标题
Long-Range Dependent Curve Time Series
作者
关键词
-
出版物
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
Volume -, Issue -, Pages 1-30
出版商
Informa UK Limited
发表日期
2019-04-17
DOI
10.1080/01621459.2019.1604362
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Limit theorems for Hilbert space-valued linear processes under long range dependence
- (2018) Marie-Christine Düker STOCHASTIC PROCESSES AND THEIR APPLICATIONS
- Functional Autoregression for Sparsely Sampled Data
- (2017) Daniel R. Kowal et al. JOURNAL OF BUSINESS & ECONOMIC STATISTICS
- An Adaptive Functional Autoregressive Forecast Model to Predict Electricity Price Curves
- (2017) Ying Chen et al. JOURNAL OF BUSINESS & ECONOMIC STATISTICS
- Inference for the autocovariance of a functional time series under conditional heteroscedasticity
- (2017) Piotr Kokoszka et al. JOURNAL OF MULTIVARIATE ANALYSIS
- A Bayesian Multivariate Functional Dynamic Linear Model
- (2017) Daniel R. Kowal et al. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
- Mortality and life expectancy forecasting for a group of populations in developed countries: A multilevel functional data method
- (2016) Han Lin Shang Annals of Applied Statistics
- Convolutional autoregressive models for functional time series
- (2016) Xialu Liu et al. JOURNAL OF ECONOMETRICS
- Functional Generalized Autoregressive Conditional Heteroskedasticity
- (2016) Alexander Aue et al. JOURNAL OF TIME SERIES ANALYSIS
- A Plug-in Bandwidth Selection Procedure for Long-Run Covariance Estimation with Stationary Functional Time Series
- (2016) Gregory Rice et al. JOURNAL OF TIME SERIES ANALYSIS
- Bootstrap methods for stationary functional time series
- (2016) Han Lin Shang STATISTICS AND COMPUTING
- Operator self-similar processes and functional central limit theorems
- (2014) Vaidotas Characiejus et al. STOCHASTIC PROCESSES AND THEIR APPLICATIONS
- A survey of functional principal component analysis
- (2013) Han Lin Shang AStA-Advances in Statistical Analysis
- Dynamic functional data analysis with non-parametric state space models
- (2013) Márcio Poletti Laurini JOURNAL OF APPLIED STATISTICS
- Testing stationarity of functional time series
- (2013) Lajos Horváth et al. JOURNAL OF ECONOMETRICS
- Selecting the Number of Principal Components in Functional Data
- (2013) Yehua Li et al. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
- Functional dynamic factor models with application to yield curve forecasting
- (2012) Spencer Hays et al. Annals of Applied Statistics
- Factor modeling for high-dimensional time series: Inference for the number of factors
- (2012) Clifford Lam et al. ANNALS OF STATISTICS
- Functional prediction of intraday cumulative returns
- (2012) Piotr Kokoszka et al. STATISTICAL MODELLING
- Weak invariance principles for sums of dependent random functions
- (2012) István Berkes et al. STOCHASTIC PROCESSES AND THEIR APPLICATIONS
- Description length and dimensionality reduction in functional data analysis
- (2011) D.S. Poskitt et al. COMPUTATIONAL STATISTICS & DATA ANALYSIS
- Parametric mortality improvement rate modelling and projecting
- (2011) Steven Haberman et al. INSURANCE MATHEMATICS & ECONOMICS
- Weakly dependent functional data
- (2010) Siegfried Hörmann et al. ANNALS OF STATISTICS
- Identifying the finite dimensionality of curve time series
- (2010) Neil Bathia et al. ANNALS OF STATISTICS
- Two estimators of the long-run variance: Beyond short memory
- (2009) Karim M. Abadir et al. JOURNAL OF ECONOMETRICS
- Modeling Hazard Rates as Functional Data for the Analysis of Cohort Lifetables and Mortality Forecasting
- (2009) Jeng-Min Chiou et al. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
- Econometric estimation in long-range dependent volatility models: Theory and practice
- (2008) Isabel Casas et al. JOURNAL OF ECONOMETRICS
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