Multiple Imputation for Categorical Time Series
نویسندگان
چکیده
منابع مشابه
Missing data imputation in multivariable time series data
Multivariate time series data are found in a variety of fields such as bioinformatics, biology, genetics, astronomy, geography and finance. Many time series datasets contain missing data. Multivariate time series missing data imputation is a challenging topic and needs to be carefully considered before learning or predicting time series. Frequent researches have been done on the use of diffe...
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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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LVQ (Learning Vector Quantization) has been used to impute missing group membership and stratum weights in confirmatory factor analysis (CFA) model with continuous indicators (Chen, Tsai, & Yang, 2010; Tsai & Yang, 2012). Currently, categorical questionnaires (e.g., Binary and Likert-type items) are widely used in education, business, economy, and psychology tests as well as international large...
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ژورنال
عنوان ژورنال: The Stata Journal: Promoting communications on statistics and Stata
سال: 2016
ISSN: 1536-867X,1536-8734
DOI: 10.1177/1536867x1601600303