References in zbMATH (referenced in 40 articles )

Showing results 1 to 20 of 40.
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  1. Ben O’Neill: Gaussian ARMA models in the ts.extend package (2021) arXiv
  2. Ebner, Bruno: On combining the zero bias transform and the empirical characteristic function to test normality (2021)
  3. Kalantari, Mahdi: Forecasting COVID-19 pandemic using optimal singular spectrum analysis (2021)
  4. Kojadinovic, Ivan; Verdier, Ghislain: Nonparametric sequential change-point detection for multivariate time series based on empirical distribution functions (2021)
  5. Zhang, Shulin; Zhou, Qian M.; Lin, Huazhen: Goodness-of-fit test of copula functions for semi-parametric univariate time series models (2021)
  6. Arsalane Chouaib Guidoum, Kamal Boukhetala: Performing Parallel Monte Carlo and Moment Equations Methods for Ito and Stratonovich Stochastic Differential Systems: R Package Sim.DiffProc (2020) not zbMATH
  7. Esam Mahdi: portes: An R Package for Portmanteau Tests in Time Series Models (2020) arXiv
  8. Henze, Norbert; Visagie, Jaco: Testing for normality in any dimension based on a partial differential equation involving the moment generating function (2020)
  9. Izhar Asael Alonzo Matamoros, Alicia Nieto-Reyes: An R package for Normality in Stationary Processes (2020) arXiv
  10. Lykou, R.; Tsaklidis, G.; Papadimitriou, E.: Change point analysis on the Corinth Gulf (Greece) seismicity (2020)
  11. Stephan Smeekes, Ines Wilms : bootUR: An R Package for Bootstrap Unit Root Tests (2020) arXiv
  12. Chatfield, Chris; Xing, Haipeng: The analysis of time series. An introduction with R (2019)
  13. David Ardia; Keven Bluteau; Kris Boudt; Leopoldo Catania; Denis-Alexandre Trottier: Markov-Switching GARCH Models in R: The MSGARCH Package (2019) not zbMATH
  14. Ramasubramanian, Karthik; Singh, Abhishek: Machine learning using R. With time series and industry-based use cases in R (2019)
  15. Brown, Jonathon D.: Advanced statistics for the behavioral sciences. A computational approach with R (2018)
  16. Hendrych, Radek; Cipra, Tomáš: Self-weighted recursive estimation of GARCH models (2018)
  17. Iacus, Stefano M.; Yoshida, Nakahiro: Simulation and inference for stochastic processes with YUIMA. A comprehensive R framework for SDEs and other stochastic processes (2018)
  18. Mair, Patrick: Modern psychometrics with R (2018)
  19. Stübinger, Johannes; Endres, Sylvia: Pairs trading with a mean-reverting jump-diffusion model on high-frequency data (2018)
  20. Stübinger, Johannes; Mangold, Benedikt; Krauss, Christopher: Statistical arbitrage with vine copulas (2018)

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