A general approach for imputation of non-normal continuous data based on copula transformation-Reference-Cited by-同舟云学术

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A general approach for imputation of non-normal continuous data based on copula transformation

Published:2022-02-21 Issue: Volume: Page:1-28
ISSN:0361-0918
Container-title:Communications in Statistics - Simulation and Computation
language:en
Short-container-title:Communications in Statistics - Simulation and Computation

Author:

Lun Zhixin¹^ORCID,Khattree Ravindra²^ORCID

Affiliation:

1. Medication Outcomes Center, Department of Clinical Pharmacy, University of California, San Francisco, California, USA

2. Department of Mathematics and Statistics and Center for Data Science and Big Data Analytics, Oakland University, Rochester, Michigan, USA

Funder

Oakland University

Publisher

Informa UK Limited

Subject

Modeling and Simulation,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/03610918.2022.2025839

Reference32 articles.

1. A generic all-purpose transformation for multivariate modeling through copulas

2. An Analysis of Transformations

3. On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario

4. Imputing continuous data under some non-Gaussian distributions

5. Maximum Likelihood from Incomplete Data Via theEMAlgorithm

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1. Joint Distributions;Synthesis Lectures on Engineering, Science, and Technology;2024

2. Best practices for addressing missing data through multiple imputation;Infant and Child Development;2023-02-23

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