Multiple imputation of maritime search and rescue data at multiple missing patterns
نویسندگان
چکیده
منابع مشابه
Multiple 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 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...
متن کاملMaritime Search and Rescue via Multiple Coordinated UAS
The advent of Unmanned Aerial Systems (UAS) has created opportunities for expensive capital assets to be replaced by these small, yet capable, platforms. Tasks that are identified as able to be performed by UAS may also benefit from the ability of a collection of UAS to operate in a cooperative and parallel manner. Parallelization also means that several parts of the search area can be covered ...
متن کاملassessment of internal validity of prognostic models through bootstrapping and multiple imputation of missing data
background: prognostic models have clinical appeal to aid therapeutic decision making. two main practical challenges in development of such models are assessment of validity of models and imputation of missing data. in this study, importance of imputation of missing data and application of bootstrap technique in development, simplification, and assessment of internal validity of a prognostic mo...
متن کاملMultiple Imputation for General Missing Data Patterns in the Presence of High-dimensional Data
Multiple imputation (MI) has been widely used for handling missing data in biomedical research. In the presence of high-dimensional data, regularized regression has been used as a natural strategy for building imputation models, but limited research has been conducted for handling general missing data patterns where multiple variables have missing values. Using the idea of multiple imputation b...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PLOS ONE
سال: 2021
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0252129