Introduction to Time Series Modeling with Applications in R
portes grátis
Introduction to Time Series Modeling with Applications in R
with Applications in R
Kitagawa, Genshiro
Taylor & Francis Ltd
08/2022
340
Mole
Inglês
9780367494247
15 a 20 dias
476
Descrição não disponível.
1. Introduction and Preparatory Analysis. 2. The Covariance Function. 3. The Power Spectrum and the Periodogram. 4. Statistical Modeling. 5. The Least Squares Method. 6. Analysis of Time Series Using ARMA Models. 7. Estimation of an AR Model. 8. The Locally Stationary AR Model. 9. Analysis of Time Series with a State-Space Model. 10. Estimation of the ARMA Model. 11. Estimation of Trends. 12. The Seasonal Adjustment Model. 13. Time-Varying Coefficient AR Model. 14. Non-Gaussian State-Space Model. 15. The Sequential Monte Carlo Filter. 17. Simulations.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
ARMA Model;AR Model;time series modeling;State Space Model;state-space model;Time Series Models;recursive filtering;Autocovariance Function;smoothing methods;Nonstationary Time Series;Kalman filter;Multivariate Time Series;non-Gaussian filter;non-Gaussian State Space Model;sequential Monte Carlo filter;Sample Autocorrelation Function;entropy maximization principle;Householder Transformation;parameter estimation;Sample Autocovariance Functions;least squares method;MA Coefficient;maximum likelihood method;Rudder Angle;recursive estimation;Nonlinear State Space Model;simulation methods;Variance Covariance Matrix;stationary time series models;Maximum Temperature Data;Cross-covariance Function;nonstationary time series models;Power Spectrum;locally stationary AR model;AR Coefficient;trend model;Autocorrelation Function;seasonal adjustment model;Stationary Time Series;time-varying coefficient AR model;Trend Component;Cauchy Distribution;ARMA Order
1. Introduction and Preparatory Analysis. 2. The Covariance Function. 3. The Power Spectrum and the Periodogram. 4. Statistical Modeling. 5. The Least Squares Method. 6. Analysis of Time Series Using ARMA Models. 7. Estimation of an AR Model. 8. The Locally Stationary AR Model. 9. Analysis of Time Series with a State-Space Model. 10. Estimation of the ARMA Model. 11. Estimation of Trends. 12. The Seasonal Adjustment Model. 13. Time-Varying Coefficient AR Model. 14. Non-Gaussian State-Space Model. 15. The Sequential Monte Carlo Filter. 17. Simulations.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
ARMA Model;AR Model;time series modeling;State Space Model;state-space model;Time Series Models;recursive filtering;Autocovariance Function;smoothing methods;Nonstationary Time Series;Kalman filter;Multivariate Time Series;non-Gaussian filter;non-Gaussian State Space Model;sequential Monte Carlo filter;Sample Autocorrelation Function;entropy maximization principle;Householder Transformation;parameter estimation;Sample Autocovariance Functions;least squares method;MA Coefficient;maximum likelihood method;Rudder Angle;recursive estimation;Nonlinear State Space Model;simulation methods;Variance Covariance Matrix;stationary time series models;Maximum Temperature Data;Cross-covariance Function;nonstationary time series models;Power Spectrum;locally stationary AR model;AR Coefficient;trend model;Autocorrelation Function;seasonal adjustment model;Stationary Time Series;time-varying coefficient AR model;Trend Component;Cauchy Distribution;ARMA Order
