Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2020 | nr 7 | 65--79
Tytuł artykułu

Stimulators of Innovation in Official Statistics

Warianty tytułu
Stymulatory innowacji w statystyce publicznej
Języki publikacji
It is worth considering what factors stimulate innovation in official statistics. The interaction of these factors causes official statistics to provide users with more accurate and user-friendly information. Innovation is a concept that has become commonplace. It is used in mass media, e.g. when advertising various products, and there are numerous scientific surveys and research work on this subject, pertaining to various fields. The term 'in-novation' comes from the Latin word 'innovatio', which means renewal. Its meaning comprises everything that is new, from technical improvements, through techno-logical advancement and organisational changes in various structures, local and global communication, media and fashion, to new ways of thinking.The term was defined and introduced into economics by Schumpeter (1912), thus indicating five instances of the occurrence of innovations:• creating a new product;• application of new technology, production methods;• creating a new market; • acquiring unknown raw materials;• reorganisation of a specific branch of the economy. (fragment of text)
Opis fizyczny
  • Główny Urząd Statystyczny; Szkoła Główna Handlowa w Warszawie
  • Alleva, G. (2017). Data Innovation in Official Statistics: the Leading Role of OpenData. Cape Town: UN World Data Forum, Congress13Jan_2017.pdf.
  • Baldacci, E., Stylianidou, N., Buono, D. (2017). Innovation in official statistical production - and multi-source statistical produciton. Eurostat,
  • Baesens, B. (2014). Analytics in a Big Data World: The Essential Guide to Data Science and its Applications. Hoboken, New York: Wiley.
  • Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. PhilosophicalTransactions of the Royal Society, 53, 370-418.
  • Bayes, T. (1958). An Essay Towards Solving a Problem in the Doctrine of Chances (with biographical note by G. A. Barnard). Biometrika, 45, 293-315.
  • Beręsewicz, M. (2019). Correlates of Representation Errors in Internet Data Sources for Real Estate Market. Journal of Official Statistics, 35(3), 509-529. DOI: 10.2478/jos-2019-0022.
  • Beręsewicz, M., Lehtonen, R., Reis, F., Di Consiglio, L., Karlberg, M. (2018). An overview of methods for treating selectivity in big data sources. Statistical Working Papers. Luxembourg: Eurostat.
  • Daas, P. J., Puts, M. J., Buelens, B., van den Hurk, P. A. (2015). Big data as a source for official statistics. Journal of Official Statistics, 31(2), 249-262.
  • De Finetti, B. (1951). Recent Suggestions for the Reconciliation of Theories of Probability. In: J. Neyman (ed.). Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability (p. 217-225). Berkeley: University of California Press.
  • Dillman, D. A. (1996). Why Innovation is Difficult in Government Surveys. Journal of Official Statistics, 12(2), 113-124.
  • Fisher, R. A. (1915). Frequency Distribution of the Values of the Correlation Coefficient in Samples from an Indefinitely Large Population. Biometrika, 10(4), 507-521.
  • Fisher, R. A. (1925). Statistical Methods for Research Workers. Edinburgh: Oliver & Boyd.
  • Fisher, R. A. (1930). Inverse Probability. Proceedings of the Cambridge Philosophical Society, 26(4), 528-535.
  • Fisher, R. A. (1935). The Design of Experiments. Edinburgh: Oliver & Boyd.
  • Fisher, R. A. (1939). A Note on Fiducial inference. Annals of Mathematical Statistics, 10(4), 383-388.
  • Fisher, R. A. (1950). Contributions to mathematical statistics. New York: Wiley.
  • Fisher, R. A. (1956). Statistical Methods and Scientific Inference. Edinburgh: Oliver & Boyd.
  • Jeffreys, H. (1931). Scientific Inference. Cambridge: Cambridge University Press.
  • Jeffreys, H. (1933). On the Prior Probability in the Theory of Sampling. Proceedings of the Cambridge Philosophical Society, 29, 83-87.
  • Jeffreys, H. (1934). Probability and Scientific Method. Royal Society of London. Proceedings. Series B. Biological Sciences, 146A(856), 9-16.
  • Kiaer, A. N. (1895). Observations et expériences concernant des dénombrements représentatives. Bulletin of the International Statistical Institute, 9, 176-183.
  • Kiaer, A. N. (1897). The Representative Method of Statistical Surveys. In: Reprint of Kiaer's paper from the Norwegian Academy of Science and Letters, 1997. Oslo: Statistics Norway.
  • Kiaer, A. N. (1905). Untitled speech with discussion. Bulletin of the International Statistical Institute, 14, 119-134.
  • Kish, L. (1995). 'The Hundred Years' War of Survey Sampling. Statistics in Transition, 2(5), 813- 830.
  • Kish, L. (2002). New Paradigms (Models) for Probability Sampling. Survey Methodology, 28, 31-34.
  • Kuhn, T. S. (1962). The Structure of Scientific Revolutions. Chicago: University of Chicago Press.
  • Kuusela, V. (2011). Paradigms in Statistical Inference for Finite Populations Up to the 1950. Helsinki: Statistics Finland.
  • Leavitt, H. J., Whisler, T. L. (1958). Management in the 1980s. Harvard Business Review, (4).
  • Laplace, P. S. (1774). Mémoire sur la probabilité des causes par les événements. In: P. S. Laplace, Oeuvres completes de Laplace, vol. 8 (p. 27-65). Paris, /bpt6k77596b/f32.
  • Laplace, P. S. (1781). Mémoire sur les probabilités. In: P. S. Laplace, Oeuvres completes de Laplace, vol. 9 (p. 383-485). Paris,
  • Lehtonen, R., Pahkinen, E., Särndal, C.-E. (2002). Research and Development in Official Statistics and Scientific Co-operation with Universities: An Empirical Investigation. Journal of Official Statistics, 18(4), 87-110.
  • Lehtonen, R., Särndal, C.-E. (2009). Research and Development in Official Statistics and Scientific Co-operation with Universities: A Follow-Up Study. Journal of Official Statistics, 25(4), 467- 482.
  • Lindley, D. V. (1958). Fiducial Distributions and Bayes' Theorem. Journal of the Royal Statistical Society: Series B, 20(1), 102 -107.
  • Lindley, D. V. (2004). Bayesian Thoughts (An Interview with Helen Joyce). Significance, 1, 73-75.
  • Lindley, D. V., Smith, A. F. (1972). Bayes Estimates for the Linear Model. Journal of the Royal Statistical Society: Series B, 34, 1-41.
  • Neyman, J. (1933). Zarys teorii i praktyki badania struktury ludności metodą reprezentacyjną. Warszawa: Instytut Spraw Społecznych.
  • Neyman, J. (1934). On the Two Different Aspects of the Representative Method: The Method of Stratified Sampling and the Method of Purposive Selection. Journal of the Royal Statistical Society, 97(4), 558-625.
  • Neyman, J. (1935). On the Problem of Confidence Intervals. The Annals of Mathematical Statistics, 6, 111-116.
  • Neyman, J. (1937). Outline of a Theory of Statistical Estimation Based on the Classical Theory of Probability. Philosophical Transactions of the Royal Society of London A, 236, 333-380.
  • Neyman, J. (1938). Contributions to the Theory of Sampling Human Populations. Journal of the American Statistical Association, 33, 101-116.
  • Neyman, J. (1952). Recognition of priority. Journal of the Royal Statistical Society, 115.
  • Neyman, J. (1971). Foundations of the Behaviouristic Statistics. In: V. P. Godambe, D. A. Sprott (ed.), Foundations of Statistical Inference (p. 1-13). Toronto: Holt, Rinehart and Winston of Canada.
  • OECD, Eurostat. (2018). Oslo Manual 2018: Guidelines for Collecting, Reporting and Using Data on Innovation. Paris, Luxembourg: OECD Publishing - Eurostat. DOI: 10.1787/9789264304604-en.
  • Pfeffermann, D. (2015). Methodological Issues and Challenges in the Production of Official Statistics: 24th Annual Morris Hansen Lecture. Journal of Survey Statistics and Methodology, 3(4), 425-483.
  • Pratt, J. W. (1965). Bayesian Interpretation of Standard Inference Statements. Journal of the Royal Statistical Society: Series B, 27, 169-203.
  • Robbins, H. (1964). The Empirical Bayes Approach to Statistical Decision Problems. Annals of Mathematical Statistics, 35, 1-20.
  • Savage, L. J. (1954). The Foundations of Statistics. New York: Wiley.
  • Savage, L. J. (1962). The Foundations of Statistical Inference. A Discussion. London: G. Barnard and D. R. Cox.
  • Schumpeter, J. A. (1912). Theorie der wirtschaftlichen Entwicklung. Leipzig: Duncker und Humblot.
  • Stigler, S. M. (1982). Thomas Bayes' Bayesian Inference. Journal of the Royal Statistical Society: Series A, 145, 250-258.
  • Stigler, S. M. (1983). Who Discovered Bayes' Theorem? American Statistician, 37(4a), 290-296.
  • Szreder, M. (2013). Twierdzenie Bayesa po 250 latach. Wiadomości Statystyczne, (12), 23-26.
  • Szreder, M. (2017). Nowe źródła informacji i ich wykorzystywanie w podejmowaniu decyzji. Wiadomości Statystyczne, (7)¸ 5-17.
  • Wald, A. (1939). Contributions to the Theory of Statistical Estimation and Testing. A Hypotheses. Annals of Mathematical Statistic, 10, 299-326.
  • Wald, A. (1950). Statistical Decision Functions. New York: Wiley.
  • Wallgren, A., Wallgren, B. (2007). Register-based Statistics. Administrative Data for Statistical Purposes. New York: Wiley.
  • Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. New York: Wiley.
Typ dokumentu
Identyfikator YADDA

Zgłoszenie zostało wysłane

Zgłoszenie zostało wysłane

Musisz być zalogowany aby pisać komentarze.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.