Analysis of social network data is often hampered by non-response and missing data. Recent studies show the negative effects of missing actors and ties on the structural properties of social networks. This means that the results of social network analyses can be severely biased if missing ties were ignored and only complete cases were analyzed. To overcome the problems created by missing data, ...

We introduce Learn.MF, an ensemble-of-classifiers based algorithm that employs random subspace selection to address the missing feature problem in supervised classification. Unlike most established approaches, Learn.MF does not replace missing values with estimated ones, and hence does not need specific assumptions on the underlying data distribution. Instead, it trains an ensemble of classifie...

Missing data are an unavoidable complication in many machine learning tasks. When ‘missing at random’ there exist a range of tools and techniques to deal with the issue. However, as studies become more ambitious, seek learn from ever-larger volumes heterogeneous data, increasingly encountered problem arises which missing values exhibit association or structure, either explicitly implicitly. Suc...

Missing data is a common occurrence in epidemiologic research. In this paper, 3 data sets with induced missing values from the Collaborative Perinatal Project, a multisite US study conducted from 1959 to 1974, are provided as examples of prototypical epidemiologic studies with missing data. Our goal was to estimate the association of maternal smoking behavior with spontaneous abortion while adj...

BACKGROUND Many randomized trials involve missing binary outcomes. Although many previous adjustments for missing binary outcomes have been proposed, none of these makes explicit use of randomization to bound the bias when the data are not missing at random. METHODS We propose a novel approach that uses the randomization distribution to compute the anticipated maximum bias when missing at ran...

BACKGROUND Missing values are commonly encountered on the Mini Mental State Examination (MMSE), particularly when administered to frail older people. This presents challenges for MMSE scoring in research settings. We sought to describe missingness in MMSEs administered in long-term-care facilities (LTCF) and to compare and contrast approaches to dealing with missing items. METHODS As part of ...

In most situations, simple techniques for handling missing data (such as complete case analysis, overall mean imputation, and the missing-indicator method) produce biased results, whereas imputation techniques yield valid results without complicating the analysis once the imputations are carried out. Imputation techniques are based on the idea that any subject in a study sample can be replaced ...

Most collaborative filtering (CF) models estimate missing ratings with an implicit assumption that the are missing-at-random, which may cause biased rating estimation and degraded performance since recent deep exploration shows likely be missing-not-at-random (MNAR). To debias MNAR estimation, we introduce item observability user selection to depict generation of propose a tripartite CF (TCF) f...