Missing Data

2020

Citations in the corpus

• Optimal and Adaptive Off-policy Evaluation in Contextual Bandits (2016, neutral): “This question has been extensively researched in the contextual bandit model (see, e.g., [Li et al., 2011, Bottou et al., 2013, Oh and Scheuren, 1983, Little and Rubin, 2002] and references therein).”
• Stochastic Back-propagation and Variational Inference in Deep Latent Gaussian Models (2014, neutral): “We test the imputation ability under two different missingness types (Little & Rubin, 1987): Missing-at-random (MAR), where we consider 60% and 80% of the pixels to be missing randomly, and Not Missing-at-random (NMAR), where we consider a square region of the image to be missing.”
• Semiparametric Regression Analysis With Missing Response at Random (2003, unknown): “practical situations, see Little and Rubin (1987,Chapter 1) .”
• Assumptions of Value-Added Models for Estimating School Effects (2009, unknown): “Statistically efficient analysis of all available data weakens the required assumptions about the missing data process (Little & Rubin, 2002; Schafer, 1997).”
• Cultural Context, Sexual Behavior, and Romantic Relationships in Disadvantaged Neighborhoods (2007, unknown): “If an individual’s repertoire is constructed from what one observes among the people with whom one interacts and from the broader cultural ideas to which one is exposed through media and institutions, then the average adolescent in a disadvantaged neighborhood will have a wider range of scripts and…”
• Protecting privacy using k-anonymity. (2008, background): “Suppression results in discarding data that was costly to collect and potentially result in a considerable loss of statistical power in any subsequent analysis. Furthermore, unless record suppression is completely random, it will bias analysis results.”
• Estimation of nonstationary spatial covariance structure (2002, unknown): “We have obtained a positive definite estimate of the spatial covariance matrix using the EM algorithm (Little and Rubin, 1987) with an initial estimate of the covariance obtained by filling in missing values with site means.”
• The Informal Economy in Sub-Saharan Africa: Size and Determinants (2017, unknown): “This alternative method treats informality as a missing data problem (Little and Rubin 1987).”
• Variations and extension of the convex–concave procedure (2016, unknown): “Many EM and MM extensions have been developed over the years and more can be found on these algorithms in (Little and Rubin 1987; Lange 2004; McLachlan and Krishnan 2007).”
• The Multilevel Approach to Repeated Measures for Complete and Incomplete Data (2003, unknown): “The ML and REML estimates of the parameters differ from these estimates (mostly only slightly, unless there are many missing data and correlations are high) and are statistically more efficient, as may be deduced from Little and Rubin (1987) .”
• Leveraging Information Technology Infrastructure to Facilitate a Firm's Customer Agility and Competitive Activity: An Empirical Investigation (2012, unknown): “We performed little’s McaR (missing completely at random) test [53] and found that these values were missing completely at random ( p > 0.05).”
• Identification and Doubly Robust Estimation of Data Missing Not at Random with an Ancillary Variable (2015, unknown): “ Little(1993,1994) studied identication of pattern-mixture models under various restrictions on the parameters of dierent patterns. For instance, the missing completely at random [MCAR,Little(1993);Little and Rubin (2002)] mechanism requires identical patterns: P(z;yjr= 1) = P(z;yjr= 0). 12 WANG MIA”