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Keywords
Missing Data
,Missing Data,miss data,missed data,misses data
Missing Data
Publications: 7,980
|
Citation Count: 87,238
Stemming Variations:
miss data, missed data, misses data
Cumulative
Annual
Definition Context
(5)
Missing data is a common feature of large data sets in general and medical data sets in particular. Depending on the goal of statistical analysis, various techniques can be used to tackle this problem. Imputation methods consist in substituting the missing values with plausible or predicted values so that the completed data can then be analysed with any chosen data mining procedure...
Cristian Preda
,
et al.
Tools for Statistical Analysis with Missing Data: Application to a Lar...
Missing data is a common problem in psychological research. Missing datacan occur due to attrition in a longitudinal study or non-response to questionnaireitems in a laboratory or field setting. Improper treatments of missing data(e.g., listwise deletion, mean imputation) can lead to biased statistical inferenceusing complete case analysis statistical techniques...
MARK FICHMAN
,
et al.
Multiple Imputation for Missing Data: Making the Most of What You Know
Missing data is a common feature for large data sets in general. Imputation is a class of procedures that aims to fill the missing values with estimated ones. This method involves replacing missing values with estimated ones based on some information available in the data set. One advantage of this approach is that the imputation phase is separated from the analysis phase, allowing different data mining algorithms to be applied to complete data sets...
B. Mehala
,
et al.
Selecting Scalable Algorithms to Deal With Missing Values
Missing data is a common occurrence in most medical research data collection enterprises. There is an extensive literature concerning missing data, much of which has focused on missing outcomes...
Nicholas J. Horton
,
et al.
Maximum likelihood analysis of generalized linear models with missing ...
Missing data is a common drawback in many real-life pattern classification scenarios. One of the most popular solutions is missing data imputation by the K nearest neighbours ðKNNÞ algorithm...
Pedro J. García-laencina
,
et al.
K nearest neighbours with mutual information for simultaneous classifi...
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Publications
(7980)
The Drink Driving Situation in Colombia
(
Citations: 4
)
RAMÓN CASTANO
Journal:
Traffic Injury Prevention - TRAFFIC INJ PREV
, vol. just-accep, no. just-accep, 2012
Reducing interference between multiple structured light depth sensors using motion
(
Citations: 1
)
Andrew Maimone
,
Henry Fuchs
Conference:
Virtual Reality, IEEE Annual International Symposium - VR
, pp. 51-54, 2012
Mean estimation with data missing at random for functional covariables
(
Citations: 1
)
Frédéric Ferraty
,
Mariela Sued
,
Philippe Vieu
Journal:
Statistics
, vol. ahead-of-p, no. ahead-of-p, pp. 1-19, 2012
Analyzing longitudinal clinical trial data with nonignorable missingness and unknown missingness reasons
(
Citations: 1
)
Hui Xie
Journal:
Computational Statistics & Data Analysis - CS&DA
, 2012
Estimating the global and regional burden of suboptimal nutrition on chronic disease: methods and inputs to the analysis
R Micha
,
S Kalantarian
,
P Wirojratana
,
T Byers
,
G Danaei
,
I Elmadfa
,
E Ding
,
E Giovannucci
,
J Powles
,
S Smith-Warner
,
M Ezzati
,
D Mozaffarian
Journal:
European Journal of Clinical Nutrition - EUR J CLIN NUTR
, vol. 66, no. 1, pp. 119-129, 2012