Consumer preference prediction aims to predict consumers' future purchases based on their
historical behavior-level data. Using machine learning algorithms, the prediction results …

We examined the degree to which an extended religious experience during Ramadan might
promote interpersonal forgiveness. With six waves of data from a sample of N= 215 …

Because adolescents are unlikely to seek, receive, or complete treatment for alcohol and/or
cannabis misuse, it is important to enhance the lasting impact of clinical contacts when they …

Multi-informant studies are popular in social and behavioral science. However, their data
analyses are challenging because data from different informants carry both shared and …

Objective To assess the bidirectional association between chronic pain and depressive
symptoms among middle-aged and older adults from two prospective cohort studies …

This paper introduces a methodology for monitoring the vessel arrival process, a critical
factor in enhancing maritime operational efficiency. This approach uses deep learning …

Missing Not at Random (MNAR) and nonnormal data are challenging to handle. Traditional
missing data analytical techniques such as full information maximum likelihood estimation …

The presence of missing values (missings) in data series is a common issue that needs to
be adequately addressed to ensure the validity of certain statistical methods and, in turn, to …

This study holds significant implications as it examines the impact of different missing data
handling methods on the internal consistency coefficients. Using Monte Carlo simulations …