A test of missing completely at random for generalised estimating equations with missing data
Biometrika, 1999zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chen, Hua Yun, Little, Roderick
exaly +2 more sources
The Generalized Estimating Equation Approach When Data are Not Missing Completely at Random
Journal of the American Statistical Association, 1997Abstract We propose two methods for handling missing data in generalized estimating equation (GEE) analyses: mean imputation and multiple imputation. Each provides valid GEE estimates when data are missing at random. Missing outcomes are imputed sequentially starting from the outcome nearest in time to the observed outcome.
Myunghee Cho Paik
exaly +4 more sources
Missing (Completely?) At Random: Lessons from Insurance Studies
Asia-Pacific Journal of Risk and Insurance, 2009A dilemma frequently faced by empirical researchers is whether they should keep observations without complete information in the analysis. Assuming missingness is not biased in any perceivable direction, most studies use a complete case analysis approach, whereby only observations with complete information are kept for empirical estimation.
exaly +2 more sources
Imputation Methods Outperform Missing-Indicator for Data Missing Completely at Random
2019 International Conference on Data Mining Workshops (ICDMW), 2019Missing 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 +3 more
openaire +2 more sources
Missing Energy Data Imputation: Addressing Missing Completely at Random Mechanism
Lecture Notes in Networks and SystemsFeres Jerbi, Hatem Haddad, Issam Smaali
exaly +2 more sources
A Nonparametric Test of Missing Completely at Random for Incomplete Multivariate Data
Psychometrika, 2015Missing data occur in many real world studies. Knowing the type of missing mechanisms is important for adopting appropriate statistical analysis procedure. Many statistical methods assume missing completely at random (MCAR) due to its simplicity. Therefore, it is necessary to test whether this assumption is satisfied before applying those procedures ...
Li, Jun, Yu, Yao
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The additive model affected by missing completely at random in the covariate
Computational Statistics, 2004This paper focuses on a comparison of several imputation procedures within the simple additive model \(y=f(x)+\varepsilon\), where the independent variable \(x\) is affected by missing completely randomly. Such imputation methods as complete case analysis, mean imputation plus random noise, single imputation and two kinds of nearest neighbour ...
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Model averaging with covariates that are missing completely at random
Economics Letters, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Optimum estimation of missing values in randomized complete block design by genetic algorithm
Knowledge-Based Systems, 2013Missing data are a part of almost all research, and we all have to decide how to deal with it from time to time. There are a number of alternative ways of dealing with missing data. The problem of handling missing data has been treated adequately in various real world data sets.
Ali Azadeh +6 more
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A Note on Normal Theory Power Calculation in SEM With Data Missing Completely at Random
Structural Equation Modeling: A Multidisciplinary Journal, 2005We consider power calculation in structural equation modeling with data missing completely at random (MCAR). Muthen and Muthen (2002) recently demonstrated how power calculations with data MCAR can be carried out by means of a Monte Carlo study. Here we show that the method of Satorra and Saris (1985), which is based on the nonnull distribution of the (
Dolan, C. +2 more
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