Evaluating Imputation Methods for rainfall data under high variability in Johor River Basin, Malaysia

Autor: Zulfaqar Sa’adi, Zulkifli Yusop, Nor Eliza Alias, Ming Fai Chow, Mohd Khairul Idlan Muhammad, Muhammad Wafiy Adli Ramli, Zafar Iqbal, Mohammed Sanusi Shiru, Faizal Immaddudin Wira Rohmat, Nur Athirah Mohamad, Mohamad Faizal Ahmad

Publikováno v: Applied Computing and Geosciences, Vol 20, Iss , Pp 100145- (2023)

Missing values in rainfall records might result in erroneous predictions and inefficient management practices with significant economic, environmental, and social consequences. This is particularly important for rainfall datasets in Peninsular Malays

Externí odkaz: https://doaj.org/article/67265cb74d7f4ef3ba34e3df26d8e54d

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BIMAM—a tool for imputing variables missing across datasets using a Bayesian imputation and analysis model

Autor: Alex Adamson, Jaymini Patel, James Potts, Fadlalla G. Elfadaly, Marta Blangiardo, Laura Potts, Cosetta Minelli, John F. Thompson

Publikováno v: International Journal of Epidemiology

Motivation Combination of multiple datasets is routine in modern epidemiology. However, studies may have measured different sets of variables; this is often inefficiently dealt with by excluding studies or dropping variables. Multilevel multiple impu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1db53114e5cec697c6093f90c43eaef2
http://oro.open.ac.uk/78970/3/78970.pdf

Framework for the treatment and reporting of missing data in observational studies : the Treatment And Reporting of Missing data in Observational Studies framework

Autor: Els Goetghebeur, Joseph W. Hogan, Melanie L. Bell, Katherine J Lee, Roderick J. A. Little, Kate Tilling, James R. Carpenter, Rosie Cornish

Publikováno v: JOURNAL OF CLINICAL EPIDEMIOLOGY
Journal of Clinical Epidemiology
Lee, K, Tilling, K M, Cornish, R P, Little, R, Bell, M, Goetghebeur, E, Hogan, J & Carpenter, J 2021, ' Framework for the Treatment And Reporting of Missing data in Observational Studies : The Treatment And Reporting of Missing data in Observational Studies framework ', Journal of Clinical Epidemiology, vol. 134, pp. 79-88 . https://doi.org/10.1016/j.jclinepi.2021.01.008

Missing data are ubiquitous in medical research. Although there is increasing guidance on how to handle missing data, practice is changing slowly and misapprehensions abound, particularly in observational research. Importantly, the lack of transparen

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::91a127879c20f1c14185e6c8ab57b52d
https://biblio.ugent.be/publication/8759670

Comparación de los métodos de imputación múltiple, Bayesianos y el algoritmo ICM (Iterative Convex Minorant) para datos con censura en intervalos

Autor: Bustos Giraldo, Olga Alexandra

Publikováno v: Repositorio UN
Universidad Nacional de Colombia
instacron:Universidad Nacional de Colombia

Los datos con censura en intervalos son comunes en varias áreas del conocimiento, tales como: epidemiología, finanzas, demografía, medicina, entre otros. Ocurren cuando el evento de interés, el tiempo de falla, no se observa exactamente, sino que

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::061e38206d6c62c8e55de632a11217e2
https://repositorio.unal.edu.co/handle/unal/76926