Graphical models that depict the process by which data are lost are helpful in recovering information from missing data. We address the question of whether any such model can be submitted to a statistical test given that the data available are corrupted by missingness. We present sufficient conditions for testability in missing data applications and note the impediments for testability when dat...

OBJECTIVE QoL data were routinely collected in a randomised controlled trial (RCT), which employed a reminder system, retrieving about 50% of data originally missing. The objective was to use this unique feature to evaluate possible missingness mechanisms and to assess the accuracy of simple imputation methods. METHODS Those patients responding after reminder were regarded as providing missin...

Using standard missing data taxonomy, due to Rubin and co-workers, and simple algebraic derivations, it is argued that some simple but commonly used methods to handle incomplete longitudinal clinical trial data, such as complete case analyses and methods based on last observation carried forward, require restrictive assumptions and stand on a weaker theoretical foundation than likelihood-based ...

Number: 4345 Presentation Title: PELP1/MNAR in ovarian cancer: implications in tumorigenesis Presentation Start/End Time: Tuesday, Apr 17, 2007, 1:00 PM 5:00 PM Location: Exhibit Hall, Los Angeles Convention Center

There are many algorithms developed for improvement the time of mining frequent itemsets (FI) or frequent closed itemsets (FCI). However, the algorithms which deal with the time of generating association rules were not put in deep research. In reality, in case of a database containing many FI/FCI (from ten thousands up to millions), the time of generating association rules is much larger than t...

In this work, we fit pattern-mixture models to data sets with responses that are potentially missing not at random (MNAR, Little and Rubin, 1987). In estimating the regression parameters that are identifiable, we use the pseudo maximum likelihood method based on exponential families. This procedure provides consistent estimators when the mean structure is correctly specified for each pattern, w...

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...

In recommendation systems, the existence of missing-not-at-random (MNAR) problem results in selection bias issue, degrading performance ultimately. A common practice to address MNAR is treat missing entries from so-called “exposure” perspective, i.e., modeling how an item exposed (provided) a user. Most existing approaches use heuristic models or re-weighting strategy on observed ratings mimic ...