LEADER 03416nam 2200529 450 001 996466414003316 005 20230421111603.0 010 $a981-16-2264-7 024 7 $a10.1007/978-981-16-2264-9 035 $a(CKB)4100000011954465 035 $a(DE-He213)978-981-16-2264-9 035 $a(MiAaPQ)EBC6639207 035 $a(Au-PeEL)EBL6639207 035 $a(OCoLC)1256242018 035 $a(PPN)258061316 035 $a(EXLCZ)994100000011954465 100 $a20220201d2021 uy 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDiagnostic methods in time series /$fFumiya Akashi [and three others] 205 $a1st ed. 2021. 210 1$aGateway East, Singapore :$cSpringer,$d[2021] 210 4$d©2021 215 $a1 online resource (X, 108 p. 17 illus., 10 illus. in color.) 225 1 $aJSS Research Series in Statistics,$x2364-0057 311 $a981-16-2263-9 320 $aIncludes bibliographical references and index. 327 $aChapter 1. Elements of Stochastic Processes -- Chapter 2. Systematic approach for portmanteau tests in view of Whittle likelihood ratio -- Chapter 3. A new look at portmanteau test -- Chapter 4. Adjustments for a class of tests under nonstandard conditions -- Chapter 5. Adjustments for variance component tests in ANOVA models -- Chapter 6. Robust causality test of infinite variance processes. 330 $aThis book contains new aspects of model diagnostics in time series analysis, including variable selection problems and higher-order asymptotics of tests. This is the first book to cover systematic approaches and widely applicable results for nonstandard models including infinite variance processes. The book begins by introducing a unified view of a portmanteau-type test based on a likelihood ratio test, useful to test general parametric hypotheses inherent in statistical models. The conditions for the limit distribution of portmanteau-type tests to be asymptotically pivotal are given under general settings, and very clear implications for the relationships between the parameter of interest and the nuisance parameter are elucidated in terms of Fisher-information matrices. A robust testing procedure against heavy-tailed time series models is also constructed in the context of variable selection problems. The setting is very reasonable in the context of financial data analysis and econometrics, and the result is applicable to causality tests of heavy-tailed time series models. In the last two sections, Bartlett-type adjustments for a class of test statistics are discussed when the parameter of interest is on the boundary of the parameter space. A nonlinear adjustment procedure is proposed for a broad range of test statistics including the likelihood ratio, Wald and score statistics. 410 0$aJSS Research Series in Statistics,$x2364-0057 606 $aTime-series analysis 606 $aAnàlisi de sèries temporals$2thub 608 $aLlibres electrònics$2thub 615 0$aTime-series analysis. 615 7$aAnàlisi de sèries temporals 676 $a519.55 700 $aAkashi$b Fumiya$0767971 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996466414003316 996 $aDiagnostic Methods in Time Series$91905068 997 $aUNISA