PING  0.9
Statistical data handling and processing in production environment
References

Avalaible resources/links on this page regarding:

General high-level guidelines/white papers

  • Memobust handbook: General Observations on GSBPM (version 1.0.1), 2014.
  • UNECE's Statistical Division: Generic Statistical Business Process Model (GSBPM v5.0), 2013.
  • Eurostat: Analysis of the future research needs for Official Statistics (online access), coll.: Methodologies & Working papers, DOI:10.2785/19629, 2012.
  • Commission of the European Communities: Communication from the Commission to the European Parliament and the Council on the production method of EU statistics: a vision for the next decade, (COM(2009) 404), 2009
  • European Commission: European Union Public Licence (EUPL version 1.1), 2007.
  • Eurostat: Quality assurance framework of the European Statistical System: (ESS QAF version 1.2)
  • Eurostat: Handbook on data quality - Assessment methods and tools (DatQAM) eds: Ehling, M. and Körner, T., 2007.
  • Olcoz, S.: Openness and reuse of applications, available on JoinUp page on OpenApps - Sharing and Reuse of Software, 2012.

Generic statistical software developments

  • Hofert, M.: Guidelines for statistical projects: Coding and typography (online access), 2014.
  • Lalor, T. and Vale, S.: Modernising the production of official statistics (online access), Revista Internacional de Estadistica y Geografia, 4(2):72-79, 2013.
  • Ince D.C., Hatton L. and Graham-Cumming J.: The case for open computer programs (online access) Nature, 482:485-488, DOI:10.1038/nature10836, 2012.
  • Saltzer, J.H. and Kaashoek, M.F.: Principles of Computer System Design: An Introduction (online access and OpenCourseWare) Morgan Kaufmann, 2009.
  • Eurostat: Guidelines on developing statistical software as open source (version 1.3), 2009.
  • European Commission: Encouraging good practice in the use of open source software in public administrations (final version), 2005.
  • Wilkinson, L.: Practical guidelines for testing statistical software (online access), Springer-Verlag, coll.: Computational Statistics, 1994.

Implementation and methodology issues

  • Salgado, D.: A modern vision of official statistical production (online access), 2016.
  • Efron, B. and Hastie, T.: Computer Age Statistical Inference - Algorithms, Evidence, and Data Science, Cambridge University Press, 2016.
  • Quatember, A.: Pseudo-Populations - A Basic Concept in Statistical Surveys, Springer, DOI:10.1007/978-3-319-11785-0, 2015.
  • Gentle, J.E.: Computational Statistics, Springer-Verlag, coll.: "Statistics and Computing", DOI:10.1007/978-0-387-98144-4, 2014.
  • Valliant, R., Dever, J.A. and Kreuter, F.: Practical Tools for Designing and Weighting Survey Samples (online access), Springer-Verlag, coll.: Statistics for Social and Behavioral Sciences, DOI:10.1007/978-1-4614-6449-5, 2013.
  • Gentle, J.E., Härdle, W.K. and Mori, Y.: Handbook of Computational Statistics - Concepts and Methods, Springer-Verlag, coll.: "Handbook of Computational Statistics", DOI:10.1007/978-3-642-21551-3, 2012.
  • Heeringa, S.G., West, B.T. and Berglund, P.A.: Applied Survey Data Analysis (online access), Chapman & Hall/CRC, coll: "Statistics in the Social and Behavioral Sciences Series", 2010.
  • Brandt, M., Franconi, L., Guerke, C., Hundepool, A., Lucarelli, M., Mol, J., Ritchie, F., Seri, G. and Welpton, R.: Guidelines for the checking of output based on microdata research (online access), ESSnet SDC, 2010.
  • Hastie, T., Tibshirani, R. and Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction (online access), Springer-Verlag, coll.: "Series in Statistics", DOI:10.1007/978-0-387-84858-7, 2009.
  • Groves, R.M., Fowler, F.J., Couper, M.P., Lepkowski, J.M., Singer, E. and Tourangeau, R.: Survey Methodology (online access), 2nd ed., coll: "Series in Survey Methodology", Wiley, 2004.
  • Chambers, R.L. and Skinner, C.J.: Analyis of Survey Data (online access), coll: "Series in Survey Methodology", Wiley, 2003.
  • Dell, F., d'Hautefoeuille, X., Fevrier, P. and Masse, E.: Mise en oeuvre du calcul de variance par linearisation (online access), source code available here, 2002.
  • Breiman, L.: Statistical modeling: The two cultures (online access), Statistical Science, 16(3):199–231, 2001.
  • Hansen, M. H., Hurwitz, W. N. and Madow, W. G.: Sample Survey Methods and Theory, volumes I and II, John Wiley & Sons Inc., 1953.

SAS best practices and resources

  • Lafler, K.P. and Rosenbloom, M.: Best practice programming techniques for SAS users (online access), 2016.
  • Nizol, M.: A simple approach to the automated unit testing of clinical SAS macros (online access), 2013.
  • Billings, T.E. and Bankston, E.: Bridging the gap between SAS applications developed by business units and conventional IT production (online access), 2013.
  • Hu, J.: The hitchhiker's guide to Github: SAS programming goes social (online access), 2013.
  • Carpenter, A. and Henderson, D.: Macro programming best practices: Styles, guidelines and conventions including the rationale behind them (online access), 2012.
  • Lal, R. and Ranga, R.: How readable and comprehensible is a SAS program? A programmatic approach to getting an insight into a program (online access), 2012.
  • Hamilton, J.: What do you mean, not everyone is like me: Writing programs for others to run, (online access), 2012.
  • Rhoads, M.: Use the full power of SAS in your function-style macros (online access), 2012.
  • Delwiche, L.D. and Slaughter, S.J.: The Little SAS Book: A Primer (online access), 5th ed., SAS Institute Inc. 2012.
  • Nelson, G.S. and Zhou, J.: Good programming practices in healthcare - Creating robust programs (online access), 2012.
  • Pool, G.: Common sense SAS - Documenting and structuring your code (online access), 2012.
  • Philp, S.: An introduction to Git version control for SAS programmers (online access), 2012.
  • DiIorio, F.: Building the better macro: Best practices for the design of reliable, effective tools (online access), 2010.
  • Bonin, G.: Best practices and advanced tips and techniques for SAS macro programming (online access). 2010.
  • Derby, N.: Suggestions for organizing SAS code and project files (online access, 2010.
  • Gregory, M. and Serono, M.: Techniques for writing robust SAS macros (online access), 2009.
  • Cheng, E.: Better, faster, and cheaper SAS software lifecycle (online access), 2009.
  • Fecht, M.: THINK before you type... Best practices learned the Hard Way (online access), 2009.
  • O’Donoghue, S. and Ratcliffe, A.: Configuration management for SAS software projects (online access), 2009.
  • Truong, S.: SAS system and SAS program validation techniques (online access), 2009.
  • Carpenter, A.L. and Payne, T.: Programming for job security: Maximize your indispensability - Become a specialist (online access), 2008.
  • Williams, T.: Better SAS programming through version control (online access), 2007.
  • Groeneveld, J.: SAS macro validation criteria (online paper and wiki), 2006.
  • Winn, T.J.: Guidelines for coding of SAS programs (online access), 2004.
  • Howard, N.: Beyond debugging: Program validation (online access), 2003.
  • Wright, J.: Drawkcab Gnimmargorp: Test-Driven Development with FUTS (online access), 2006.
  • Carpenter, A.L. and Smith, R.O.: Library and file management: Building a dynamic application (online access), 2002.
  • Carpenter, A.L. and Payne, T.: Programming for job security revisited: Even more tips and techniques to maximize your indispensability (online access), 1998.
  • SAS Graphic Programs and Macros: available at http://www.datavis.ca/sasmac/.
  • FUTS - Framework for Unit Testing SAS: available at http://thotwave.com/portfolio-item/futs-framework-for-unit-testing-sas/.
  • "Programming-SAS" and "SAS_ListProcessing" from Hu's personal SAS code repositories.
  • SAS macros from Bass's personal code repository: available at https://github.com/scottbass/SAS/tree/master/Macro.
  • SASUnit - Unit testing for SAS programs: available at https://sourceforge.net/projects/sasunit/.

R best practices and resources

  • Templ, M. and Todorov, V.: The software environment R for official statistics and survey methodology (online access), Austrian Journal of Statistics, 45:97–124, DOI:10.17713/ajs.v45i1.100, 2016.
  • Nash, J.C. (2014): On best practice optimization methods in R (online access), Journal of Statistical Software, 60(2), 2014.
  • Todorov, M. and Templ, M.: R in the statistical office: Part II (online access), United Nations Industrial Development Organization, Working Paper 1/2012, 2012.
  • Templ, M., Hulliger, B., Alfons, A., Lussmann, D. and Filzmoser, P.: Visualisation Tools (online access Advanced Methodology for European Laeken Indicators, Project Report, 2011.
  • Todorov, M.: R in the statistical office: The UNIDO experience (online access), United Nations Industrial Development Organization, Working Paper 3/2010, 2010.
  • Kelley, K., Lai, K> and Wu, P.-J.: Using R for data analysis - A best practice for research (online access), "Best Practices in Quantitative Methods", Osbourne, J. (ed.), pp.535–572, Sage, 2008.
  • Google's R Style guide.