rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Basic data analysis for time series with R
  name:
    name part: Derryberry, DeWayne R.
    role:
      role term: creator
    role:
      role term: author.
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: nju
    date issued: 2014
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource
  abstract: "This book emphasizes the collaborative analysis of data that is used to collect increments of time or space. Written at a readily accessible level, but with the necessary theory in mind, the author uses frequency- and time-domain and trigonometric regression as themes throughout the book. The content includes modern topics such as wavelets, Fourier series, and Akaike's Information Criterion (AIC), which is not typical of current-day "classics." Applications to a variety of scientific fields are showcased. Exercise sets are well crafted with the express intent of supporting pedagogy through recognition and repetition. R subroutines are employed as the software and graphics tool of choice. Brevity is a key component to the retention of the subject matter. The book presumes knowledge of linear algebra, probability, data analysis, and basic computer programming"--
  abstract: "This book emphasizes the collaborative analysis of data that is used to collect increments of time or space. Written at a readily accessible level, but with the necessary theory in mind, the author uses frequency- and time-domain and trigonometric regression as themes throughout the book"--
  table of contents: Machine generated contents note: Part I -- Basic correlation structures Chapter 0 -- R basics 0.1 Getting started 0.2 Special R conventions 0.3 Common structures 0.4 Common functions 0.5 Time series functions 0.6 Importing data Chapter 1 -- Review of regression and more about R 1.1 Goals of this chapter 1.2 The simple(st) regression model 1.3 Simulating the data from a model and estimating the model parameters in R 1.4 Basic inference for the model 1.5 Residuals analysis -- What can go wrong ... 1.6 Matrix manipulation in R Chapter 2 -- The modeling approach taken in this book and some examples of typical serially correlated data 2.1 Signal and noise 2.2 Time series data 2.3 Simple regression in the framework 2.4 Real data and simulated data 2.5 The diversity of time series data 2.6 Getting data into R Chapter 3 -- Some comments on assumptions 3.1 Introduction 3.2 The normality assumption 3.3 Equal variance 3.4 Independence 3.5 Power of logarithmic transformations illustrated 3.6 Summary Chapter 4 -- The autocorrelation function and AR(1), AR(2) models 4.1 Standard models -- What are the alternatives to white noise? 4.2 Autocovariance and autocorrelation 4.3 The acf() function in R 4.4 The first alternative to white noise: Autoregressive errors -- AR(1), AR(2) Chapter 5 -- The moving average models MA(1) and MA(2) 5.1 The moving average model 5.2 The autocorrelation for MA(1) models 5.3 A duality between MA(l) and AR(m) models 5.4 The autocorrelation for MA(2) models 5.5 Simulated examples of the MA(1) model 5.5 Simulated examples of the MA(2) model 5.6 AR(m) and MA(l) model acf() plots Part II -- Analysis of periodic data and model selection Chapter 6 -- Review of transcendental functions and complex numbers 6.1 Background 6.2 Complex arithmetic 6.3 Some important series 6.4 Useful facts about periodic transcendental functions Chapter 7 -- The power spectrum and the periodogram 7.1 Introduction 7.2 A definition and a simplified form for p(f) 7.3 Inverting p(f) to r…
  note: DeWayne R. Derryberry, Department of Mathematics and Statistics, Idaho State University, Voise, ID.
  note: Includes bibliographical references and index.
  subject:
    topic: Time-series analysis
    topic: Data processing
  subject:
    topic: R (Computer program language)
  subject:
    topic: MATHEMATICS
    topic: Probability & Statistics
    topic: General
  subject:
    topic: R (Computer program language)
  subject:
    topic: Time-series analysis
    topic: Data processing
  classification: QA280
  classification: 001.4/2202855133
  classification: MAT029000
  related item:
    title info:
      title: Basic data analysis for time series with R
    name:
      name part: Derryberry, DeWayne R., author.
    origin info:
      publisher: Hoboken, New Jersey : John Wiley & Sons, Inc., [2014]
    identifier: (DLC) 2014007300
  identifier: 9781118593370
  identifier: 1118593375
  identifier: 9781118593363
  identifier: 1118593367
  identifier: 9781118593233
  identifier: 1118593235
  identifier: 1118422546
  identifier: 9781118422540
  identifier: 9781322007595
  identifier: 1322007594
  identifier: 2014007300
  identifier: CL0500000567 Safari Books Online
  identifier: https://doi.org/10.1002/9781118593233
  record info:
    record content source: DLC
    record creation date: 140417
    record change date: 20170124070335.2
    record identifier: ocn877564736
    language of cataloging:
      language term: eng