<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this article, we share some reflections on the state of statistical science and its evolution in the production systems of official statistics. We first try to make a synthesis of the evolution of statistical thinking. We then examine the evolution of practices in official statistics, which had to face very early on a diversification of sou rces: first with the use of censuses, then sample surveys and finally administrative files. At each stage, a profound revision of methods was necessary. We show that since the middle of the 20th century, one of the major challenges of statistics has been to produce estimates from a variety of sources. To do this, a large number of methods have been proposed which are based on very different f oundations. The term “big data” encompasses a set of sources and new statistical methods. We first examine the potential of valorization of big data in official statistics. Some applications such as image analysis for agricultural prediction are very old and will be further developed. However, we report our skepticism towards web-scrapping methods. Then we examine the use of new deep learning methods. With access to more and more sources, the great challenge will remain the valorization and harmonization of these sources.</p>
</abstract>

In this article, we share some reflections on the state of statistical science and its evolution in the production systems of official statistics. We first try to make a synthesis of the evolution of statistical thinking. We then examine the evolution of practices in official statistics, which had to face very early on a diversification of sou rces: first with the use of censuses, then sample surveys and finally administrative files. At each stage, a profound revision of methods was necessary. We show that since the middle of the 20th century, one of the major challenges of statistics has been to produce estimates from a variety of sources. To do this, a large number of methods have been proposed which are based on very different f oundations. The term “big data” encompasses a set of sources and new statistical methods. We first examine the potential of valorization of big data in official statistics. Some applications such as image analysis for agricultural prediction are very old and will be further developed. However, we report our skepticism towards web-scrapping methods. Then we examine the use of new deep learning methods. With access to more and more sources, the great challenge will remain the valorization and harmonization of these sources.

Some Thoughts on Official Statistics and its Future (with discussion)

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Some Thoughts on Official Statistics and its Future (with discussion)"}

In this article, we share some reflections on the state of statistical science and its evolution in the production systems of official statistics. We first try...

Some Thoughts on Official Statistics and its Future (with discussion)

Data publikacji: 14 cze 2022

Zakres stron: 557 - 598

Otrzymano: 01 wrz 2020

Przyjęty: 01 lut 2021

DOI: https://doi.org/10.2478/jos-2022-0026

Słowa kluczowe
Deduction, foundations, induction, Lasso, -value, registers, sampling, statistical learning

© 2022 Yves Tillé et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Some Thoughts on Official Statistics and its Future (with discussion)

Data publikacji: 14 cze 2022

Zakres stron: 557 - 598

Otrzymano: 01 wrz 2020

Przyjęty: 01 lut 2021

DOI: https://doi.org/10.2478/jos-2022-0026

Słowa kluczoweDeduction, foundations, induction, Lasso, -value, registers, sampling, statistical learning

© 2022 Yves Tillé et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
Deduction, foundations, induction, Lasso, -value, registers, sampling, statistical learning