Publications
See my Google Scholar.
2026
Liu, Bowen, Ananda, Malwane, and Weerahandi, Sam (2026). A novel exact inference approach for log–logistic reliability functions with applications to time-to-event data. arXiv:2605.01193.
Liu, Bowen, Ananda, Malwane, and Weerahandi, Sam (2026). Inference on survival reliability with Type-I censored Weibull data. arXiv:2604.12011.
Liu, Bowen, and Ananda, Malwane (2026). Exact statistical inference for quantities of loggamma distribution. Electronic Research Archive, 34(4), 2572–2589.
2025
Liu, Bowen, and Ananda, Malwane M. A. (2025). Novel discrete composite distributions with applications to infectious disease data. Communications in Statistics - Theory and Methods, 54(24), 8079–8099.
Crowley, Julia C, Liu, Bowen, and Nan, Ailing (2025). The impacts of local emergency management agency resource constraints on planning for hazard mitigation and debris management. Progress in Disaster Science, 100480.
Liu, Bowen, Hossain, Md Farhad, and Hossain, Shaheed (2025). A comparative evaluation of multiple machine learning approaches for forecasting dengue outbreaks in Bangladesh. Scientific Reports, 15(1), 35931.
Hossain, Safayet, Hossain, Md Farhad, Liu, Bowen, Ara, Anjuman, Alsaoud, Haneen, and Patwary, Md Abdul Majed (2025). Health challenges among waste collectors in Bangladesh: exploring risk factors using multi-level modeling. Safety and Health at Work, 16(1), 13–20.
Crowley, Julia, Liu, Bowen, and Jan, Hanan (2025). Assessing the knowledge, attitudes, and practices (KAP) of dengue in Thailand: a systematic review and meta-analysis. Archives of Public Health, 83(1), 38.
Liu, Bowen, and Ananda, Malwane M. A. (2025). The application of accumulation tests in peaks over threshold modeling with fire insurance data sets. Model Assisted Statistics and Applications, 20(1), 38–51.
Shen, Linchuan, Amei, Amei, Liu, Bowen, Xu, Gang, Liu, Yunqing, Oh, Edwin C, Zhou, Xin, and Wang, Zuoheng (2025). Marginal interaction test for detecting interactions between genetic marker sets and environment in genome-wide studies. G3: Genes, Genomes, Genetics, 15(1), jkae263.
2024
Hossain, Md Farhad, Hossain, Shaheed, Akter, Mst Nira, Nahar, Ainur, Liu, Bowen, and Faruque, Md Omar (2024). Metabolic syndrome predictive modelling in Bangladesh applying machine learning approach. PLoS One, 19(9), e0309869.
Fu, Mei Rosemary, Liu, Bowen, Qiu, Jeanna Mary, Sun, Yuanlu, Axelrod, Deborah, Guth, Amber, Korth, Stephanie, Kremer, Howard L, and Wang, Yao (2024). ASO Author Reflections: Infection and Skin Trauma Incrementally Increase the Risk of Breast Cancer-Related Lymphedema. Annals of Surgical Oncology, 31(12), 8110–8111.
Fu, Mei Rosemary, Liu, Bowen, Qiu, Jeanna Mary, Sun, Yuanlu, Axelrod, Deborah, Guth, Amber, Korth, Stephanie, Kremer, Howard L, and Wang, Yao (2024). The effects of daily-living risks on breast cancer-related lymphedema. Annals of Surgical Oncology, 31(12), 8076–8085.
2023
2022
In this paper, we propose a new family of distributions, by exponentiating the random variables associated with the probability density functions of composite distributions. We also derive some mathematical properties of this new family of distributions, including the moments and the limited moments. Specifically, two special models in this family are discussed. Three real datasets were chosen, to assess the performance of these two special exponentiated-composite models. When fitting to these three datasets, these three special exponentiated-composite distributions demonstrate significantly better performance, compared to the original composite distributions. Download paper here
Exponentiated models have been widely used in modeling various types of data such as survival data and insurance claims data. However, the exponentiated composite distribution models have not been explored yet. In this paper, we introduce an improvement of the one-parameter Inverse Gamma-Pareto composite model by exponentiating the random variable associated with the one-parameter Inverse Gamma-Pareto composite distribution function. The goodness-of-fit of the exponentiated Inverse Gamma-Pareto was assessed using three different insurance data sets. The two-parameter exponentiated Inverse Gamma-Pareto model outperforms the one-parameter Inverse Gamma-Pareto model in terms of goodness-of-fit measures for all datasets. In addition, the proposed exponentiated composite Inverse Gamma-Pareto model provides a very good fit with some well-known insurance datasets. Download paper here
2021
We clarified some issues and problems in the application of Bayesian methods in Meta-analysis in this letter to the editor.
This meta-analysis examined studies on the relationship between teachers’ self-efficacy and technology integration in K-12 education. A total of 14 studies in this meta-analysis with 3272 participants including 532 pre-service teachers and 2740 inservice teachers from Finland, Taiwan, US, Turkey, and Korea. Findings indicated that teachers’ self-efficacy had a positive relationship with their technology integration in K-12 education (r=. 32); however, the relationship between teachers’ self-efficacy and their technology integration did not differ significantly in terms of population (ie, pre-service teachers vs. in-service teachers), region (ie, US versus Finland, Taiwan, Turkey, and Korea), and sample size (n= 300). Implications for both pre-service and in-service teachers’ professional development with selfefficacy and technology integration were provided.
This systematic review and meta-analysis were conducted on all eligible cohort studies to evaluate the association between high-sensitivity C-reactive protein (hs-CRP) and osteoporotic fracture risk. Both frequentist and Bayesian approaches were employed for the meta-analysis. We found that high tertiles of hs-CRP were significantly associated with increased fracture risk. Download paper here
2019
This systematic review and meta-analysis summarized the results from nine eligible observational studies. Lithium use was significantly associated with a decrease risk of fractures. Download paper here
This meta-analysis pooled results from 23 qualifying individual cohort studies and found that depression was significantly associated with an increased risk of fractures and bone loss. Download paper here