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Intensive, Repeated Self-Report Measures: Should We Be Concerned About Changes in Data Quality Over Time?

JMIR Mhealth Uhealth
Stone AA, Schneider S, Toledo MJ, Hernandez R.   +3 more
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The randomized marker method for single-case randomization tests: Handling data missing at random and data missing not at random

Behavior Research Methods, 2022
Single-case experiments are frequently plagued by missing data problems. In a recent study, the randomized marker method was found to be valid and powerful for single-case randomization tests when the missing data were missing completely at random.
Tamal Kumar De, Patrick Onghena
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Distinguishing “Missing at Random” and “Missing Completely at Random”

The American Statistician, 1996
Abstract Missing at random (MAR) and missing completely at random (MCAR) are ignorability conditions—when they hold, they guarantee that certain kinds of inferences may be made without recourse to complicated missing-data modeling. In this article we review the definitions of MAR, MCAR, and their recent generalizations.
Daniel F. Heitjan, Srabashi Basu
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Subtypes of the missing not at random missing data mechanism.

Psychological Methods, 2021
issing values that are missing not at random (MNAR) can result from a variety of missingness processes. However, two fundamental subtypes of MNAR values can be obtained from the definition of the MNAR mechanism itself. The distinction between them deserves consideration because they have characteristic differences in how they distort relationships in ...
Brenna Gomer, Ke-Hai Yuan
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Tests If Dropouts Are Missed at Random

Biometrical Journal, 1998
Summary: Dropouts are a common problem in longitudinal investigations where individuals are measured repeatedly over time. This holds also in a study on rheumatoid arthritis where an inception cohort was followed up over three years. The question arose whether or not these individuals caused a selection bias.
Listing, Joachim, Schlittgen, Rainer
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Likelihood‐based Inference with Missing Data Under Missing‐at‐Random

Scandinavian Journal of Statistics, 2015
AbstractLikelihood‐based inference with missing data is challenging because the observed log likelihood is often an (intractable) integration over the missing data distribution, which also depends on the unknown parameter. Approximating the integral by Monte Carlo sampling does not necessarily lead to a valid likelihood over the entire parameter space ...
Yang, Shu, Kim, Jae Kwang
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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.
Barata, A. Pereira, Takes, F.W., Herik, H.J. van den, Veenman, C.J.   +3 more
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Missing value imputation on missing completely at random data using multilayer perceptrons

Neural Networks, 2011
Data mining is based on data files which usually contain errors in the form of missing values. This paper focuses on a methodological framework for the development of an automated data imputation model based on artificial neural networks. Fifteen real and simulated data sets are exposed to a perturbation experiment, based on the random generation of ...
Esther-Lydia, Silva-Ramírez, Rafael, Pino-Mejías, Manuel, López-Coello, María-Dolores, Cubiles-de-la-Vega   +3 more
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Quantile regression with covariates missing at random

Statistica Sinica, 2014
Regression quantiles can be underpowered or biased when there are miss- ing values in some covariates. We propose a method that produces consistent linear quantile estimation in the presence of missing covariates. The proposed method cor- rects bias by constructing unbiased estimating equations that simultaneously hold at all the quantile levels.
Ying Wei, Yunwen Yang
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