latest developments and applications in the field of data analysis and classification. It gathers selected and peer-reviewed contributions presented at the 11th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2017), held in Milan, Italy, on September 13–15, 2017.
Описание: This book focuses on a central question in the field of complex systems: Given a fluctuating (in time or space), uni- or multi-variant sequentially measured set of experimental data (even noisy data), how should one analyse non-parametrically the data, assess underlying trends, uncover characteristics of the fluctuations (including diffusion and jump contributions), and construct a stochastic evolution equation?Here, the term 'non-parametrically' exemplifies that all the functions and parameters of the constructed stochastic evolution equation can be determined directly from the measured data.The book provides an overview of methods that have been developed for the analysis of fluctuating time series and of spatially disordered structures. Thanks to its feasibility and simplicity, it has been successfully applied to fluctuating time series and spatially disordered structures of complex systems studied in scientific fields such as physics, astrophysics, meteorology, earth science, engineering, finance, medicine and the neurosciences, and has led to a number of important results.The book also includes the numerical and analytical approaches to the analyses of complex time series that are most common in the physical and natural sciences. Further, it is self-contained and readily accessible to students, scientists, and researchers who are familiar with traditional methods of mathematics, such as ordinary, and partial differential equations.The codes for analysing continuous time series are available in an R package developed by the research group Turbulence, Wind energy and Stochastic (TWiSt) at the Carl von Ossietzky University of Oldenburg under the supervision of Prof. Dr. Joachim Peinke. This package makes it possible to extract the (stochastic) evolution equation underlying a set of data or measurements.
Автор: Murtagh, Fionn Название: Data science foundations ISBN: 0367657759 ISBN-13(EAN): 9780367657758 Издательство: Taylor&Francis Рейтинг: Цена: 7961.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is designed to provide a new framework for Data Science, based on a solid foundation in mathematics and computational science. It is written in an accessible style, for readers who are engaged with the subject but not necessarily experts in all aspects.
Описание: 1 Introduction.- 2 Introduction to Stochastic Processes.- 3 Kramers-Moyal Expansion and Fokker-Planck Equation.- 4 Continuous Stochastic Process.- 5 The Langevin Equation and Wiener Process.- 6 Stochastic Integration, It o and Stratonovich Calculi.- 7 Equivalence of Langevin and Fokker-Planck Equations.- 8 Examples of Stochastic Calculus.-9 Langevin Dynamics in Higher Dimensions.- 10 Levy Noise Driven Langevin Equation and its Time Series-Based Reconstruction.- 11 Stochastic Processes with Jumps and Non-Vanishing Higher-Order Kramers-Moyal Coefficients.- 12 Jump-Diffusion Processes.- 13 Two-Dimensional (Bivariate) Jump-Diffusion Processes.- 14 Numerical Solution of Stochastic Differential Equations: Diffusion and Jump-Diffusion Processes.- 15 The Friedrich-Peinke Approach to Reconstruction of Dynamical Equation for Time Series: Complexity in View of Stochastic Processes.- 16 How To Set Up Stochastic Equations For Real-World Processes: Markov-Einstein Time Scale.- 17 Reconstruction of Stochastic Dynamical Equations: Exemplary Stationary Diffusion and Jump-Diffusion Processes.- 18 The Kramers-Moyal Coefficients of Non-Stationary Time series in The Presence of Microstructure (Measurement) Noise.- 19 Influence of Finite Time Step in Estimating of the Kramers-Moyal Coefficients.- 20 Distinguishing Diffusive and Jumpy Behaviors in Real-World Time Series.- 21 Reconstruction of Langevin and Jump-Diffusion Dynamics From Empirical Uni- and Bivariate Time Series.- 22 Applications and Outlook.- 23 Epileptic Brain Dynamics.
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