… In this paper, four evaluation metrics are used to evaluate the proposed model, including
Pearson correlation coefficient r, mean error (ME), mean absolute error (MAE), and standard …

… strategies that address missingness (multiple imputation [MI], … This study aims to
evaluate the impact of SATs on treatment … Bayesian multi-parameter evidence synthesis (B-MPES) …

… We evaluated the performance of ProxySense for predicting … (CO), comparing to multiple
baselines by means of prediction … with data pre-prosessing including data imputation aimed at …

… First, k-nearest neighbor (KNN) was used to impute missing values in the dataset. Later, …
, most studies have used the Multi-parameter Intelligent Monitoring in Intensive Care dataset (…

… Given that EM-I employs multiple imputation, average parameters were calculated based on
… scaling model for test scores in international large-scale assessment studies. Measurement …

… evaluated the predictive accuracy of several in silico algorithms for immune composition in
AML based on a reference of multi-parameter … data of GSE425 were imputed by the impute.…

… use a multi-method/multi-parameter approach and to focus on … tested in a competitive manner,
and their performances were … The aim of both the data augmentation and the imputation is …

… for various machine learning models. The data were prepared using multiple imputations
for missing values, and categorical data were processed into numeric data using one-hot-…

… In order to make multiple imputation in Bayesian way do the following: • Impute draws,
not means, … This is an application of the Gibbs sampler used for estimation of the multi-parameter …