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001441801 001__ 1441801
001441801 003__ OCoLC
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001441801 019__ $$a1294217308$$a1294283917
001441801 020__ $$a9789811681622$$q(electronic bk.)
001441801 020__ $$a9811681627$$q(electronic bk.)
001441801 020__ $$z9789811681615
001441801 020__ $$z9811681619
001441801 0247_ $$a10.1007/978-981-16-8162-2$$2doi
001441801 035__ $$aSP(OCoLC)1294513600
001441801 040__ $$aN$T$$beng$$erda$$epn$$cN$T$$dN$T$$dEBLCP$$dGW5XE$$dYDX$$dOCLCO$$dOCLCF$$dSFB$$dUKAHL$$dOCLCQ
001441801 049__ $$aISEA
001441801 050_4 $$aQA280
001441801 08204 $$a519.55$$223
001441801 1001_ $$aBalakrishna, N.,$$eauthor.
001441801 24510 $$aNon-Gaussian autoregressive-type time series /$$cN. Balakrishna.
001441801 264_1 $$aSingapore :$$bSpringer,$$c2022.
001441801 300__ $$a1 online resource.
001441801 336__ $$atext$$btxt$$2rdacontent
001441801 337__ $$acomputer$$bc$$2rdamedia
001441801 338__ $$aonline resource$$bcr$$2rdacarrier
001441801 504__ $$aIncludes bibliographical references.
001441801 5050_ $$aIntro -- Preface -- Contents -- About the Author -- Acronyms -- 1 Basics of Time Series -- 1.1 Useful Characteristics of Time Series -- 1.2 Linear Time Series Models -- 1.2.1 Autoregressive Models -- 1.2.2 Moving Average Models -- 1.2.3 Autoregressive Moving Average Models -- 1.3 Random Coefficient AR Models -- 1.3.1 Random Lag Autoregressive Model of Order p (RLAR(p)) -- 1.4 Other Non-linear Time Series Models -- 1.5 Non-Gaussian Time Series -- 1.6 Model Specifications -- 1.6.1 Marginal Specific Models -- 1.6.2 Error Specific Models -- 1.6.3 Conditionally Specified Models -- References
001441801 5058_ $$a2 Statistical Inference for Stationary Linear Time Series -- 2.1 Methods of Estimation -- 2.2 Yule-Walker Method of Estimation -- 2.3 Maximum Likelihood Methods -- 2.3.1 ML Method for ARMA Models with Non-Gaussian Innovations -- 2.3.2 MLE for Stationary AR(p) Model -- 2.3.3 Modified Method of Maximum Likelihood Estimation (MMLE) -- 2.3.4 Maximum Probability Estimation -- 2.4 Quasi-Maximum Likelihood Method -- 2.5 Method of Conditional Least Squares -- 2.5.1 Two-Stage Conditional Least Squares Method -- 2.6 Generalized Method of Moments
001441801 5058_ $$a2.7 Godambe Estimating Functions and Quasi-likelihood Methods -- 2.7.1 Estimating Functions for Stochastic Processes -- 2.7.2 Quasi-likelihood Scores Based on Conditional Mean and Variance -- 2.7.3 Asymptotic Theory of Estimating Functions -- 2.8 Other Methods of Estimation -- 2.9 Methods of Model Identification, Diagnosis and Forecasting -- References -- 3 AR Models with Stationary Non-Gaussian Positive Marginals -- 3.1 Constant Coefficient Exponential Autoregressive Models -- 3.1.1 First-Order Exponential Autoregressive Models -- 3.1.2 Higher Order Exponential Autoregressive Models
001441801 5058_ $$a3.1.3 ACF of EAR(p) Processes -- 3.2 Estimation for Stationary Exponential AR Models -- 3.2.1 Estimation in the Presence of Zero-Defects -- 3.2.2 Conditional Least Square Method for EAR(p) Models -- 3.3 Random Coefficient Exponential AR Models -- 3.3.1 Transposed EAR (TEAR) Models -- 3.3.2 New Exponential AR(1) (NEAR(1)) Model -- 3.3.3 Generalized Exponential AR(1) (GEAR(1)) Model -- 3.3.4 New Exponential AR(2) (NEAR(2)) Model -- 3.3.5 NEAR(p) Models -- 3.4 Estimation for Random Coefficient Exponential AR Models -- 3.4.1 Estimation for NEAR(1) Model -- 3.4.2 Estimation for NEAR(2) Model
001441801 5058_ $$a3.4.3 Estimation in NEAR(p) Model -- 3.4.4 Quasi-likelihood Estimates for NEAR(p) Model -- 3.5 Gamma Autoregressive Models -- 3.5.1 Constant Coefficient Gamma AR(1) Models -- 3.5.2 Random Coefficient GAR(1) Models -- 3.5.3 Beta-Gamma ARMA Models -- 3.5.4 Gamma Models by Random Thinning -- 3.5.5 GAR(1) Models with Conditional Specifications -- 3.6 Estimation for Gamma Time Series -- 3.7 Other Non-negative Stationary AR(1) Models -- 3.7.1 Mixed Exponential AR(1) Model -- 3.7.2 Birnbaum-Saunders AR Model -- 3.7.3 Inverse Gaussian Time Series Models -- 3.7.4 Mittag-Leffler Processes -- References
001441801 506__ $$aAccess limited to authorized users.
001441801 520__ $$aThis book brings together a variety of non-Gaussian autoregressive-type models to analyze time-series data. This book collects and collates most of the available models in the field and provide their probabilistic and inferential properties. This book classifies the stationary time-series models into different groups such as linear stationary models with non-Gaussian innovations, linear stationary models with non-Gaussian marginal distributions, product autoregressive models and minification models. Even though several non-Gaussian time-series models are available in the literature, most of them are focusing on the model structure and the probabilistic properties.
001441801 588__ $$aDescription based on print version record.
001441801 650_0 $$aTime-series analysis.
001441801 650_6 $$aSérie chronologique.
001441801 655_7 $$aLlibres electrònics.$$2thub
001441801 655_0 $$aElectronic books.
001441801 77608 $$iPrint version:$$aBALAKRISHNA, N.$$tNON-GAUSSIAN AUTOREGRESSIVE-TYPE TIME SERIES.$$d[S.l.] : SPRINGER VERLAG, SINGAPOR, 2022$$z9811681619$$w(OCoLC)1280600500
001441801 852__ $$bebk
001441801 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-8162-2$$zOnline Access$$91397441.1
001441801 909CO $$ooai:library.usi.edu:1441801$$pGLOBAL_SET
001441801 980__ $$aBIB
001441801 980__ $$aEBOOK
001441801 982__ $$aEbook
001441801 983__ $$aOnline
001441801 994__ $$a92$$bISE