New Insights into Handling Missing Values in Environmental Epidemiological Studies

New Insights into Handling Missing Values in Environmental Epidemiological Studies

Missing data are unavoidable in environmental epidemiologic surveys. The aim of this study was to compare methods for handling large amounts of missing values: omission of missing values, single and multiple imputations (through linear regression or partial least squares regression), and a fully Bayesian approach. These methods were applied to the PARIS birth cohort, where indoor domestic pollu...

Missing values in epidemiological studies

Handling Missing Attribute Values

In this chapter methods of handling missing attribute values in data mining are described. These methods are categorized into sequential and parallel. In sequential methods, missing attribute values are replaced by known values first, as a preprocessing, then the knowledge is acquired for a data set with all known attribute values. In parallel methods, there is no preprocessing, i.e., knowledge...

Handling Missing Values in Data Mining

Missing Values and its problems are very common in the data cleaning process. Several methods have been proposed so as to process missing data in datasets and avoid problems caused by it. This paper discusses various problems caused by missing values and different ways in which one can deal with them. Missing data is a familiar and unavoidable problem in large datasets and is widely discussed i...

Handling of Missing Values in Lexical Acquisition

We propose a strategy to reduce the impact of the sparse data problem in the tasks of lexical information acquisition based on the observation of linguistic cues. It justifies that the uncertainty created by missing values, i.e. non-observed cues, can be handled by estimating its likelihood of being observable. Because of the Zipfian distribution of words, instead of estimating the likelihood f...