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Analysis

Advanced Tables - Table Plus

columnStyles
autoNumber	true

Report

Theme	Title	Country/Organisation	Topic	Reference

Poster Canada Crop


Other	Not available	Other	ML techniques	Schnaubelt, Mahias (2019) : A comparison of machine learning model validation schemes for non-stationary time series data, FAU Discussion Papers in Economics, No. 11/2019, Friedrich-Alexander-Universitat Erlangen-N ¨ urnberg, Institute for Economics, N ¨ urnberg. hp ://hdl.handle.net/10419/209136

Canada


Coding & Classification	Industry and Occupation Coding	Canada	ML application	Justin J. Evans, Isaac Ross, Julie Portelance. StatisticsCanada_CCHS_ML_Production_Report. [Online] 2020. https://statswiki.unece.org/display/MLP/Working+documents?preview=/244092601/256970399/Statistics_Canada_FastText_Techniques_Report.pdf

Canada

Coding & Classification

Industry and Occupation Coding

Canada

ML code and data

https://github.com/UNECE/CodingandClassification_Statcan

Canada


Coding & Classification	Industry and Occupation Coding	Canada	ML techniques	YanPeng Gao, Isaac Ross, Justin J. Evans. Statistiscs_Canada_FastText_Techniques_Report. [Online] 2019. https://statswiki.unece.org/download/attachments/244092601/Statistics_Canada_FastText_Techniques_Report.pdf?version=2&modificationDate=1567626783886&api=v2

Flanders

Coding & Classification

Sentiment Analysis of twitter data

Belgium Flanders

ML code

https://github.com/jmaslankowski/WP7-Population-Life-Satisfaction

Flanders


Coding & Classification	Sentiment Analysis of twitter data	Belgium Flanders	ML code	https://github.com/mireusen/hlmos-statistiek-vlaanderen-twitter

Flanders


Coding & Classification	Sentiment Analysis of twitter data	Belgium Flanders	ML code	https://github.com/wimulkeman/dutch-sentiment-analysis

Flanders


Coding & Classification	Sentiment Analysis of twitter data	Belgium Flanders	ML model	https://github.com/wietsedv/bertje/blob/master/README.md

Flanders

Coding & Classification

ML model

Sentiment Analysis of twitter data

Belgium Flanders

ML model

https://tfhub.dev/google/universal-sentence-encoder-multilingual-large/3

Poland


Coding & Classification	Production description to ECOICOP	Poland	ML code	https://colab.research.google.com/drive/1Epn2NeFRuFC_XyXtQ4qezGVBA5aAzqIh

Poland


Coding & Classification	Production description to ECOICOP	Poland	ML code and data	https://github.com/statisticspoland/ecoicop_classification

Poland

Coding & Classification

Production description to ECOICOP

Poland

ML library

https://scikit-learn.org/stable/index.html

Poster FlandersCoding & Classification


Coding & Classification	Not available	Other	ML application	https://www.cbs.nl/nl-nl/over-ons/innovatie/project/innovatieve-hotspots

Theme report

Coding & Classification	WP1 - Theme 1 Coding and Classification Report	Theme report	ML library	https://en.wikipedia.org/wiki/FastText
Coding & Classification	WP1 - Theme

report

1 Coding

&

and Classification Report

Theme report

ML tutorial

https://machinelearningmastery.com/types-of-classification-in-machine-learning/

Theme reportCoding & Classification

Coding & Classification

WP1 - Theme 1 Coding and Classification Report

Theme report

ML tutorial

https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/

Theme report

Coding & Classification

Naive Bayes

WP1 - Theme 1 Coding and Classification Report	Theme report	Naive Bayes	https://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/
Coding & Classification	WP1 - Theme

report

1 Coding

&

and Classification Report	Theme report	Random Forest	https://builtin.com/data-science/random-forest-algorithm
Coding & Classification	WP1 - Theme

report

1 Coding

& Coding & Classification

and Classification Report

Theme report

Random Forest

https://towardsdatascience.com/understanding-random-forest-58381e0602d2

Theme report

Coding & Classification	WP1 - Theme 1 Coding and Classification Report	Theme report	Subject matter	https://www.ons.gov.uk/methodology/classificationsandstandards/standardoccupationalclassificationsoc/soc2010/soc2010volume2thestructureandcodingindex#electronic-version-of-the-index
Coding & Classification	WP1 - Theme

report

1 Coding

&

and Classification Report

Theme report

XGBoost

https://machinelearningmastery.com/gentle-introduction-xgboost-applied-machine-learning/

US BLS


Coding & Classification	Automatic coding of occupation and industry in social statistical surveys	US BLS	ML application	https://www.bls.gov/iif/deep-neural-networks.pdf

US BLS

Coding & Classification

ML

Automatic coding of occupation and industry in social statistical surveys

US BLS

ML application

https://www.bls.gov/iif/deep-neural-networks.pdf

US BLS


Coding & Classification	Automatic coding of occupation and industry in social statistical surveys	US BLS	ML application	https://www.bls.gov/osmr/research-papers/2014/pdf/st140040.pdf

US BLS


Coding & Classification	Automatic coding of occupation and industry in social statistical surveys	US BLS	ML application	https://www.bls.gov/osmr/research-papers/2014/pdf/st140040.pdf

US BLS


Coding & Classification	Automatic coding of occupation and industry in social statistical surveys	US BLS	ML code	https://github.com/USDepartmentofLabor/soii_neural_autocoder

US BLS


Coding & Classification	Automatic coding of occupation and industry in social statistical surveys	US BLS	ML tutorial	https://github.com/ameasure/autocoding-class/blob/master/machine_learning.ipynb

Extra

Edit & Imputation

Not available

Other

Terminology

https://www.analyticsvidhya.com/glossary-of-common-statistics-and-machine-learning-terms/

Germany

Edit & Imputation

Bayesian

Machine learning for imputation

Germany

Bayesian Networks

Cheng J., Greiner R., Kelly J., Bell D. A., & Liu W. (2002). Learning Bayesian Networks from Data: An Information-Theory Based Approach. Artificial Intelligence, 137, 43–90.

Germany


Edit & Imputation	Machine learning for imputation	Germany	Bayesian Networks	Di Zio M., Sacco G., Scanu M., & Vicard P. (2004). Multivariate Techniques for Imputation Based on Bayesian Networks. Compstat 2004 Symposium.

Germany


Edit & Imputation	Machine learning for imputation	Germany	Bayesian Networks	Di Zio M., Scanu M., Coppola L., Luzi O., & Ponti A. (2004). Bayesian Networks for Imputation. Journal of the Royal Statistical Society Series A, 167(2), 309–322.

Germany

Edit & Imputation

Machine learning for imputation

Germany

Bayesian Networks

Jensen F. V. & Nielsen T. D. (2007). Bayesian Networks and Decision Graphs. Second edition. Springer.

Germany


Edit & Imputation	Machine learning for imputation	Germany	Bayesian Networks	Kalisch M., Bühlmann P. (2007). Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm. Journal of Machine Learning Research, 8, 613–636.

Germany

Edit & Imputation

Machine learning for imputation

Germany

Bayesian Networks

Lauritzen S. L. (1995). The EM Algorithm for Graphical Association Models With Missing Data. Computational Statistics and Data Analysis, 19, 191–201.

Germany


Edit & Imputation	Machine learning for imputation	Germany	Bayesian Networks	Moore A. & Wong W. (2003). Optimal Reinsertion: A New Search Operator for Accelerated and More Accurate Bayesian Network Structure Learning. In Proceedings of the Twentieth International Conference on Machine Learning (ICML 2003), 552–559.

Germany


Edit & Imputation	Machine learning for imputation	Germany	Bayesian Networks	Rey del Castillo P. (2012). Use of Machine Learning Methods to Impute Categorical Data. Conference of European Statisticians WP. 37.

Germany

Edit & Imputation

Machine learning for imputation

Germany

Bayesian Networks

Riggelsen C. (2006). Learning parameters of Bayesian networks from incomplete data via importance sampling. International Journal of Approximate Reasoning, 42(1-2), 69–83.

Germany


Edit & Imputation	Machine learning for imputation	Germany	Bayesian Networks	Spirtes P., Glymour C., & Scheines R. (2000). Causation, prediction, and search. Second edition. MIT Press.

Germany

Edit & Imputation

Bayesian Networks

Machine learning for imputation

Germany

Bayesian Networks

Tsamardinos I., Brown L. E., & Aliferis C. F. (2006). The Max-Min Hill-Climbing Bayesian Network Structure Learning Algorithm. Machine Learning, 65, 31–78.

Germany


Edit & Imputation	Machine learning for imputation	Germany	K-nearest neighbour	Beretta L. & Santaniello A. (2016). Nearest Neighbor Imputation Algorithms: A Critical Evalutation. Medical Informatics and Decision Making, 16, 197–208.

Germany


Edit & Imputation	Machine learning for imputation	Germany	K-nearest neighbour	Cucala L., Marin J. M., Robert C. P., & Titterington D. M. (2009). A Bayesian Reassessment of Nearest-Neighbor Classification. Journal of the American Statistical Association, 104, 263–273.

Germany

Edit & Imputation

Machine learning for imputation

Germany

K-nearest neighbour

Devroye L., Györfi L., & Lugosi G. (1996). A Probabilistic Theory of Pattern Recognition. Springer.

Germany


Edit & Imputation	Machine learning for imputation	Germany	K-nearest neighbour	Liao S. G., Lin Y., Kang D. D., Chandra D., Bon J., Kaminski N., Sciurba F. C., & Tseng G. C. (2014). Missing Value Imputation in High-Dimensional Phenomic Data: Imputable or not, and how? Bioinformatics, 15, 346.

Germany

Edit & Imputation

Machine learning for imputation

Germany

K-nearest neighbour

Troyanskaya O., Cantor M., Sherlock G., Brown P. O., Hastie T., Tibshirani R., Botstein D., & Altman R. B. (2001). Missing Value Estimation Methods for DNA Microarrays. Bioinformatics, 17, 520–525.

Germany


Edit & Imputation	Machine learning for imputation	Germany	ML application	Beck M., Dumpert F., & Feuerhake J. (2018). Proof of Concept Machine Learning – Abschlussbericht. Online available on: https://www.destatis.de/GPStatistik/receive/DEMonografie_monografie_00004835 (in German)

Germany


Edit & Imputation	Machine learning for imputation	Germany	ML application	Bertsimas D., Pawlowski C., & Zhuo Y. D. (2017). From predictive methods to missing data imputation: an optimization approach. The Journal of Machine Learning Research, 18(1), 7133–7171.

Germany

Edit & Imputation

Machine learning for imputation

Germany

ML application

Park S., Pannekoek J., & van der Loo M. P. J. (2018). Imputation of Economic Data based on Random Forest. Technical Report. Online available on statswiki.

Germany


Edit & Imputation	Machine learning for imputation	Germany	ML application	Richman M. B., Trafalis T. B., & Adrianto I. (2009). Missing data imputation through machine learning algorithms. In Artificial Intelligence Methods in the Environmental Sciences (pp. 153–169).

Germany

Edit & Imputation

ML application

Machine learning for imputation

Germany

ML application

Yang B., Janssens D., Ruan D., Bellemans T. & Wets G. (2013). A data imputation method with support vector machines for activity-based transportation models. In Computational Intelligence for Traffic and Mobility (pp. 159–171).

Germany


Edit & Imputation	Machine learning for imputation	Germany	ML code	Crookston N. L. & Finley A. O. (2007). yaImpute: An R Package for kNN Imputation. Journal of Statistical Software, 23(10), 1–16.

Germany


Edit & Imputation	Machine learning for imputation	Germany	ML code	Mayer M. (2019). missRanger: Fast Imputation of Missing Values. Online: https://cran.r-project.org/web/packages/missRanger/index.html

Germany

Edit & Imputation

Machine learning for imputation

Germany

ML code

Scutari M. (2010). Learning Bayesian Networks with the bnlearn R Package. Journal of Statistical Software, 35(3), 1–22.

Germany


Edit & Imputation	Machine learning for imputation	Germany	ML code	Steinwart I. & Thomann P. (2017). liquidSVM: A Fast and Versatile SVM package. Online: https://arxiv.org/abs/1702.06899.

Germany

Edit & Imputation

Machine learning for imputation

Germany

ML code

van Buuren S. & Groothuis-Oudshoorn K. (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1–67.

Germany


Edit & Imputation	Machine learning for imputation	Germany	ML Code	Wright M. N. & Ziegler A. (2017). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. Journal of Statistical Software, 77(1), 1–17.

Germany


Edit & Imputation	Machine learning for imputation	Germany	ML techniques	Hamner B., Frasco M., & LeDell E. (2018). Metrics: Evaluation Metrics for Machine Learning. Online: https://CRAN.R-project.org/package=Metrics.

Germany

Edit & Imputation

Machine learning for imputation

Germany

ML techniques

Honghai F., Guoshun C., Cheng Y., Bingru Y., & Yumei C. (2005). A SVM regression based approach to filling in missing values. In International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (pp. 581–587).

Germany


Edit & Imputation	Machine learning for imputation	Germany	ML techniques	Mikhchi A., Honarvar M., Kashan N. E. J., & Aminafshar, M. (2016). Assessing and comparison of different machine learning methods in parent-offspring trios for genotype imputation. Journal of theoretical biology, 399, 148–158.

Germany

Edit & Imputation

ML techniques

Machine learning for imputation

Germany

ML techniques

Stekhoven D. J. & Buehlmann P. (2012). MissForest – non-parametric missing value imputation for mixed-type data. Bioinformatics, 28(1), 112–118.

Germany


Edit & Imputation	Machine learning for imputation	Germany	ML techniques	van Buuren S. (2018). Flexible Imputation of Missing Data. 2nd edition. CRC.

Germany


Edit & Imputation	Machine learning for imputation	Germany	ML tutorial	Torgo L. (2010). Data Mining with R, learning with case studies Chapman and Hall/CRC. Online: http://www.dcc.fc.up.pt/~ltorgo/DataMiningWithR.

Germany

Edit & Imputation

Machine learning for imputation

Germany

Not published

Dumpert F., Hansen M., Peters F., & Spies L. (2018). Bericht zur Maßnahme Machine Learning Methodik. Internal Paper, yet unpublished, in German.

Germany


Edit & Imputation	Machine learning for imputation	Germany	R library	//cran.r-project.org/

Germany

Edit & Imputation

Machine learning for imputation

Germany

Random Forest

Athey S., Tibshirani J., & Wager S. (2019). Generalized Random Forests. The Annals of Statistics, 47(2), 1148–1178.

Germany


Edit & Imputation	Machine learning for imputation	Germany	Random Forest	Biau G. & Scornet E. (2016). A random forest guided tour. Test, 25(2), 197–227.

Germany


Edit & Imputation	Machine learning for imputation	Germany	Random Forest	Breiman L. (2001). Random forests. Machine learning, 45(1), 5–32.

Germany

Edit & Imputation

Machine learning for imputation

Germany

Random Forest

Burgette L. F. & Reiter J. P. (2010). Multiple imputation for missing data via sequential regression trees. American journal of epidemiology, 172(9), 1070–1076.

Germany


Edit & Imputation	Machine learning for imputation	Germany	Random Forest	Caiola G. & Reiter J. P. (2010). Random Forests for Generating Partially Synthetic, Categorical Data. Trans. Data Privacy, 3(1), 27-42.

Germany

Edit & Imputation

Random Forest

Machine learning for imputation

Germany

Random Forest

Ding Y. & Simonoff J. S. (2010). An investigation of missing data methods for classification trees applied to binary response data. Journal of Machine Learning Research, 11, 131–170.

Germany


Edit & Imputation	Machine learning for imputation	Germany	Random Forest	Doove L. L., Van Buuren S., & Dusseldorp E. (2014). Recursive partitioning for missing data imputation in the presence of interaction effects. Computational Statistics & Data Analysis, 72, 92–104.

Germany


Edit & Imputation	Machine learning for imputation	Germany	Random Forest	Feelders, A. (1999). Handling missing data in trees: surrogate splits or statistical imputation? In European Conference on Principles of Data Mining and Knowledge Discovery (pp. 329–334).

Germany

Edit & Imputation

Machine learning for imputation

Germany

Random Forest

Mentch L. & Hooker G. (2016). Quantifying uncertainty in random forests via confidence intervals and hypothesis tests. Journal of Machine Learning Research, 17(1), 841–881.

Germany


Edit & Imputation	Machine learning for imputation	Germany	Random Forest	Reiter J. P. (2005). Using CART to generate partially synthetic public use microdata. Journal of Official Statistics, 21(3), 441–462.

Germany

Edit & Imputation

Machine learning for imputation

Germany

Random Forest

Saar-Tsechansky M. & Provost F. (2007). Handling missing values when applying classification models. Journal of Machine Learning Research, 8, 1623–1657.

Germany


Edit & Imputation	Machine learning for imputation	Germany	Random Forest	Wager S., Hastie T., & Efron B. (2014). Confidence intervals for random forests: The jackknife and the infinitesimal jackknife. Journal of Machine Learning Research, 15(1), 1625–1651.

Germany


Edit & Imputation	Machine learning for imputation	Germany	Statistics	Bankier M., Lachance M., & Poirier P. (2000). 2001 Canadian census minimum change donor imputation methodology. UNECE Work Session on Statistical Data Editing 2000, Working Paper No. 17. Online: http://www.unece.org/fileadmin/DAM/stats/documents/ece/ces/2000/10/sde/17.e.pdf

Germany

Edit & Imputation

Machine learning for imputation

Germany

Statistics

Breiman L. (2001). Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical science, 16(3), 199–231.

Germany


Edit & Imputation	Machine learning for imputation	Germany	Statistics	Chambers R. (2001). Evaluation Criteria for Statistical Editing and Imputation. Online available: https://www.cs.york.ac.uk/euredit/

Germany

Edit & Imputation

Statistics

Machine learning for imputation

Germany

Statistics

Little R. J. & Rubin D. B. (1987; 2002). Statistical analysis with missing data. Wiley.

Germany


Edit & Imputation	Machine learning for imputation	Germany	Statistics	Little R. J. (2011). Imputation. In: Lovric M., International Encyclopedia of Statistical Science. Springer.

Germany


Edit & Imputation	Machine learning for imputation	Germany	Statistics	Rubin D. B. (1987). Multiple imputation for nonresponse in surveys. Wiley.

Germany

Edit & Imputation

Machine learning for imputation

Germany

Support Vector Machine

Boser B. E., Guyon I. M., & Vapnik V. N. (1992). A training algorithm for optimal margin classifiers. Fifth Annual ACM Workshop on Computational Learning Theory, 144–152.

Germany


Edit & Imputation	Machine learning for imputation	Germany	Support Vector Machine	Chechik G., Heitz G., Elidan G., Abbeel P., & Koller D. (2007). Max-margin classification of incomplete data. In Advances in Neural Information Processing Systems (pp. 233–240).

Germany

Edit & Imputation

Machine learning for imputation

Germany

Support Vector Machine

Cortes C. & Vapnik V. N. (1995). Support-vector networks. Machine Learning, 20, 273–297.

Germany


Edit & Imputation	Machine learning for imputation	Germany	Support Vector Machine	Drechsler J. & Reiter J. P. (2011). An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets. Computational Statistics & Data Analysis, 55(12), 3232–3243.

Germany


Edit & Imputation	Machine learning for imputation	Germany	Support Vector Machine	Drechsler J. (2010). Using support vector machines for generating synthetic datasets. In International Conference on Privacy in Statistical Databases (pp. 148–161).

Germany


Edit & Imputation	Machine learning for imputation	Germany	Support Vector Machine	Hable R. (2012). Asymptotic normality of support vector machine variants and other regularized kernel methods. Journal of Multivariate Analysis, 106, 92–117.

Germany


Edit & Imputation	Machine learning for imputation	Germany	Support Vector Machine	Honghai F., Guoshun C., Cheng Y., Bingru Y., & Yumei C. (2005). A SVM regression based approach to filling in missing values. In International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (pp. 581–587).

Germany


Edit & Imputation	Machine learning for imputation	Germany	Support Vector Machine	Pelckmans K., De Brabanter J., Suykens J. A., & De Moor B. (2005). Handling missing values in support vector machine classifiers. Neural Networks, 18(5-6), 684–692.

Germany

Edit & Imputation

Machine learning for imputation

Germany

Support Vector Machine

Rogers S. D. (2012). Support Vector Machines for Classification and Imputation. Master thesis. Brigham Young University.

Germany


Edit & Imputation	Machine learning for imputation	Germany	Support Vector Machine	Smola A. J., Vishwanathan S. V. N., & Hofmann T. (2005). Kernel Methods for Missing Variables. In AISTATS 2005 – Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics (pp. 325–332).

Germany


Edit & Imputation	Machine learning for imputation	Germany	Support Vector Machine	Steinwart I. & Christmann A. (2008). Support Vector Machines. Springer.

Germany


Edit & Imputation	Machine learning for imputation	Germany	Support Vector Machine	Stewart T. G., Zeng D., & Wu M. C. (2018). Constructing support vector machines with missing data. Wiley Interdisciplinary Reviews: Computational Statistics, 10, 1–16.

Germany

Edit & Imputation

Machine learning for imputation

Germany

Support Vector Machine

Wen Z., Shi J., Li Q., He B., & Chen J. (2018). ThunderSVM: A fast SVM library on GPUs and CPUs. Journal of Machine Learning Research, 19(21), 1–5.

Germany


Edit & Imputation	Machine learning for imputation	Germany	Support Vector Machine	Yang B., Janssens D., Ruan D., Bellemans T., & Wets G. (2013). A data imputation method with support vector machines for activity-based transportation models. In Computational Intelligence for Traffic and Mobility (pp. 159-171).

Germany


Edit & Imputation	Machine learning for imputation	Germany	Support Vector Machine	Zhang Y. & Liu Y. (2009). Data imputation using least squares support vector machines in urban arterial streets. IEEE Signal Processing Letters, 16(5), 414–417.

Italy-E


Edit & Imputation	Machine Learning for Data Editing Cleaning in NSI : Some ideas and hints	Italy	ML application	Martin Beck, Florian Dumpert, Joerg Feuerhake (2018). Machine Learning in Official Statistics (Shorter English version available on arXiv: https://arxiv.org/abs/1812.10422)

Italy-E

Edit & Imputation

StandardsGSBPM

Machine Learning for Data Editing Cleaning in NSI : Some ideas and hints

Italy

Standards

GSBPM (2019). Generic Statistical Business Process Model. Version 5.1, January 2019, UNECE. Available at: https://statswiki.unece.org/display/GSBPM/Generic+Statistical+Business+Process+Model.

Italy-E


Edit & Imputation	Machine Learning for Data Editing Cleaning in NSI : Some ideas and hints	Italy	Standards	GSDEM (2019). Generic Statistical Data Editing Models - GSDEMs, Version 2.0, April 2019, UNECE. Available at: https://statswiki.unece.org/display/sde/GSDEM

Italy-E

Edit & Imputation

Machine Learning for Data Editing Cleaning in NSI : Some ideas and hints

Italy

Standards

GSIM (2019). Generic Statistical Information Model, Version 1.2, May 2019, UNECE. Available at: http://www1.unece.org/stat/platform/display/gsim.

Italy-E


Edit & Imputation	Machine Learning for Data Editing Cleaning in NSI : Some ideas and hints	Italy	Statistics	EDIMBUS (2007). Recommended Practices for Editing and Imputation in Cross-sectional Business Surveys, EDIMBUS project report, https://ec.europa.eu/eurostat/documents/64157/4374310/30-Recommended+Practices-for-editing-and-imputation-in-cross-sectional-business-surveys-2008.pdf.

Italy-E

Edit & Imputation

Statistics

Machine Learning for Data Editing Cleaning in NSI : Some ideas and hints

Italy

Statistics

MEMOBUST (2014). Handbook on Methodology of Modern Business Statistics, CROS-portal, Eurostat, https://ec.europa.eu/eurostat/cros/content/handbook-methodology-modern-business-statistics_en.

Italy-E


Edit & Imputation	Machine Learning for Data Editing Cleaning in NSI : Some ideas and hints	Italy	Statistics	Van der Loo M. (2015) A Formal Typology of Data Validation Functions, UNECE, Conference of European Statisticians, Budapest. Available at: http://www.markvanderloo.eu/files/statistics/WP_5_Netherlands_A_formal_typology_of_data_validation_functions.pdf

Italy-E


Edit & Imputation	Machine Learning for Data Editing Cleaning in NSI : Some ideas and hints	Italy	Statistics	Waal, T.de, Pannekoek, J. and Scholtus, S. (2011). Handbook of Statistical Data Editing and Imputation. Wiley, Hoboken.

Italy-I

Edit & Imputation

Imputation of the variable “Attained Level of Education” in Base Register of Individuals

Italy

ML application

[1] Di Zio M., Di Cecco D., Di Laurea D., Filippini R., Massoli P., Rocchetti G. “Mass imputation of the attained level of education in the Italian System of Registers”, Workshop on Statistical Data Editing, Neuchâtel, Switzerland, 18-20 September 2018

Italy-I

Edit & Imputation

Imputation of the variable “Attained Level of Education” in Base Register of Individuals

Italy

ML application

[2] Di Zio M., Filippini R., Rocchetti G. “An imputation procedure for the Italian attained level of education in the register of individuals based on administrative and survey data”, Workshop on Statistical Data Editing, Neuchâtel, Switzerland, 31 August - 2 September 2020

Italy-I

Edit & Imputation

Imputation of the variable “Attained Level of Education” in Base Register of Individuals

Italy

ML application

[3] Bernasconi, Eleonora, et al. "Satellite-Net: Automatic Extraction of Land Cover Indicators from Satellite Imagery by Deep Learning." arXiv preprint arXiv:1907.09423 (2019).

Italy-I

Edit & Imputation

Imputation of the variable “Attained Level of Education” in Base Register of Individuals

Italy

ML application

[4] De Fausti Fabrizio, Pugliese Francesco and Diego Zardetto. "Toward Automated Website Classification by Deep Learning." arXiv preprint arXiv:1910.09991 (2019).

Italy-I


Edit & Imputation	Imputation of the variable “Attained Level of Education” in Base Register of Individuals	Italy	ML code	https://github.com/defausti/MLP_Imputation.git

Italy-I


Edit & Imputation	Imputation of the variable “Attained Level of Education” in Base Register of Individuals	Italy	ML techniques	[6] Yoon, Jinsung, James Jordon, and Mihaela Van Der Schaar. "Gain: Missing data imputation using generative adversarial nets." arXiv preprint arXiv:1806.02920 (

2018).Italy-IEdit & Imputation

2018).
Edit & Imputation	Imputation of the variable “Attained Level of Education” in Base Register of Individuals	Italy	Statistics	[5] Cybenko, George. "Approximation by superpositions of a sigmoidal function." Mathematics of control, signals and systems 2.4 (1989): 303-314.

Poster Canada GenSyst


Edit & Imputation	Not available	Other	ML code	Stekhoven, D. J. (2015). missForest: Nonparametric missing value imputation using random forest. Astrophysics Source Code Library

Poster Canada GenSyst

Edit & Imputation

Not available

Other

Statistics

Gray, D. (2019). A Generalized Framework to Evaluate Imputation Strategies: Recent Developments. In JSM Proceedings, Government Statistics Section. Alexandria, VA: American Statistical Association. 1861-1870

Poster Canada GenSyst


Edit & Imputation	Not available	Other	Statistics	Gray, D. (2020). Evaluating Imputation Methods using ImpACT: First Case Study, United Nations Statistical Commission and Economic Commission for Europe – Workshop on Statistical Data Editing

Poster Canada GenSyst

Edit & Imputation	Not available	Other	Statistics	Stelmack, A. (2018). On the Development of a Generalized Framework to Evaluate and Improve Imputation Strategies at Statistics Canada, United Nations Statistical Commission and Economic Commission for Europe – Workshop on Statistical Data Editing.
Edit & Imputation	WP1 - Theme

report

2 Edit

&

and Imputation Report	Theme report	Data Science	Cao L. (2017). Data science: a comprehensive overview. ACM Computing Surveys, 50(3), 1–42.
Edit & Imputation	WP1 - Theme

report

2 Edit

&

and Imputation Report

Theme report

Statistics

Chambers R. (2001). Evaluation Criteria for Statistical Editing and Imputation.

VITOEdit & Imputation


Edit & Imputation	Early estimates of energy balance statistics using machine learning	Belgium VITO	Big Data	Daas, P.J.H., Puts, M.J., Buelens, B. and van den Hurk, P. (2015). Big data as a source for official statistics. Journal of Official Statistics, 31, 249–262.

VITO


Edit & Imputation	Early estimates of energy balance statistics using machine learning	Belgium VITO	Big Data	Hassani, H., Saporta, G. and Silva, E.S. (2014). Data mining and official statistics: the past, the present and the future. Big Data, 1, 34–43.

VITO


Edit & Imputation	Early estimates of energy balance statistics using machine learning	Belgium VITO	ML code	https://github.com/VITObelgium/energy-balance-ml

VITO


Edit & Imputation	Early estimates of energy balance statistics using machine learning	Belgium VITO	ML tutorial	Hastie, T., Tibshirani, R., Friedman, J. & Franklin, J. (2009). The Elements of Statistical Learning: Data Mining, Inference and Prediction, 2nd ed. New York: Springer.

VITO


Edit & Imputation	Early estimates of energy balance statistics using machine learning	Belgium VITO	Random Forest	Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

VITO


Edit & Imputation	Early estimates of energy balance statistics using machine learning	Belgium VITO	Statistics	Claeskens, G. & Hjort, N. L. (2008). Model Selection and Model Averaging. Cambridge: Cambridge University Press.

VITO

Edit &

Imputation

Imputation	Early estimates of energy balance statistics using machine learning	Belgium VITO	Statistics	Gelman, A. & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models, Vol. 1 New York: Cambridge University Press.
Imagery	Use of Landsat satellite data for the mapping of urban areas in non-census years	Mexico

Imagery

	Data	https://ieeexplore.ieee.org/document/8518312
Imagery	Use of Landsat satellite data for the mapping of urban areas in non-census years	Mexico

Imagery

Data	https://www.opendatacube.org/
Imagery	Learning statistical information from images: a proof of concept	Netherlands

Imagery

	Data	https://www.cbs.nl/nl-nl/dossier/nederland-regionaal/geografische-data/kaart-van-100-meter-bij-100-meter-met-statistieken
Imagery	Learning statistical information from images: a proof of concept	Netherlands

Imagery

Data	Persian cat, Model T, Granny Smith; http://image-net.org/challenges/LSVRC/2015/browse-synsets
Imagery	Arealstatistik Deep Learning (ADELE)	Switzerland

Imagery

ML application

https://www.bfs.admin.ch/bfs/de/home/statistiken/raum-umwelt/erhebungen/area.assetdetail

.5687737.html

.5687737.html
Imagery	WP1 - Theme 3 Imagery Analysis Report	Theme report

Imagery

	Big Data	Curzi, G., Modenini, D., & Tortora, P. (2020). Large Constellations of Small Satellites: A Survey of Near Future Challenges and Missions. Aerospace, 7, 133. doi:10.3390/aerospace7090133
Imagery	WP1 - Theme 3 Imagery Analysis Report	Theme report

Imagery

	Big Data	Safyan, M. (2020). Handbook of Small Satellites, Technology, Design, Manufacture, Applications, Economics and Regulation. 1057-1073. doi:10.1007/978-3-030-36308-664
Imagery	WP1 - Theme 3 Imagery Analysis Report	Theme report

Imagery

	Data	http://aws.amazon.com/es/public-data-sets/landsat/
Imagery	WP1 - Theme 3 Imagery Analysis Report	Theme report

Imagery

Data	http://landsat.gsfc.nasa.gov/?p=10221
Imagery	WP1 - Theme 3 Imagery Analysis Report	Theme report

Imagery

	Data	https://eur-lex.europa.eu/eli/reg_del/2013/1159/oj
Imagery	WP1 - Theme 3 Imagery Analysis Report	Theme report

Imagery

	Data	Toth, C., & Jóźków, G. (2016). Remote sensing platforms and sensors: A survey. ISPRS Journal of Photogrammetry and Remote Sensing, 22-36.
Imagery	WP1 - Theme 3 Imagery Analysis Report	Theme report

Imagery

	ML application	Ferreira, B., Iten, M., & Silva, R. G. (2020). Monitoring sustainable development by means of earth observation data and machine learning: a review. Environmental Sciences Europe, 32, 120. doi:10.1186/s12302-020-00397-4
Imagery	WP1 - Theme

reportImagery

3 Imagery Analysis Report	Theme report	ML application	Holloway, J., & Mengersen, K. (2018). Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals: A Review. Remote Sensing, 10, 1365. doi:10.3390/rs10091365
Imagery	WP1 - Theme 3 Imagery Analysis Report	Theme report

Imagery

	ML application	Youssef, R., Aniss, M., & Jamal, C. (2020). Machine Learning and Deep Learning in Remote Sensing and Urban Application: A Systematic Review and Meta-Analysis. Proceedings of the 4th Edition of International Conference on Geo-IT and Water Resources 2020, Geo-IT and Water Resources 2020. New York, NY, USA: Association for Computing Machinery. doi:10.1145/3399205.3399224
Imagery	WP1 - Theme 3 Imagery Analysis Report	Theme report

Imagery

ML techniques	Bishop, C. M. (2006). Pattern Recognition and Machine Learning. USA: Springer.
Imagery	Generic Pipeline for Production of Official Statistics Using Satellite Data and Machine Learning	UNECE

Imagery

Big Data	[1] Conference of European Statisticians (2019) In-depth Review on Satellite Imagery and Earth Observation Technology in Official Statistics
Imagery	Generic Pipeline for Production of Official Statistics Using Satellite Data and Machine Learning	UNECE

ImageryImagery

	Big Data	[1] United Nations Global Working Group on Big Data (2017) Satellite Imagery and Geospatial Data Task Team Report
Imagery

UNECE

Generic Pipeline for Production of Official Statistics Using Satellite Data and Machine Learning

UNECE

Big Data

Committee on Earth Observation Satellites (2015) Satellite Earth Observations in

Support of Climate Information ChallengesUNECEImageryImagery

Support of Climate Information Challenges
Imagery	Generic Pipeline for Production of Official Statistics Using Satellite Data and Machine Learning	UNECE	Data	[1] Lewis, A. et al. (2017) Remote Sensing of Environment
Imagery

UNECEImagery

Generic Pipeline for Production of Official Statistics Using Satellite Data and Machine Learning	UNECE	Data	[1] UCS Satellite Database (accessed Feb. 2020)
Imagery

UNECE

Generic Pipeline for Production of Official Statistics Using Satellite Data and Machine Learning	UNECE	Data	Roberts, D., Dunn, B. and Mueller, N. (2018) Open Data Cube Products Using High-Dimensional Statistics of Time Series
Imagery	Generic Pipeline for Production of Official Statistics Using Satellite Data and Machine Learning	UNECE

Imagery

	Standards	United Nations Economic Commission for Europe (2019) Generic Statistical Business Process Model (version 5.1)
Imagery	Generic Pipeline for Production of Official Statistics Using Satellite Data and Machine Learning	UNECE

Imagery

Statistics

[1] United Nations Statistics Division (2019) Guidelines on the use of electronic data collection technologies in population and housing censuses

WP2

Quality

WP2 Quality

Framework

Australian Bureau of Statistics (2005). Data Quality Framework, Australian Bureau of Statistics, (https://www.abs.gov.au/websitedbs/D3310114.nsf//home/Quality:+The+ABS+Data+Quality+Framework)

WP2

Quality

WP2 Quality

Framework

Eurostat (2017). European Statistics Code of Practice , Eurostat, https://ec.europa.eu/eurostat/web/quality/european-statistics-code-of-practice.

WP2

Quality

WP2 Quality

Framework

Statistics Canada (2017). Quality Assurance Framework, Statistics Canada, https://www150.statcan.gc.ca/n1/pub/12-539-x/12-539-x2019001-eng.htm

WP2

Quality

WP2 Quality

Framework

United Nation (2019). National Quality Assurance Frameworks Manual for Official Statistics, United Nations, https://unstats.un.org/unsd/methodology/dataquality/)

WP2

Quality

WP2 Quality

Framework

United Nations (2012). Guidelines for the template for a generic national quality assurance, United Nations, https://unstats.un.org/unsd/statcom/doc12/BG-NQAF.pdf.

WP2

Quality

WP2 Quality

ML application

Luque, A., Carrasco, A., Martín, A. and de las Heras, A. (2019). The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recognition, 91, 216–231.

WP2

Quality

WP2 Quality

ML application

Pepe, M.S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press.

WP2

Quality

WP2 Quality

ML application

Vanwinckelen, G. and Blockeel, H. (2014). Look before you leap: Some insights into learner evaluation with cross-validation. JMLR Workshop and Conference Proceedings, 1, 3–19.

WP2

Quality

WP2 Quality

ML techniques

Goldstein, A., Kapelner, A., Bleich, J., and Pitkin, E. (2014). Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation. arXiv

WP2

Quality

WP2 Quality

ML techniques

Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning. 2nd edition. Springer.

WP2

Quality

WP2 Quality

ML techniques

Japkowicz, N. and Shah, M. (2011).Evaluating Learning Algorithms.Cambridge University Press.

WP2

Quality

WP2 Quality

ML techniques

Stothard, C. (2020). Evaluating Machine Learning Classifiers: A review. Australian Bureau of Statistics, available upon request.

WP2

Quality

WP2 Quality

Practices

Arrieta, B.A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R. and Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115

WP2

Quality

WP2 Quality

Practices

Begley C, Ioannidis J. (2015). Reproducibility in science: Improving the standard for basic and preclinical research. Circ. Res. P 116-126.

WP2

Quality

WP2 Quality

Practices

Bhatt, U., Xiang, A., Sharma, S., Weller,A., Taly, A., Jia, Y., Ghosh, J., Puri, R., Moura, J.M.F. and Eckersley, P. (2020). Explainable machine learning in deployment. arXiv

WP2

Quality

WP2 Quality

Practices

Goodman, S., Fanelli, D. and Ioannidis, J. (2016). What does research reproducibility mean? Science Translational Medicine, p 341-353

WP2

Quality

WP2 Quality

Practices

Hanson, B., Sugden, A. and Alberts, B. (2011) Making data maximally available. Science, p 331-649.

WP2

Quality

WP2 Quality

Practices

Molnar (2019) Interpretable Machine Learning - A Guide for Making Black Box Models Explainable

WP2

Quality

WP2 Quality

Practices

Petkovic (2020) AI and trust: explainability, transparency. Ethical implications of AI and AI Tools Lab, Frankfurt Big Data Lab, Goethe University

WP2

Quality

WP2 Quality

Practices

Ribeiro, M.T., Singh, S. and Guestrin, C. (2016) “Why Should I Trust You?” Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144

WP2

Quality

WP2 Quality

Practices

Stodden, V., Seiler, J. and Ma, Z. (2018). An empirical analysis of journal policy effectiveness for computational reproducibility. Proc Natl Acad Sci USA p 2584–2589.

WP2

Quality

WP2 Quality

Practices

Szabo, L. (2019) Artificial intelligence is rushing into patient care—and could raise risks. Scientific American, December 2019

WP2

Quality

WP2 Quality

Practices

Vilone, G. and Longo, L. (2020) Explainable artificial intelligence: a systematic review. arXiv

WP2

Quality

WP2 Quality

Statistics

Bengio, Y. And Grandvalent, Y. (2004). No Unbiased Estimator of the Variance of K-Fold Cross-Validation. Journal of Machine Learning Research, 5, 1089–1105.

WP2

Quality

WP2 Quality

Statistics

Bickel, P. J. and Freedman, D. A. (1981). Some Asymptotic Theory for the Bootstrap. The Annals of Statistics, 9(6), 1196–1217.

WP2

Quality

WP2 Quality

Statistics

Biemer, P.P. (2010). Total Survey Error – Design, Implementation, and Evaluation. Public Option Quarterly, 74(5), 817–848.

WP2

Quality

WP2 Quality

Statistics

Borra, S. and Di Ciaccio, A. (2010). Measuring the prediction error. A comparison of cross-validation, bootstrap and covariance penalty methods. Computational Statistics and Data Analysis, 54, 2976–2989.

WP2

Quality

WP2 Quality

Statistics

DiCiccio, T. and Efron, B. (1996). Bootstrap confidence intervals. Statistical Science, p 189-212

WP2

Quality

WP2 Quality

Statistics

Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. The Annals of Statistics. 7(1), 1–26.

WP2

Quality

WP2 Quality

Statistics

Eurostat (2014). Handbook on Methodology of Modern Business Statistics, CROS-portal, MEMOBUST, https://ec.europa.eu/eurostat/cros/content/handbook-methodology-modern-business-statistics_en.

WP2

Quality

WP2 Quality

Statistics

Groves, R.M. and Lyberg, L. (2010). Total Survey Error – Past, Present, and Future. Public Opinion Quarterly, 74(5), 849–879.

WP2

Quality

WP2 Quality

Statistics

Hand D.J. (2012) Assessing the performance of classification methods. International Statistical Review. 80(3), 400–414.

WP2

Quality

WP2 Quality

Statistics

Kim, J.-H. (2009). Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap. Computational Statistics and Data Analysis, 53, 3735–3745.

WP2

Quality

WP2 Quality

Statistics

Platek, R. and Särndal, C.-E. (2001). Can a Statistician Deliver? Journal of Official Statistics, 17(1), 1–20.

WP2

Quality

WP2 Quality

Statistics

Quenouille, M.H. (1956). Notes on Bias in Estimation. Biometrika, 43, 353–60.

WP2

Quality

WP2 Quality

Statistics

Stone, M. (1974). Cross-validatory Choice and Assessment of Statistical Predictions. Journal of the Royal Society B, 36, 111–147.

WP2

Quality

WP2 Quality

Statistics

Wolter, K. M. (2007). Introduction to Variance Estimation.2nd edition.Springer.

Poster Canada RecLinkRecord Linkage


Other	Not available	Other	ML application	Christen, P. (2007). “A two-step Classification to Unsupervised Record Linkage”, in Proceedings of the 6-th Australian Conference on Data Mining and Analytics, 70, 111-119.

Poster Canada RecLinkRecord Linkage


Other	Not available	Other	ML library	De Bruin, J. (2019). “Python Record Linkage Toolkit: A toolkit for record linkage and duplicate detection in Python”. Zenodo. https://doi.org./10.5281/zenodo.3559043

Poster Canada RecLinkRecord Linkage


Other	Not available	Other	Statistics	Fellegi, I.P., and Sunter, A.B. (1969), ”A theory of record linkage”, Journal of the American Statistical Association, 64, 1183–1210

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