What is the difference between missing completely at random and missing at random? [PDF]
Int J Epidemiol, 2014 The terminology describing missingness mechanisms is confusing. In particular the meaning of 'missing at random' is often misunderstood, leading researchers faced with missing data problems away from multiple imputation, a method with considerable ...
Bhaskaran K, Smeeth L.
europepmc +10 more sources
Optimal nonparametric testing of Missing Completely At Random, and its connections to compatibility [PDF]
The Annals of Statistics, 2023 Given a set of incomplete observations, we study the nonparametric problem of testing whether data are Missing Completely At Random (MCAR). Our first contribution is to characterise precisely the set of alternatives that can be distinguished from the ...
Berrett, T. B., Samworth, R. J.
core +8 more sources
Missingness mechanisms and generalizability of patient reported outcome measures in colorectal cancer survivors – assessing the reasonableness of the “missing completely at random” assumption [PDF]
BMC Medical Research MethodologyBackground Patient-Reported Outcome Measures (PROM) provide important information, however, missing PROM data threaten the interpretability and generalizability of findings by introducing potential bias.
Johanne Dam Lyhne +5 more
doaj +7 more sources
Tests of homoscedasticity, normality, and missing completely at random for incomplete multivariate data. [PDF]
Psychometrika, 2010 Test of homogeneity of covariances (or homoscedasticity) among several groups has many applications in statistical analysis. In the context of incomplete data analysis, tests of homoscedasticity among groups of cases with identical missing data patterns have been proposed to test whether data are missing completely at random (MCAR). These tests of MCAR
Jamshidian M, Jalal S.
europepmc +8 more sources
Little's test of missing completely at random [PDF]
The Stata Journal: Promoting communications on statistics and Stata, 2013 In missing-data analysis, Little’s test (1988, Journal of the American Statistical Association 83: 1198–1202) is useful for testing the assumption of missing completely at random for multivariate, partially observed quantitative data. I introduce the mcartest command, which implements Little’s missing completely at random test and its extension for ...
Li, Cheng, Li, Cheng
semanticscholar +5 more sources
Is cancer stage data missing completely at random? A report from a large population-based cohort of non-small cell lung cancer [PDF]
Frontiers in Oncology, 2023 IntroductionPopulation-based datasets are often used to estimate changes in utilization or outcomes of novel therapies. Inclusion or exclusion of unstaged patients may impact on interpretation of these studies.MethodsA large population-based dataset in ...
Andrew G. Robinson +8 more
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MissMech: An R Package for Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random (MCAR) [PDF]
Journal of Statistical Software, 2014 Researchers are often faced with analyzing data sets that are not complete. To prop- erly analyze such data sets requires the knowledge of the missing data mechanism.
Mortaza Jamshidian +2 more
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Regression-Based Approach to Test Missing Data Mechanisms [PDF]
Data, 2022 Missing data occur in almost all surveys; in order to handle them correctly it is essential to know their type. Missing data are generally divided into three types (or generating mechanisms): missing completely at random, missing at random, and missing ...
Serguei Rouzinov, André Berchtold
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A novel test of missing completely at random: U -statistics-based approach
Statistics, 2023 In this paper, a novel test for testing whether data are missing completely at random is proposed. Asymptotic properties of the test are derived utilizing the theory of non-degenerate U-statistics. It is shown that the novel test statistic coincides with the well-known Little's d2 statistic in the case of a multivariate data that has only one variable ...
Danijel Aleksi'c
openaire +4 more sources
Stat, 2023 SummaryAlthough the era of digitalization has enabled access to large quantities of data, due to their insufficient structuring, some data are often missing, and sometimes, the percentage of missing data is significant compared to the entire sample. On the other hand, most of the statistical methodology is designed for complete data.
Danijel Aleksić +2 more
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