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Multivariate Time Series Analysis: With R and Financial Applications
Multivariate Time Series Analysis: With R and Financial Applications

An accessible guide to the multivariate time series tools used in numerous real-world applications

Multivariate Time Series Analysis: With R and Financial Applications is the much anticipated sequel coming from one of the most influential and prominent experts on the topic of time series. Through a...

Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics)
Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics)

Time Series Analysis and Its Applications presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using nontrivial data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception...

Statistics for Censored Environmental Data Using Minitab and R
Statistics for Censored Environmental Data Using Minitab and R

Praise for the First Edition

" . . . an excellent addition to an upper-level undergraduate course on environmental statistics, and . . . a 'must-have' desk reference for environmental practitioners dealing with censored datasets."
—Vadose Zone Journal

Statistical Methods for...

Chemometrics with R: Multivariate Data Analysis in the Natural Sciences and Life Sciences (Use R!)
Chemometrics with R: Multivariate Data Analysis in the Natural Sciences and Life Sciences (Use R!)

This accessible primer on multivariate statistics in the life sciences gives a full description of the general data analysis paradigm, from exploratory analysis to modeling to validation; covers other aspects of chemometrics; and supplies the relevant R code.

...
S+Functional Data Analysis User's Guide
S+Functional Data Analysis User's Guide
S+Functional Data Analysis is the first commercial object oriented package for exploring, modeling, and analyzing functional data. Functional data analysis (FDA) handles longitudinal data and treats each observation as a function of time (or other variable). The functions are related. The goal is to analyze a sample of functions instead of a...
Approximation Methods for Polynomial Optimization: Models, Algorithms, and Applications (SpringerBriefs in Optimization)
Approximation Methods for Polynomial Optimization: Models, Algorithms, and Applications (SpringerBriefs in Optimization)
Polynomial optimization, as its name suggests, is used to optimize a generic multivariate polynomial function, subject to some suitable polynomial equality and/or inequality constraints. Such problem formulation dates back to the nineteenth century when the relationship between nonnegative polynomials and sum of squares (SOS) was...
Data Analysis with IBM SPSS Statistics: Implementing data modeling, descriptive statistics and ANOVA
Data Analysis with IBM SPSS Statistics: Implementing data modeling, descriptive statistics and ANOVA

Master data management & analysis techniques with IBM SPSS Statistics 24

About This Book

  • Leverage the power of IBM SPSS Statistics to perform efficient statistical analysis of your data
  • Choose the right statistical technique to analyze different types of data and build efficient...
Random Processes by Example
Random Processes by Example

This volume first introduces the mathematical tools necessary for understanding and working with a broad class of applied stochastic models. The toolbox includes Gaussian processes, independently scattered measures such as Gaussian white noise and Poisson random measures, stochastic integrals, compound Poisson, infinitely divisible and stable...

Exploratory Multivariate Analysis by Example Using R (Chapman & Hall/CRC Computer Science & Data Analysis)
Exploratory Multivariate Analysis by Example Using R (Chapman & Hall/CRC Computer Science & Data Analysis)

Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis...

An R and S-Plus® Companion to Multivariate Analysis (Springer Texts in Statistics)
An R and S-Plus® Companion to Multivariate Analysis (Springer Texts in Statistics)

Applied statisticians often need to perform analyses of multivariate data; for these they will typically use one of the statistical software packages, S-Plus or R. This book sets out how to use these packages for these analyses in a concise and easy-to-use way, and will save users having to buy two books for the job. The author is...

Data Analysis Using SAS Enterprise Guide
Data Analysis Using SAS Enterprise Guide

The present book, Data Analysis Using SAS Enterprise Guide, provides readers with an overview of Enterprise Guide, the newest point-and-click interface from SAS. SAS Enterprise Guide is a graphical user (point-and-click) interface to the main SAS application, having relatively recently replaced the Analyst interface, which itself had replaced...

Meta-Learning in Computational Intelligence (Studies in Computational Intelligence)
Meta-Learning in Computational Intelligence (Studies in Computational Intelligence)

In the early days of pattern recognition and statistical data analysis life was rather simple: datasets were relatively small, collected from well-designed experiments, analyzed using a few methods that had good theoretical background. Explosive growth of the use of computers led to the creation of huge amounts of data of all kinds,...

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