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Imputing missing values in high-dimensional time-series data remains a significant challenge in statistics and machine learning. Although various methods have been proposed in recent years, many struggle with limitations and reduced accuracy, particularly when the missing rate is high. In this work, we present a novel f-divergence-based bidirectional generative adversarial imputation network, tf-BiGAIN, designed to address these challenges in time-series data imputation. Unlike traditional imputation methods, tf-BiGAIN employs a generative model to synthesize missing values without relying on distributional assumptions. The imputation process is achieved by training two neural networks, implemented using bidirectional modified gated recurrent units, with f-divergence serving as the objective function to guide optimization. Compared to existing deep learning-based methods, tf-BiGAIN introduces two key innovations. First, the use of f-divergence provides a flexible and adaptable framework for optimizing the model across diverse imputation tasks, enhancing its versatility. Second, the use of bidirectional gated recurrent units allows the model to leverage both forward and backward temporal information. This bidirectional approach enables the model to effectively capture dependencies from both past and future observations, enhancing its imputation accuracy and robustness. We applied tf-BiGAIN to analyze two real-world time-series datasets, demonstrating its superior performance in imputing missing values and outperforming existing methods in terms of accuracy and robustness.

Background: Previous research has identified differences in e-cigarette use and socioeconomic factors between different racial groups However, there is little research examining specific risk factors contributing to the racial differences.

Objective: This study sought to identify racial disparities in e-cigarette use and to determine risk factors that help explain these differences.

Methods: We used Wave 5 (2018-2019) of the Adult Population Assessment of Tobacco and Health (PATH) Study. First, we conducted descriptive statistics of e-smoking across our risk factor variables. Next, we used multiple logistic regression to check the risk effects by adjusting all covariates. Finally, we conducted a mediation analysis to determine whether identified factors showed evidence of influencing the association between race and e-cigarette use. All analyses were performed in R or SAS. The R package mma was used for the mediation analysis.

Results: Between Hispanic and non-Hispanic White populations, our potential risk factors collectively explain 17.5% of the racial difference, former cigarette smoking explains 7.6%, receiving e-cigarette advertising 2.6%, and perception of e-cigarette harm explains 27.8% of the racial difference. Between non-Hispanic Black and non-Hispanic White populations, former cigarette smoking, receiving e-cigarette advertising, and perception of e-cigarette harm explain 5.2%, 1.8%, and 6.8% of the racial difference, respectively. E-cigarette use is most prevalent in the non-Hispanic White population compared to non-Hispanic Black and Hispanic populations, which may be explained by former cigarette smoking, exposure to e-cigarette advertising, and e-cigarette harm perception.

Conclusions: These findings suggest that racial differences in e-cigarette use may be reduced by increasing knowledge of the dangers associated with e-cigarette use and reducing exposure to e-cigarette advertisements. This comprehensive analysis of risk factors can be used to significantly guide smoking cessation efforts and address potential health burden disparities arising from differences in e-cigarette usage.