LEADER 00901nam--2200337---450- 001 990001704390203316 005 20040527112747.0 035 $a000170439 035 $aUSA01000170439 035 $a(ALEPH)000170439USA01 035 $a000170439 100 $a20040527d1966----km-y0itay0103----ba 101 0 $aita 102 $aIT 105 $a||||||||001yy 200 1 $aEsercizio per l'anno 1965$fIstituto per la ricostruzione industriale 210 $aRoma$cIRI$d1966 215 $aIV, 264 p.$d30 cm. 410 0$12001 454 1$12001 461 1$1001-------$12001 710 02$aIRI$0354205 801 0$aIT$bsalbc$gISBD 912 $a990001704390203316 951 $aIII G 178$b30998 L.M.$cIII G 951 $aIII G 179$b30830 L.M.$cIII G 959 $aBK 969 $aUMA 979 $aSIAV8$b10$c20040527$lUSA01$h1127 996 $aEsercizio per l'anno 1965$9947641 997 $aUNISA LEADER 05857nam 22006855 450 001 9910728935303321 005 20240319164649.0 010 $a981-9908-03-5 024 7 $a10.1007/978-981-99-0803-5 035 $a(MiAaPQ)EBC30558385 035 $a(Au-PeEL)EBL30558385 035 $a(OCoLC)1381096935 035 $a(DE-He213)978-981-99-0803-5 035 $a(BIP)088157097 035 $a(PPN)270619097 035 $a(CKB)26816396800041 035 $a(EXLCZ)9926816396800041 100 $a20230531d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aResearch Papers in Statistical Inference for Time Series and Related Models $eEssays in Honor of Masanobu Taniguchi /$fedited by Yan Liu, Junichi Hirukawa, Yoshihide Kakizawa 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (591 pages) 311 08$aPrint version: Liu, Yan Research Papers in Statistical Inference for Time Series and Related Models Singapore : Springer Singapore Pte. Limited,c2023 9789819908028 327 $aChapter 1. Frequency domain empirical likelihood method for infinite variance models -- Chapter 2. Diagnostic testing for time series -- Chapter 3. Statistical Inference for Glaucoma Detection -- Chapter 4. On Hysteretic Vector Autoregressive Model with Applications -- Chapter 5. Probabilistic Forecasting for Daily Electricity Loads and Quantiles for Curve-to-Curve Regression -- Chapter 6. Exact topological inference on resting-state brain networks -- Chapter 7. An Introduction to Geostatistics -- Chapter 8. Relevant change points in high dimensional time series -- Chapter 9. Adaptiveness of the empirical distribution of residuals in semi-parametric conditional location scale models -- Chapter 10. Standard testing procedures for white noise and heteroskedasticity -- Chapter 11. Estimation of Trigonometric Moments for Circular Binary Series -- Chapter 12. Time series analysis with unsupervised learning -- Chapter 13. Recovering the market volatility shocks in high-dimensional time series -- Chapter 14. Asymptotic properties of mildly explosive processes with locally stationary disturbance -- Chapter 15. Multi-Asset Empirical Martingale Price Estimators for Financial Derivatives -- Chapter 16. Consistent Order Selection for ARFIMA Processes -- Chapter 17. Recursive asymmetric kernel density estimation for nonnegative data -- Chapter 18. Fitting an error distribution in some heteroscedastic time series models -- Chapter 19. Symbolic Interval-Valued Data Analysis for Time Series Based on Auto-Interval-Regressive Models -- Chapter 20. ROBUST LINEAR INTERPOLATION AND EXTRAPOLATION OF STATIONARY TIME SERIES -- Chapter 21. Non Gaussian models for fMRI data -- Chapter 22. Robust inference for ordinal response models -- Chapter 23. Change point problems for diffusion processes and time series models -- Chapter 24. Empirical likelihood approach for time series -- Chapter 25. Exploring the Dependence Structure Between Oscillatory Activities in Multivariate Time Series -- Chapter 26. Projection-based nonparametric goodness-of-fit testing with functional data. 330 $aThis book compiles theoretical developments on statistical inference for time series and related models in honor of Masanobu Taniguchi's 70th birthday. It covers models such as long-range dependence models, nonlinear conditionally heteroscedastic time series, locally stationary processes, integer-valued time series, Lévy Processes, complex-valued time series, categorical time series, exclusive topic models, and copula models. Many cutting-edge methods such as empirical likelihood methods, quantile regression, portmanteau tests, rank-based inference, change-point detection, testing for the goodness-of-fit, higher-order asymptotic expansion, minimum contrast estimation, optimal transportation, and topological methods are proposed, considered, or applied to complex data based on the statistical inference for stochastic processes. The performances of these methods are illustrated by a variety of data analyses. This collection of original papers provides the reader with comprehensive and state-of-the-art theoretical works on time series and related models. It contains deep and profound treatments of the asymptotic theory of statistical inference. In addition, many specialized methodologies based on the asymptotic theory are presented in a simple way for a wide variety of statistical models. This Festschrift finds its core audiences in statistics, signal processing, and econometrics. 606 $aTime-series analysis 606 $aMathematical statistics 606 $aNonparametric statistics 606 $aTime Series Analysis 606 $aParametric Inference 606 $aNon-parametric Inference 606 $aMathematical Statistics 606 $aEstadística matemàtica$2thub 606 $aAnàlisi de sèries temporals$2thub 608 $aLlibres electrònics$2thub 615 0$aTime-series analysis. 615 0$aMathematical statistics. 615 0$aNonparametric statistics. 615 14$aTime Series Analysis. 615 24$aParametric Inference. 615 24$aNon-parametric Inference. 615 24$aMathematical Statistics. 615 7$aEstadística matemàtica 615 7$aAnàlisi de sèries temporals 676 $a519.55 700 $aLiu$b Yan$0652380 701 $aHirukawa$b Junichi$01365698 701 $aKakizawa$b Yoshihide$0256443 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910728935303321 996 $aResearch Papers in Statistical Inference for Time Series and Related Models$93387849 997 $aUNINA