Abstract
New and advanced methods for nonlinear time series analysis are in general not available in standard software packages. Their implementation requires substantial time, computing power as well as programming skills. In time series analysis such a scenario is given by a recently suggested nonparametric lag selection procedure for univariate nonlinear autoregressive models which is based on the Corrected Asymptotic Final Prediction Error. In this paper we suggest a worldwide Web based specific client/server architecture that provides empirical researchers with fast access to new methods and powerful computing environments without knowing the statistical computing language and the server location. This architecture is implemented using the XploRe Quantlet technology and illustrated for nonparametric lag selection. Access to the Quantlet computing service can be obtained via standard WWW browsers or a Java client. The XploRe Quantlet service can be helpful in constructing research books and interactive teaching environments as the electronic version of this paper, available from http://ise.wiwi.hu-berlin.de/∼rolf/webquant.pdf, demonstrates.
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Härdle, W., Kleinow, T. & Tschernig, R. Web Quantlets for Time Series Analysis. Annals of the Institute of Statistical Mathematics 53, 179–188 (2001). https://doi.org/10.1023/A:1017980807689
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DOI: https://doi.org/10.1023/A:1017980807689