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60 Publications


2018 | Conference Paper | LibreCat-ID: 4665
Schäfer, B., & Feng, Y. (2018). Further Development of the Double Conditional Smoothing for Nonparametric Surfaces Under a Lattice Spatial Model. In Book of Abstracts (p. 7). Paderborn, Germany.
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2018 | Conference Paper | LibreCat-ID: 4667
Feng, Y., & Letmathe, S. (2018). The Non-Gaussian ESEMIFAR Model. Presented at the European Conference on Data Analysis, Paderborn, Germany.
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2018 | Conference Paper | LibreCat-ID: 4668
Forstinger, S., Feng, Y., & Peitz, C. (2018). Forecasting Non-Negative Financial Processes Using Different Parametric and Semi-Parametric ACD-Type Models. In Book of Abstracts (p. 17). Paderborn, Germany.
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2018 | Conference Paper | LibreCat-ID: 4669
Zhang, X., & Feng, Y. (2018). A Box-Cox Semiparametric Multiplicative Error Model. In Book of Abstracts (p. 19). Paderborn, Germany.
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2018 | Dissertation | LibreCat-ID: 4672
Forstinger, S. (2018). Modelling and forecasting financial and economic time series using different semiparametric ACD models. Universität Paderborn.
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2017 | Working Paper | LibreCat-ID: 4633
Zhang, X., Feng, Y., & Peitz, C. (2017). A general class of SemiGARCH models based on the Box-Cox transformation.
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2017 | Working Paper | LibreCat-ID: 4671
Feng, Y., & Gries, T. (2017). Data-driven local polynomial for the trend and its derivatives in economic time series.
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2016 | Book | LibreCat-ID: 5119
Peitz, C. (2016). Die parametrische und semiparametrische Analyse von Finanzzeitreihen: neue Methoden, Modelle und Anwendungsm\"oglichkeiten. Springer-Verlag.
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2015 | Journal Article | LibreCat-ID: 4592
Feng, Y., Forstinger, S., & Peitz, C. (2015). On the iterative plug-in algorithm for estimating diurnal patterns of financial trade durations. Journal of Statistical Computation and Simulation, 86(12), 2291–2307. https://doi.org/10.1080/00949655.2015.1107908
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2015 | Journal Article | LibreCat-ID: 4593
Feng, Y., & Zhou, C. (2015). Forecasting financial market activity using a semiparametric fractionally integrated Log-ACD. International Journal of Forecasting, 31(2), 349–363. https://doi.org/10.1016/j.ijforecast.2014.09.001
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