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A New Generation of a Statistical Computing Environment on the Net

  • Conference paper
COMPSTAT

Abstract

With the availability of the net a new generation of computing environments has to be designed for a large scale of statistical tasks ranging from data analysis to highly interactive operations. It must combine the flexibility of multi window desktops with standard operations and interactive user driven actions. It must be equally well suited for first year students and for high demanding researchers. Its design must has various degrees of flexibility that allow to address different levels of user groups. We present here some ideas how a new generation of a computing environment can be used as a student front end tool for teaching elementary statistics as well as a research device for highly computer intensive tasks, e.g. for semiparametric analysis and bootstrapping.

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References

  • Becker, R. A., Chambers, J. C. & Wilks, A. R. (1988). The new S language: a programming environment for data analysis and graphics, Wadsworth and Brooks/Cole Advanced Books and Software, Pacific Grove, CA.

    Google Scholar 

  • Bowman, A. & Robinson, D. (1989). C.I.T.: Introduction to statistics, Software, IOP Publishing Ltd.

    Google Scholar 

  • Bowman, A. & Robinson, D. (1990). C.I.T.: Regression & Anova, Software, IOP Publishing Ltd.

    Google Scholar 

  • Biining, H. (1983). Adaptive distribution-free test (german), Statistische Hefte pp. 47–67.

    Google Scholar 

  • Chen, R., Härdle, W., Linton, O. & Severance-Lossin, E. (1996). Nonparametric estimation of additive separable regression models, in W. Härdle &; M. Schimek (eds), Statistical Theory and Computational Aspects of Smoothing, Physika Verlag Heidelberg.

    Google Scholar 

  • Fan, J. & Gijbels, I. (1996). Local Polynomial Modeling and Its Application-Theory and Methodologies, Chapman Sz Hall.

    Google Scholar 

  • Flury, B. & Riedwyl, H. (1981). Graphical representation of multivariate data by means of asymmetrical faces, Journal of the American Statistical Association 76(376): 757–765.

    Article  Google Scholar 

  • Härdle, W. (1988). XploRe — a Computing Environment for eXploratory Regression and density smoothing, Statistical Software Newsletters 14: 113–119.

    Google Scholar 

  • Härdle, W., Klinke, S. & Turlach, B. (1995). XploRe-an Interactive Statistical Computing Environment, Springer, Heidelberg.

    Google Scholar 

  • Härdle, W. & Scott, D. (1992). Smoothing in low and high dimensions by weighted averaging using rounded points, Computational Statistics pp. 97–128.

    Google Scholar 

  • Hüttner, J., Wandke, H. & Rätz, A. (1995). Benutzerfreundliche Software, Bernd-Michael Paschke Verlag Berlin 1995, Berlin.

    Google Scholar 

  • ISP (1987). ISP is a program for PCs available from Artemis Systems Inc.

    Google Scholar 

  • Jones, M. & Sibson, R. (1987). What is projection pursuit?, Journal of the Royal Statistical Society A 150: 1–36.

    Article  MathSciNet  MATH  Google Scholar 

  • Katkovnik, V. (1985). Nonparametric identification and data smoothing: local approximation approach in (in Russian), Nauka, Moscow.

    Google Scholar 

  • Katkovnik, V. Y. (1979). Linear and nonlinear methods for nonparametric regression analysis (in russian), Avtomatika i Telemehanika pp. 35–46.

    Google Scholar 

  • Katkovnik, V. Y. (1983). Convergence of the linear and nonlinear nonparametric kernel estimates (in russian), Avtomatika i Telemehanika pp. 108–20.

    Google Scholar 

  • Klinke, S. (1995). Data Structures in Computational Statistics, PhD thesis, Institute of statistics, Catholic university of Louvain.

    Google Scholar 

  • Proenca, I. (1995). Interactive graphics for teaching simple statistics, XploRe — an interactive statistical computing environment, Springer, pp. 113–140.

    Google Scholar 

  • Schneider, S (1988). See Becker, Chambers and Wilks, (1988).

    Google Scholar 

  • Schneider, B. (1994). Querleser — Zerstreutheit mit System: Skip-Listen, c’t 2: 204–207.

    Google Scholar 

  • Stuetzle, W. (1987). Plot windows, Journal of the American Statistical Association 82(398): 466–475.

    Article  Google Scholar 

  • Velleman, P. (1992). Data Desk, Data Description, Ithaca NY.

    Google Scholar 

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© 1996 Physica-Verlag Heidelberg

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Schmelzer, S., Kötter, T., Klinke, S., Härdle, W. (1996). A New Generation of a Statistical Computing Environment on the Net. In: Prat, A. (eds) COMPSTAT. Physica-Verlag HD. https://doi.org/10.1007/978-3-642-46992-3_12

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  • DOI: https://doi.org/10.1007/978-3-642-46992-3_12

  • Publisher Name: Physica-Verlag HD

  • Print ISBN: 978-3-7908-0953-4

  • Online ISBN: 978-3-642-46992-3

  • eBook Packages: Springer Book Archive

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