Time Series Analysis: Methods and Applications

- Volume 30

Forfatter: info mangler
Bog
  • Format
  • Bog, hardback
  • Engelsk

Beskrivelse

The field of statistics not only affects all areas of scientific activity, but also many other matters such as public policy. It is branching rapidly into so many different subjects that a series of handbooks is the only way of comprehensively presenting the various aspects of statistical methodology, applications, and recent developments.The Handbook of Statistics is a series of self-contained reference books. Each volume is devoted to a particular topic in statistics, with Volume 30 dealing with time series. The series is addressed to the entire community of statisticians and scientists in various disciplines who use statistical methodology in their work. At the same time, special emphasis is placed on applications-oriented techniques, with the applied statistician in mind as the primary audience.

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Detaljer
  • SprogEngelsk
  • Sidetal776
  • Udgivelsesdato18-05-2012
  • ISBN139780444538581
  • Forlag North-Holland
  • FormatHardback
Størrelse og vægt
  • Vægt1370 g
  • coffee cup img
    10 cm
    book img
    15,2 cm
    22,9 cm

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    Var Discrimination Molecular biology Test Stochastic processes Analysis of variance Chaos Stationary processes Spectral analysis Causality Quantile regression Reproducible research Poisson distribution Résolution Frequency Estimation Filtering COGARCH process Linear Prediction Brain connectivity Innovation Approach Nonlinear time series Literature Review Nonparametric estimation Bootstrap methods Extremes Missing observations Spm Autoregressive process Prediction High dimensional inference Markov Chains Bootstrap Generalized Ornstein-Uhlenbeck process Discrete fourier transforms State space modeling Principal component analysis GARCH Local Likelihood Statistical computing Subsampling Time series Long-range Dependence Stochastic volatility Principal components 60H10 Fourier transform Spatial processes Specification Tests Spatial Functional data Lévy process Regularization Karhunen Loeve Expansion Nonparametric tests Frequency Domain Discrete Fourier transform Discrete Wavelet Transform Nonlinearity Regime-switching Coherence Resampling Conditional Distribution (auto)regression Nonstationarity Stationarity 62M10 60G70 91G70 asymptotic consistency bispectrum Auto-regressive Models Autocorrelation categorical time series AR-sieve coherency envelope cluster and multicore computing Biomedical Time Series Criteria Pollutants Empirical orthogonal functions empirical spectral process derivative process characteristic scales Daubechies wavelet filters fMRI Data Gaussian maximum likelihood hidden Markov Heteroscedasticity and Autocorrelation Consistent Heteroscedastic models higher order moments initial value sensitivity Lèvy-driven CARMA process intrinsically stationary time series linear and nonlinear Kriging predictors locally stationary time series linearity testing Nonstationary Time Series nullrecurrent Markov chain gaussianity testing Multiple Linear Regression Ozone concentration modeling Rank and M-estimation quantile autoregression (QAR) power-law processes second order correctness SAST and CAST models sinusoidal regression space time autoregressive models robust estimator linear and nonlinear time series spectral envelope smooth localized complex exponentials Stationary process locally stationary process spectral matrix Spatial Interpolation shape curve time varying autoregressive process spatio-temporal processes Spectral Density Estimation Monitoring compliance Link Function multiscale contamination Surrogate Data periodogram Pickands' grid nonlinear cointegrating regression nonlinear integrated process NN-ARX model quantitative programming environment spatial temporal correlations stochastic coefficients time varying spectral density value-at-risk (VaR) spatial stochastic process time series graphics time varying parameter Temporal Processes

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