Li, Man, Shanwen Zhu, Zhenglian Wang, Qiushi Feng, Junni Zhang, Fengqing Chao, Wei Tang, Linda George, Emily Grundy, Michael Murphy, Michael Lutz, Adrian Dobra, Kenneth Land*, and Zeng Yi*. “Optimized Random-Combinations of Total Fertility Rates and Life Expectancies at Birth for Probabilistic Population Projections”. Population Research and Policy Review 44, no. 7 (2025).

Chao, Fengqing*, Bruno Masquelier, Danzhen You, Lucia Hug, Yang Liu, David Sharrow, Håvard Rue, Hernando Ombao, and Leontine Alkema. “Sex differences in mortality among children, adolescents, and young people aged 0–24 years: a systematic assessment of national, regional, and global trends from 1990 to 2021”. Lancet Global Health 11, no. 10 (2023): e1519-e1530

Chao, Fengqing*, Samir K.C.*, and Hernando Ombao. “Estimation and probabilistic projection of levels and trends in the sex ratio at birth in seven provinces of Nepal from 1980 to 2050: a Bayesian modeling approach.” BMC Public Health 22, no. 1 (2022): 358

Chao, Fengqing*, Muhammad Asif Wazir, and Hernando Ombao. “Levels and Trends Estimates of Sex Ratio at Birth for Seven Provinces of Pakistan from 1980 to 2020 with Scenario-Based Probabilistic Projections of Missing Female Birth to 2050: a Bayesian Modeling Approach”. International Journal of Population Studies 8, no. 2 (2022): 51-70

Chao, Fengqing*, Christophe Z. Guilmoto, and Hernando Ombao. “Sex ratio at birth in Vietnam among six subnational regions during 1980–2050, estimation and probabilistic projection using a Bayesian hierarchical time series model with 2.9 million birth records.” PLoS ONE 16, no. 7 (2021): e0253721

Chao, Fengqing*, Patrick Gerland, Alex R. Cook, Christophe Z. Guilmoto, and Leontine Alkema. “Projecting sex imbalances at birth at global, regional and national levels from 2021 to 2100: scenario-based Bayesian probabilistic projections of the sex ratio at birth and missing female births based on 3.26 billion birth records”. BMJ Global Health 6, no. 8 (2021): e005516

Chao, Fengqing*, Patrick Gerland, Alex R. Cook, and Leontine Alkema. “Global estimation and scenariobased projections of sex ratio at birth and missing female births using a Bayesian hierarchical time series mixture model.” Annals of Applied Statistics 15, no. 3 (2021): 1499-1528

Guilmoto, Christophe Z.*, Fengqing Chao, and Purushottam M. Kulkarni. “On the estimation of female births missing due to prenatal sex selection.” Population Studies 74, no. 2 (2020): 283-289

Chao, Fengqing*, Christophe Z. Guilmoto, Samir K.C., and Hernando Ombao. “Probabilistic projection of the sex ratio at birth and missing female births by State and Union Territory in India.” PLoS ONE 15, no. 8 (2020): e0236673

Chao, Fengqing*, Patrick Gerland, Alex R. Cook, and Leontine Alkema. “Systematic assessment of the sex ratio at birth for all countries and estimation of national imbalances and regional reference levels.” Proceedings of the National Academy of Sciences 116, no. 19 (2019): 9303-9311

Chao, Fengqing*, and Ajit Kumar Yadav. “Levels and trends in the sex ratio at birth and missing female births for 29 states and union territories in India 1990–2016: A Bayesian modeling study.” Foundations of Data Science 1, no. 2 (2019): 177-196

Brown, Peter, RELISH Consortium, Yaoqi Zhou*. “Large expert-curated database for benchmarking document similarity detection in biomedical literature search.” Database 2019 (2019): baz138.

Chao, Fengqing*, Danzhen You, Jon Pedersen, Lucia Hug, and Leontine Alkema. “National and regional under-5 mortality rate by economic status for low-income and middle-income countries: a systematic assessment.” Lancet Global Health 6, no. 5 (2018): e535-e547

Alkema, Leontine*, Fengqing Chao, Danzhen You, Jon Pedersen, and Cheryl C. Sawyer. “National, regional, and global sex ratios of infant, child, and under-5 mortality and identification of countries with outlying ratios: a systematic assessment.” Lancet Global Health 2, no. 9 (2014): e521-e530

Chao, Fengqing*, and Leontine Alkema*. “How informative are vital registration data for estimating maternal mortality? A Bayesian analysis of WHO adjustment data and parameters.” Statistics and Public Policy 1, no. 1 (2014): 6-18