Nonparametric statistical inference and imputation for incomplete categorical data
نویسندگان
چکیده
منابع مشابه
A 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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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: Statistics and Its Interface
سال: 2020
ISSN: 1938-7989,1938-7997
DOI: 10.4310/sii.2020.v13.n1.a2