Results 251 to 260 of about 1,718,863 (277)

Imputation Methods Outperform Missing-Indicator for Data Missing Completely at Random

2019 International Conference on Data Mining Workshops (ICDMW), 2019
Missing data is a ubiquitous cross-domain problem persistent in the context of big data analytics. Approaches to deal with missing data can be partitioned into methods that impute substitute values and methods that introduce missing-indicator variables.
António Pereira Barata, Frank W. Takes, H. Jaap van den Herik, Cor J. Veenman   +3 more
openaire   +2 more sources

On the Distribution of the Number of Missing Words in Random Texts

Combinatorics, Probability and Computing, 2003
Determining the distribution of the number of empty urns after a number of balls have been thrown randomly into the urns is a classical and well understood problem. We study a generalization: Given a finite alphabet of size σ and a word length q, what is the distribution of the number X of words (of length q) that do not occur in a random text of ...
Sven Rahmann, Eric Rivals
openaire   +1 more source

Analysis of missing data with random forests

2012
Random Forests are widely used for data prediction and interpretation purposes. They show many appealing characteristics, such as the ability to deal with high dimensional data, complex interactions and correlations. Furthermore, missing values can easily be processed by the built-in procedure of surrogate splits.
openaire   +2 more sources

Missing Randomization …

Anesthesiology, 2016
John, Picard, Jason, Wilson
openaire   +2 more sources

Missing Inaction: Preventing Missing Outcome Data in Randomized Clinical Trials

Journal of Biopharmaceutical Statistics, 2009
Many methods are available to deal with missing data in randomized clinical trials, and active statistical research in the area continues. When, however, a high proportion of outcome data is missing, the methods can produce inaccurate estimates of the true effect size.
openaire   +2 more sources

Missing Not at Random

2021
openaire   +1 more source

Score Test for Missing at Random or Not under Logistic Missingness Models

Biometrics, 2023
Zhiping Lu, Yukun Liu
exaly  

Missing Completely at Random

2021
openaire   +1 more source

Nonparametric Estimation of Mean Functionals with Data Missing at Random

Journal of the American Statistical Association, 1994
Philip E Cheng
exaly   +2 more sources

Handling missing data in modelling quality of clinician-prescribed routine care: Sensitivity analysis of departure from missing at random assumption

Statistical Methods in Medical Research, 2020
Susan Gachau, Matteo Quartagno, Edmund Njeru Njagi   +2 more
exaly