Missing data

By Lyle Ungar. 2019

8,281 citations recorded by the source corpus.

. The aim of Deliverable 4.4 is to automatically ﬁll gaps due to heavy occlusion and clutter in recorded 3D data. The recovered 3D structure will feed back into the shared representation developed in T3.1.

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Citations in the corpus

Missing Data Imputation for Supervised Learning (2016, methodology): “Following the notation of Little and Rubin (2014, Chap.”
Modeling Nonignorable Missing Data in Speeded Tests (2008, methodology): “…item response theory; maximum marginal likelihood; missing data; nonignorable missing data; sequential model; steps model Missing data usually create problems for statistical analyses, especially whenignorability of the missing data does not hold (Heitjan, 1994; Little & Rubin, 1987; Rubin, 1976).”
Impact of a Large-Scale Science Intervention Focused on English Language Learners (2016, methodology): “Multiple imputation using the Markov chain Monte Carlo method (Little & Rubin, 1987) was used to account for missing data at pretest (i.e., multiply Impact of Science Intervention on English Language Learners imputed pretest).”
Accounting for missing data in statistical analyses: multiple imputation is not always the answer (2019, methodology): “It is important to conduct sensitivity analyses to the assumptions made about the missing data and any other assumptions relevant to the method used.(1,19,23) There may also be concerns specific to the type of study being analysed: for example, respecting the intention to treat principle in randomiz”
Tobit Models in Social Science Research (2003, background): “In its place, a framework that allows for some type of correlation between the selection mechanism and the continuous outcome would be desirable, but the identification demands of these types of models are great (see, e.g., Little and Rubin 1987).”
Longitudinal Cross-Gender Factorial Invariance of the Academic Motivation Scale (2006, methodology): “For the purpose of this study, it was assumed that the data was missing at random (R.J.A. Little & Rubin, 1987 ).”
Fairness without Demographics through Adversarially Reweighted Learning (2020, background): “Re-weighting [28, 26, 41] is a popular paradigm typically used to address problems such as class imbalance by upweighting examples from minority class.”
Parenting Stress of Low-Income Parents of Toddlers and Preschoolers: Psychometric Properties of a Short Form of the Parenting Stress Index (2007, methodology): “These techniques have known limitations ( Little & Rubin, 2002 ), but the relatively small number of missing data coupled with the large sample supported its use over the alternatives, such as WLS with listwise deletion or Full Information Maximum Likelihood estimation (which uses all of the availab”
A Statistically Justified Pairwise ML Method for Incomplete Nonnormal Data: A Comparison With Direct ML and Pairwise ADF (2005, methodology): “In fact, a missingness mechanism is called ignorable if it is MCAR or MAR and if it is independent of parameters to be estimated, an additional condition that is almost always true in practice. For details, see Little and Rubin (2002) .”
Measuring teaching quality in higher education: assessing selection bias in course evaluations (2017, methodology): “In the statistics literature on missing data, this is known as the problem of not-at-random missing data or non-ignorable non-response (Allison 2001; Rubin and Little 1987): see Berg (2005) for an overview of non-response bias.”
A local influence approach to sensitivity analysis of incomplete longitudinal ordinal data (2001, background): “This situation is termed ignorable by Rubin (1976) and Little and Rubin (1987) and leads to considerable simplification in the analysis.”
Signaling in Secret: Pay-for-Performance and the Incentive and Sorting Effects of Pay Secrecy (2014, result): “With all of the coefficients statistically insignificant, the results suggest that all attrition was random and hence unlikely to bias our findings (Little & Rubin, 1987).”
Survey nonresponse and the distribution of income (2005, background): “Various imputation/matching methods address item nonresponse by exploiting the questions that are in fact answered [20, 21 ].”
The Impact of Culture on Filipino American Students' Sense of Belonging (2010, background): “In one of the few persistence studies that did include an examination of Asian American students specifically, Museus, Nichols, and Lambert (2008) provide some evidence that the concept of cultural integration might not adequately explain Asian American students’ success in college.”
Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation (2016, methodology): “We will take the frequentist view in this paper; for more discussions on imputation approach, please refer to Meng (1994), Rubin (1996), Paik (1997), Lipsitz et al. (1998), Aerts et al. (2002), Little & Rubin (2002) and Ibrahim et al. (2005).”