References in zbMATH (referenced in 29 articles )

Showing results 1 to 20 of 29.
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  1. Nordhausen, Klaus; Fischer, Gregor; Filzmoser, Peter: Blind source separation for compositional time series (2021)
  2. Sun, Zequn; Fisher, Thomas J.: Testing for correlation between two time series using a parametric bootstrap (2021)
  3. Zhang, Yongli; Rolling, Craig; Yang, Yuhong: Estimating and forecasting dynamic correlation matrices: a nonlinear common factor approach (2021)
  4. Zhao, Xin; Barber, Stuart; Taylor, Charles C.; Milan, Zoka: Interval forecasts based on regression trees for streaming data (2021)
  5. Esam Mahdi: portes: An R Package for Portmanteau Tests in Time Series Models (2020) arXiv
  6. Izhar Asael Alonzo Matamoros, Cristian Andres Cruz Torres: varstan: An R package for Bayesian analysis of structured time series models with Stan (2020) arXiv
  7. Tomarchio, Salvatore D.; Punzo, Antonio: Dichotomous unimodal compound models: application to the distribution of insurance losses (2020)
  8. Arratia, Argimiro; Dorador, Albert: On the efficacy of stop-loss rules in the presence of overnight gaps (2019)
  9. Czado, Claudia: Analyzing dependent data with vine copulas. A practical guide with R (2019)
  10. David Ardia; Keven Bluteau; Kris Boudt; Leopoldo Catania; Denis-Alexandre Trottier: Markov-Switching GARCH Models in R: The MSGARCH Package (2019) not zbMATH
  11. David Ardia; Kris Boudt; Leopoldo Catania: Generalized Autoregressive Score Models in R: The GAS Package (2019) not zbMATH
  12. Bee, Marco; Dickson, Maria Michela; Santi, Flavio: Likelihood-based risk estimation for variance-gamma models (2018)
  13. Davis, Richard A.; Drees, Holger; Segers, Johan; Warchoł, Michał: Inference on the tail process with application to financial time series modeling (2018)
  14. Punzo, Antonio; Bagnato, Luca; Maruotti, Antonello: Compound unimodal distributions for insurance losses (2018)
  15. Stefano Iacus; Lorenzo Mercuri; Edit Rroji: COGARCH(p, q): Simulation and Inference with the yuima Package (2017) not zbMATH
  16. Gregor Kastner: Dealing with Stochastic Volatility in Time Series Using the R Package stochvol (2016) not zbMATH
  17. Mirzaei Talarposhti, Fatemeh; Javedani Sadaei, Hossein; Enayatifar, Rasul; Gadelha Guimarães, Frederico; Mahmud, Maqsood; Eslami, Tayyebeh: Stock market forecasting by using a hybrid model of exponential fuzzy time series (2016)
  18. Chen, Yining: Semiparametric time series models with log-concave innovations: maximum likelihood estimation and its consistency (2015)
  19. Matilainen, Markus; Nordhausen, Klaus; Oja, Hannu: New independent component analysis tools for time series (2015)
  20. Arratia, Argimiro: Computational finance. An introductory course with R (2014)

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