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Using Perl for Statistics: Data Processing and Statistical Computing

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  • Baiocchi, Giovanni

Abstract

In this paper we show how Perl, an expressive and extensible high-level programming language, with network and ob ject-oriented programming support, can be used in processing data for statistics and statistical computing. The paper is organized in two parts. In Part I, we introduce the Perl programming language, with particular emphasis on the features that distinguish it from conventional languages. Then, using practical examples, we demonstrate how Perl's distinguishing features make it particularly well suited to perform labor intensive and sophisticated tasks ranging from the preparation of data to the writing of statistical reports. In Part II we show how Perl can be extended to perform statistical computations using modules and by "embedding" specialized statistical applications. We provide example on how Perl can be used to do simple statistical analyses, perform complex statistical computations involving matrix algebra and numerical optimization, and make statistical computations more easily reproducible. We also investigate the numerical and statistical reliability of various Perl statistical modules. Important computing issues such as ease of use, speed of calculation, and efficient memory usage, are also considered.

Suggested Citation

  • Baiocchi, Giovanni, 2004. "Using Perl for Statistics: Data Processing and Statistical Computing," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i01).
    • Handle: RePEc:jss:jstsof:v:011:i01
    DOI: http://hdl.handle.net/10.18637/jss.v011.i01
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    Cited by:

    1. R L Bregman, 2009. "Preemptive expediting to improve project due date performance," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 120-129, January.
    2. Bregman, Robert L., 2009. "A heuristic procedure for solving the dynamic probabilistic project expediting problem," European Journal of Operational Research, Elsevier, vol. 192(1), pages 125-137, January.
    3. Giovanni Baiocchi, 2009. "PDL: an object-oriented programming environment for econometrics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(5), pages 849-856.
    4. Giovanni Baiocchi, 2007. "Reproducible research in computational economics: guidelines, integrated approaches, and open source software," Computational Economics, Springer;Society for Computational Economics, vol. 30(1), pages 19-40, August.

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