LEADER 04264nam 22007095 450 001 9910983047703321 005 20251001133938.0 010 $a9783031705847 010 $a303170584X 024 7 $a10.1007/978-3-031-70584-7 035 $a(CKB)37391037500041 035 $a(MiAaPQ)EBC31890808 035 $a(Au-PeEL)EBL31890808 035 $a(DE-He213)978-3-031-70584-7 035 $a(OCoLC)1499719792 035 $a(EXLCZ)9937391037500041 100 $a20250128d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTime Series Analysis and Its Applications $eWith R Examples /$fby Robert H. Shumway, David S. Stoffer 205 $a5th ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (822 pages) 225 1 $aSpringer Texts in Statistics,$x2197-4136 311 08$a9783031705830 311 08$a3031705831 327 $a1. Characteristics of Time Series -- 2. Time Series Regression and Exploratory Data Analysis -- 3. ARIMA Models -- 4. Spectral Analysis and Filtering -- 5. Additional Time Domain Topics -- 6. State-Space Models -- 7. Statistical Methods in the Frequency Domain -- 8. Appendix A: Large Sample Theory -- Appendix B: Time Domain Theory -- Appendix C: Spectral Domain Theory -- Appendix R: R Supplement. 330 $aThis 5th edition of this popular graduate textbook presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. It includes numerous examples using nontrivial data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and monitoring a nuclear test ban treaty. The R package ?astsa? has had major updates and the text will reflect those updates. In general, the graphics have been improved. New topics include random number generation, modeling and fitting predator-prey interactions, more emphasis on structural models, testing for linearity, discussion of EM algorithm is more extensive, Bayesian analysis of state space models and MCMC is more extensive (including new scripts in astsa), particle methods are introduced, stochastic volatility coverage is expanded, changepoint detection is introduced (new topic). The book is designed as a textbook for graduate level students in the physical, biological, and social sciences and as a graduate level text in statistics. Some parts may also serve as an undergraduate introductory course. Theory and methodology are separated to allow presentations on different levels. In addition to coverage of classical methods of time series regression, ARIMA models, spectral analysis and state-space models, the text includes modern developments including categorical time series analysis, multivariate spectral methods, long memory series, nonlinear models, resampling techniques, GARCH models, ARMAX models, stochastic volatility, and Markov chain Monte Carlo integration methods. This edition includes R code for each numerical example. 410 0$aSpringer Texts in Statistics,$x2197-4136 606 $aStatistics 606 $aBiometry 606 $aStatistical Theory and Methods 606 $aBiostatistics 606 $aSeries temporales$2UAMSUB 606 $aAnálisis temporal$2UAMSUB 606 $aEstadística$2thub 606 $aBiometria$2thub 606 $aAnàlisi de sèries temporals$2thub 608 $aLlibres electrònics$2thub 615 0$aStatistics. 615 0$aBiometry. 615 14$aStatistical Theory and Methods. 615 24$aBiostatistics. 615 7$aSeries temporales 615 7$aAnálisis temporal 615 7$aEstadística 615 7$aBiometria 615 7$aAnàlisi de sèries temporals 676 $a519.55 686 $aB0250$2Inspec 700 $aShumway$b Robert H$0249361 702 $aStoffer$b David S. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910983047703321 996 $aTime series analysis and its applications$9157341 997 $aUNINA