Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Robin Mitra"'
Publikováno v:
International Journal of Population Data Science, Vol 7, Iss 3 (2022)
Objectives Generation of synthetic data could improve the efficiency of administrative data analysis. We describe barriers and facilitators to synthetic administrative data in the UK based on our experience of generating, assessing, and evaluating th
Externí odkaz:
https://doaj.org/article/a4a6b745c826467ca14cd20593713227
Autor:
Theodora Kokosi, Bianca De Stavola, Robin Mitra, Lora Frayling, Aiden Doherty, Iain Dove, Pam Sonnenberg, Katie Harron
Publikováno v:
International Journal of Population Data Science, Vol 7, Iss 1 (2022)
Use of administrative data for research and for planning services has increased over recent decades due to the value of the large, rich information available. However, concerns about the release of sensitive or personal data and the associated disclo
Externí odkaz:
https://doaj.org/article/0f146167316d45c1940d7868c8d43ea4
Autor:
Jerome P. Reiter, Robin Mitra
Publikováno v:
The Journal of Privacy and Confidentiality, Vol 1, Iss 1 (2009)
To limit disclosures, statistical agencies and other data disseminators can release partially synthetic, public use microdata sets. These comprise the units originally surveyed; but some collected values, for example, sensitive values at high risk of
Externí odkaz:
https://doaj.org/article/3e43326ad4b04b6e888ab0ddb85bdcb8
Autor:
Robin Mitra, Sarah F. McGough, Tapabrata Chakraborti, Chris Holmes, Ryan Copping, Niels Hagenbuch, Stefanie Biedermann, Jack Noonan, Brieuc Lehmann, Aditi Shenvi, Xuan Vinh Doan, David Leslie, Ginestra Bianconi, Ruben Sanchez-Garcia, Alisha Davies, Maxine Mackintosh, Eleni-Rosalina Andrinopoulou, Anahid Basiri, Chris Harbron, Ben D. MacArthur
Publikováno v:
Nature Machine Intelligence. 5:13-23
Missing data are an unavoidable complication in many machine learning tasks. When data are `missing at random' there exist a range of tools and techniques to deal with the issue. However, as machine learning studies become more ambitious, and seek to
Publikováno v:
Journal of the Royal Statistical Society Series A: Statistics in Society. 185:1613-1643
Over the past three decades, synthetic data methods for statistical disclosure control have continually evolved, but mainly within the domain of survey data sets. There are certain characteristics of administrative databases, such as their size, whic
Autor:
Theodora Kokosi, Bianca De Stavola, Robin Mitra, Lora Frayling, Aiden Doherty, Iain Dove, Pam Sonnenberg, Katie Harron
Use of administrative data for research and for planning services has increased over recent decades due to the value of the large, rich information available. However, concerns about the release of sensitive or personal data and the associated disclo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fa76c446b7e805d40189a3958368fadb
https://doi.org/10.23889/ijpds.v7i1.1727
https://doi.org/10.23889/ijpds.v7i1.1727
Autor:
Sarah McGough, Robin Mitra
Missing data posed an inevitable complication in many machine learning tasks in the past. When data were missing at random, various tools and techniques were available to address the issue. However, as machine learning studies became more ambitious a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::00f4d6617b4772db143758d8ab537698
Autor:
Robin Mitra
Publikováno v:
Biometrical journal. Biometrische ZeitschriftREFERENCES.
Analysts often estimate treatment effects in observational studies using propensity score matching techniques. When there are missing covariate values, analysts can multiply impute the missing data to create m completed data sets. Analysts can then e
Publikováno v:
Biometrical Journal. 62:1192-1207
In this article, we address a missing data problem that occurs in transplant survival studies. Recipients of organ transplants are followed up from transplantation and their survival times recorded, together with various explanatory variables. Due to
Publikováno v:
Privacy in Statistical Databases ISBN: 9783031139444
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::42faa9182e8882515dfddd5e73d7788a
https://doi.org/10.1007/978-3-031-13945-1_15
https://doi.org/10.1007/978-3-031-13945-1_15