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Multiply imputing missing values in data sets with mixed measurement scales using a sequence of generalised linear models.

, and . Comput. Stat. Data Anal., (2016)

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Multiply imputing missing values in data sets with mixed measurement scales using a sequence of generalised linear models., and . Comput. Stat. Data Anal., (2016)A Framework for Auditable Synthetic Data Generation., , , , , , , and . CoRR, (2022)Learning from data with structured missingness., , , , , , , , , and 10 other author(s). CoRR, (2023)Data privacy preserving scheme using generalised linear models., , , , , and . Comput. Secur., (2017)On Integrating the Number of Synthetic Data Sets m into the a priori Synthesis Approach., , , and . PSD, volume 13463 of Lecture Notes in Computer Science, page 205-219. Springer, (2022)Estimating Risks of Identification Disclosure in Partially Synthetic Data., and . J. Priv. Confidentiality, (2009)Adjusting Survey Weights When Altering Identifying Design Variables Via Synthetic Data., and . Privacy in Statistical Databases, volume 4302 of Lecture Notes in Computer Science, page 177-188. Springer, (2006)Using mixtures of t densities to make inferences in the presence of missing data with a small number of multiply imputed data sets., , and . Comput. Stat. Data Anal., (2015)Confidentiality challenges in releasing longitudinally linked data., , , , and . Trans. Data Priv., 13 (2): 151-170 (2020)Statistical Disclosure Control for Data Privacy Using Sequence of Generalised Linear Models., , , , , and . ACISP (1), volume 9722 of Lecture Notes in Computer Science, page 77-93. Springer, (2016)