Results 271 to 280 of about 179,516 (314)

Missing Data Imputation Techniques

International Journal of Business Intelligence and Data Mining, 2007
Intelligent data analysis techniques are useful for better exploring real-world data sets. However, the real-world data sets always are accompanied by missing data that is one major factor affecting data quality. At the same time, good intelligent data exploration requires quality data.
Qinbao Song, Martin Shepperd
openaire   +1 more source

Privacy-Preserving Data Imputation

Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06), 2006
In this paper, we investigate privacy-preserving data imputation on distributed databases. We present a privacy-preserving protocol for filling in missing values using a lazy decision tree imputation algorithm for data that is horizontally partitioned between two parties.
Geetha Jagannathan, Rebecca Wright
openaire   +1 more source

Imputation and Missing Data

2017
The presence of missing data is a big challenge for statisticians, especially if the distribution of the missing values is not completely random. Analysis performed on datasets with missing data can lead to erroneous conclusions and significant bias in the results.
Amir Momeni, Matthew Pincus, Jenny Libien   +2 more
openaire   +1 more source

IMPUTING MISSING DATA

Journal of the American Academy of Child & Adolescent Psychiatry, 2004
Calvin D, Croy, Douglas K, Novins
openaire   +2 more sources

Imputation of Missing Data

2011
Below is a subset of data from a smoking cessation study for smokers newly diagnosed with cancer.Patients were assessed for anxiety and depression at baseline using the Hospital Anxiety and Depression Scale (Zigmond and Snaith 1983), at least 7 days before they were hospitalized for surgery.
Yuelin Li, Jonathan Baron
openaire   +1 more source

An overview of real‐world data sources for oncology and considerations for research

Ca-A Cancer Journal for Clinicians, 2022
Lynne Penberthy, Donna R Rivera, Jennifer L Lund, Msph   +2 more
exaly  

[Imputation of missing data].

Nederlands tijdschrift voor geneeskunde, 2013
In medical research missing data are sometimes inevitable. Different missingness mechanisms can be distinguished: (a) missing completely at random; (b) missing by design; (c) missing at random, and (d) missing not at random. If participants with missing data are excluded from statistical analyses, this can lead to biased study results and loss of ...
Ralph C A, Rippe, Martin, den Heijer, Saskia, le Cessie   +2 more
openaire   +1 more source

Innovations in research and clinical care using patient‐generated health data

Ca-A Cancer Journal for Clinicians, 2020
H S L Jim, Aasha I Hoogland, Naomi C Brownstein   +2 more
exaly  

Imputing Missing Network Data

2016
Missing data on network ties is a fundamental problem for network analyses. The biases induced by missing edge data, even when missing completely at random (MCAR), are widely acknowledged (Kossinets, 2006; Huisman & Steglich, 2008; Huisman, 2009). Although model based techniques for missing network data are quite promising, they are not available ...
Krause, Robert, Huisman, Mark, Steglich, Christian, Snijders, T.A.B.   +3 more
openaire   +1 more source

Imputed Data

2011
openaire   +1 more source