Multiple imputation of unordered categorical missing data: A comparison of the multivariate normal imputation and multiple imputation by chained equations
نویسندگان
چکیده
منابع مشابه
Multiple imputation for missing data: fully conditional specification versus multivariate normal imputation.
Statistical analysis in epidemiologic studies is often hindered by missing data, and multiple imputation is increasingly being used to handle this problem. In a simulation study, the authors compared 2 methods for imputation that are widely available in standard software: fully conditional specification (FCS) or "chained equations" and multivariate normal imputation (MVNI). The authors created ...
متن کاملMultiple Imputation for Missing Data
Multiple imputation provides a useful strategy for dealing with data sets with missing values. Instead of filling in a single value for each missing value, Rubin’s (1987) multiple imputation procedure replaces each missing value with a set of plausible values that represent the uncertainty about the right value to impute. These multiply imputed data sets are then analyzed by using standard proc...
متن کاملA nonparametric multiple imputation approach for missing categorical data
BACKGROUND Incomplete categorical variables with more than two categories are common in public health data. However, most of the existing missing-data methods do not use the information from nonresponse (missingness) probabilities. METHODS We propose a nearest-neighbour multiple imputation approach to impute a missing at random categorical outcome and to estimate the proportion of each catego...
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Multiple imputation by chained equations (MICE) is an effective tool to handle missing data an almost unavoidable problem in quantitative data analysis. However, despite the empirical and theoretical evidence supporting the use of MICE, researchers in the social sciences often resort to inferior approaches unnecessarily risking erroneous results. The complexity of the decision process when enco...
متن کاملMultiple imputation: dealing with missing data.
In many fields, including the field of nephrology, missing data are unfortunately an unavoidable problem in clinical/epidemiological research. The most common methods for dealing with missing data are complete case analysis-excluding patients with missing data--mean substitution--replacing missing values of a variable with the average of known values for that variable-and last observation carri...
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ژورنال
عنوان ژورنال: Brazilian Journal of Probability and Statistics
سال: 2016
ISSN: 0103-0752
DOI: 10.1214/15-bjps292