Statistical Methods & Applications - Call for Papers: Big Data and alternative data sources for Small Area Estimation
Guest Editors
Monica Pratesi (University of Pisa, Italy)
Roberta Siciliano (University of Naples Federico II, Italy)
Partha Lahiri (Joint Program in Survey Methodology, University of Maryland, College Park, USA)
Call
Planning and evaluation of government programs usually requires access to a huge amount of data concerning national, sub-national and supranational socio-economic, environment and health related statistics. There is, however, a growing need for statistics relating to much smaller geographical areas, where data are too sparse to support the sort of standard estimation methods typically employed at national level. These 'small area' official statistics are routinely used for a variety of purposes, including assessing economic well-being of a nation, making public policies, and allocating funds in various government program. With the rapid development of survey methodology, different governmental agencies are now exploring ways of combining national survey data with a variety of structured and unstructured data, including administrative, census records and Big Data to produce reliable small area statistics. The field of small area estimation (SAE) research is quickly expanding to meet this demand and is constantly tackling practical problems that are theoretically challenging.
As a follow-up of of the SAE2021 (this opens in a new tab), a Satellite Meeting of the International Statistical Institute 63rd World Statistics Congress, Naples, Italy, on September 20-24, 2021, this special issue of Statistical Methods and Applications is dedicated to collecting papers on cutting-edge methodological developments and/or unique applications to analyze the use of big data and alternative data sources in small area estimation. The papers should clearly introduce new methodologies and demonstrate their utility for the users of small area statistics and/or propose innovative applications where the use of big data sources in small area estimation is required and favourable.
Paper submission is not restricted to the participants of the Satellite Meeting of the International Statistical Institute 63rd World Statistics Congress, Naples, Italy, on September 20-24, 2021.
Methodological settings
- Spatial approaches and satellite imagery in SAE
- Computational Science for SAE
- Machine Learning for SAE
- Statistical Learning for SAE
- Social media digital fingerprints and SAE of social indicators
- Anonymized and aggregated mobile data for SAE
- Methods for indirect SAE using Big Data
- Inference for Big Data and nonprobability samples in SAE context
- SAE & Big Data in Official Statistics
- SAE using Big Data Sources
- SAE of indicators of education and household characteristics
- SAE and Big Data in poverty mapping
- SAE methods for time series data
- Other topics related to SAE and Big Data
Application domains
- Medicine, health, biology, epidemiology
- Economics, politics, environmental and social sciences
- Digital society and smart cities
- Equitable, sustainable development, sustainable finance
- Environmental protection, climate change
Tentative schedule
Submission of full paper: 30th June 2023
Submission guidelines
Papers should be formatted according to the Statistical Methods and Applications journal instructions for authors at:
https://www.springer.com/journal/10260 (this opens in a new tab)
Springer has LaTeX templates: see “Submission guidelines / Text” at:
https://www.springer.com/journal/10260/submission-guidelines (this opens in a new tab)
No templates for Word.
Submissions should be made in the usual way, online at http://www.springer.com/10260 (this opens in a new tab), where further guidance about the structure, length and format of manuscripts may be found. Authors should select ‘SI: Big Data and alternative data sources for Small Area Estimation’ during the submission step ‘Additional Information.’
All manuscripts will be peer-reviewed in line with the journal’s standard policy.