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Wavelet methods for continuous-time prediction using Hilbert-valued autoregressive processes
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Cited by:
- Horváth, Lajos & Kokoszka, Piotr & Rice, Gregory, 2014. "Testing stationarity of functional time series," Journal of Econometrics, Elsevier, vol. 179(1), pages 66-82.
- Axel Bücher & Holger Dette & Florian Heinrichs, 2020. "Detecting deviations from second-order stationarity in locally stationary functional time series," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(4), pages 1055-1094, August.
- Shang, Han Lin & Hyndman, Rob.J., 2011.
"Nonparametric time series forecasting with dynamic updating,"
Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1310-1324.
- Han Lin Shang & Rob J Hyndman, 2009. "Nonparametric time series forecasting with dynamic updating," Monash Econometrics and Business Statistics Working Papers 8/09, Monash University, Department of Econometrics and Business Statistics.
- Álvarez-Liébana, Javier & Bosq, Denis & Ruiz-Medina, María D., 2016. "Consistency of the plug-in functional predictor of the Ornstein–Uhlenbeck process in Hilbert and Banach spaces," Statistics & Probability Letters, Elsevier, vol. 117(C), pages 12-22.
- Berhoune, Kamila & Bensmain, Nawel, 2018. "Sieves estimator of functional autoregressive process," Statistics & Probability Letters, Elsevier, vol. 135(C), pages 60-69.
- Aue, Alexander & Gabrys, Robertas & Horváth, Lajos & Kokoszka, Piotr, 2009. "Estimation of a change-point in the mean function of functional data," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2254-2269, November.
- Horváth, Lajos & Husková, Marie & Kokoszka, Piotr, 2010. "Testing the stability of the functional autoregressive process," Journal of Multivariate Analysis, Elsevier, vol. 101(2), pages 352-367, February.
- Antoniadis, Anestis & Paparoditis, Efstathios & Sapatinas, Theofanis, 2009. "Bandwidth selection for functional time series prediction," Statistics & Probability Letters, Elsevier, vol. 79(6), pages 733-740, March.
- A. Soltani & M. Hashemi, 2011. "Periodically correlated autoregressive Hilbertian processes," Statistical Inference for Stochastic Processes, Springer, vol. 14(2), pages 177-188, May.
- Laukaitis, Algirdas, 2008. "Functional data analysis for cash flow and transactions intensity continuous-time prediction using Hilbert-valued autoregressive processes," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1607-1614, March.
- Álvarez-Liébana, J. & Bosq, D. & Ruiz-Medina, M.D., 2017. "Asymptotic properties of a component-wise ARH(1) plug-in predictor," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 12-34.
- Ying Chen & Bo Li, 2017. "An Adaptive Functional Autoregressive Forecast Model to Predict Electricity Price Curves," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 371-388, July.
- Alexander Aue & Diogo Dubart Norinho & Siegfried Hörmann, 2015. "On the Prediction of Stationary Functional Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 378-392, March.
- Ying Chen & Wee Song Chua & Wolfgang Karl Härdle, 2019.
"Forecasting limit order book liquidity supply–demand curves with functional autoregressive dynamics,"
Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1473-1489, September.
- Chen, Ying & Chua, Wee Song & Härdle, Wolfgang Karl, 2016. "Forecasting limit order book liquidity supply-demand curves with functional AutoRegressive dynamics," SFB 649 Discussion Papers 2016-025, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
- Caponera, Alessia & Panaretos, Victor M., 2022. "On the rate of convergence for the autocorrelation operator in functional autoregression," Statistics & Probability Letters, Elsevier, vol. 189(C).
- Piotr Fryzlewicz & Sébastien Bellegem & Rainer Sachs, 2003. "Forecasting non-stationary time series by wavelet process modelling," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 55(4), pages 737-764, December.
- repec:hum:wpaper:sfb649dp2016-025 is not listed on IDEAS
- Zhang, Xianyang, 2016. "White noise testing and model diagnostic checking for functional time series," Journal of Econometrics, Elsevier, vol. 194(1), pages 76-95.
- István Berkes & Robertas Gabrys & Lajos Horváth & Piotr Kokoszka, 2009. "Detecting changes in the mean of functional observations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(5), pages 927-946, November.
- Boukhiar, Souad & Mourid, Tahar, 2022. "Resolvent estimators for functional autoregressive processes with random coefficients," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
- Ruiz-Medina, M.D. & Romano, E. & Fernández-Pascual, R., 2016. "Plug-in prediction intervals for a special class of standard ARH(1) processes," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 138-150.
- Mas, André, 2007. "Weak convergence in the functional autoregressive model," Journal of Multivariate Analysis, Elsevier, vol. 98(6), pages 1231-1261, July.
- Devin Didericksen & Piotr Kokoszka & Xi Zhang, 2012. "Empirical properties of forecasts with the functional autoregressive model," Computational Statistics, Springer, vol. 27(2), pages 285-298, June.
- Yousri Slaoui, 2020. "Recursive nonparametric regression estimation for dependent strong mixing functional data," Statistical Inference for Stochastic Processes, Springer, vol. 23(3), pages 665-697, October.
- C. Abraham & G. Biau & B. Cadre, 2006. "On the Kernel Rule for Function Classification," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 58(3), pages 619-633, September.
- Canale, Antonio & Vantini, Simone, 2016. "Constrained functional time series: Applications to the Italian gas market," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1340-1351.
- van Delft, Anne & Eichler, Michael, 2017. "Locally Stationary Functional Time Series," LIDAM Discussion Papers ISBA 2017023, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
- Xu, Meng & Li, Jialiang & Chen, Ying, 2017. "Varying coefficient functional autoregressive model with application to the U.S. treasuries," Journal of Multivariate Analysis, Elsevier, vol. 159(C), pages 168-183.
- Wafaa Benyelles & Tahar Mourid, 2012. "On a minimum distance estimate of the period in functional autoregressive processes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(8), pages 1703-1718, February.
- Kargin, V. & Onatski, A., 2008.
"Curve forecasting by functional autoregression,"
Journal of Multivariate Analysis, Elsevier, vol. 99(10), pages 2508-2526, November.
- A. Onatski & V. Karguine, 2005. "Curve Forecasting by Functional Autoregression," Computing in Economics and Finance 2005 59, Society for Computational Economics.
- Ana M. Aguilera & Manuel Escabias & Francisco A. Ocaña & Mariano J. Valderrama, 2015. "Functional Wavelet-Based Modelling of Dependence Between Lupus and Stress," Methodology and Computing in Applied Probability, Springer, vol. 17(4), pages 1015-1028, December.
- Laukaitis, Algirdas & Vasilecas, Olegas & Laukaitis, Ricardas, 2009. "Estimation of the autoregressive operator by wavelet packets," Statistics & Probability Letters, Elsevier, vol. 79(1), pages 38-43, January.