Results 221 to 230 of about 175,631 (262)

Missing Data Imputation

2012
Missing data in clinical research data is often a real problem. As an example, a 35 patient data file of 3 variables consists of 3 × 35 = 105 values if the data are complete. With only 5 values missing (1 value missing per patient) 5 patients will not have complete data, and are rather useless for the analysis.
Ton J. Cleophas, Aeilko H. Zwinderman
openaire   +1 more source

Imputation of the Missing Data

2013
We may consider the existence of missing observations as unimportant, considering that the risk of misunderstanding is negligible. The surveyor assumes some model that allows adequately explaining the variable of interest. In such cases, we are able to predict the unknown values and to plug them into some estimator.
openaire   +1 more source

[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

Imputing missing yield trial data

Theoretical and Applied Genetics, 1990
The Additive Main effects and Multiplicative Interaction (AMMI) statistical model has been demonstrated effective for understanding genotype-environment interactions in yields, estimating yields more accurately, selecting superior genotypes more reliably, and allowing more flexible and efficient experimental designs. However, AMMI had required data for
H G, Gauch, R W, Zobel
openaire   +2 more sources

Privacy-preserving imputation of missing data

Data & Knowledge Engineering, 2008
Handling missing data is a critical step to ensuring good results in data mining. Like most data mining algorithms, existing privacy-preserving data mining algorithms assume data is complete. In order to maintain privacy in the data mining process while cleaning data, privacy-preserving methods of data cleaning will be required.
Geetha Jagannathan, Rebecca N. Wright
openaire   +2 more sources

Optimized Parameters for Missing Data Imputation

2006
To complete missing values, a solution is to use attribute correlations within data. However, it is difficult to identify such relations within data containing missing values. Accordingly, we develop a kernel-based missing data imputation method in this paper.
Shichao Zhang 0001, Yongsong Qin, Xiaofeng Zhu 0001, Jilian Zhang, Chengqi Zhang   +4 more
openaire   +1 more source

Meta-GAIN for Missing Data Imputation

Proceedings of the AAAI Conference on Artificial Intelligence
Although previous deep imputation methods (eg., Generative Adversarial Network (GAN) based methods) have been widely designed to impute missing data, they still suffer from the issues, ie., lack of the imputation diversity and the generalization ability.
Tao Tong, Xiaofeng Zhu 0001, Jiangzhang Gan   +2 more
openaire   +2 more sources

Missing Data Imputation for Machine Learning

2019
The imputation of missing values in datasets always plays an important role in the data preprocessing. In the process of data collection, because of the various reasons, the datasets often contain some missing values, and the excellent missing data imputation algorithms can increase the reliability of the dataset and reduce the impact of missing values
Shaoqian Wang, Bo Li 0090, Mao Yang 0001, Zhongjiang Yan   +3 more
openaire   +2 more sources

Multiple imputation as a missing data machine.

Proceedings. Symposium on Computer Applications in Medical Care, 1994
This paper deals with problems concerning missing data in clinical databases. After signalling some shortcomings of popular solutions to incomplete data problems, we outline the concepts behind multiple imputation. Multiple imputation is a statistically sound method for handling incomplete data. Application of multiple imputation requires a lot of work
Brand, J., Buuren, S. van, Mulligen, E.M. van, Timmers, T., Gelsema, E.   +4 more
openaire   +2 more sources

Influence of Data Distribution in Missing Data Imputation

2017
Dealing with missing data is a crucial step in the preprocessing stage of most data mining projects. Especially in healthcare contexts, addressing this issue is fundamental, since it may result in keeping or loosing critical patient information that can help physicians in their daily clinical practice.
Miriam Seoane Santos, Jastin Pompeu Soares, Pedro Henriques Abreu, Hélder Araújo, João A. M. Santos   +4 more
openaire   +1 more source