A list of papers is also on Google Scholar.

Peer-reviewed publications

• Cresswell, J.A., Alexander, M., Chong, M.Y., Link, H.M., Pejchinovska, M., Gazeley, U., et al., ‘Global and regional causes of maternal deaths 2009–20: a WHO systematic analysis.’ The Lancet Global Health, 2025. doi: 10.1016/S2214-109X(24)00560-6
• Dharamshi, A., Alexander, M., Winant, C. and Barbieri, M. ‘Jointly Estimating Subnational Mortality for Multiple Populations’. Demographic Research, 2025. doi: 10.4054/DemRes.2025.52.3.
• Chong, M. Y., Pejchinovska, M., & Alexander, M. ‘Estimating causes of maternal death in data‐sparse contexts’. Statistics in Medicine., 2024. doi: https://doi.org/10.1002/sim.10199
• Alexander, M. and Raftery, A.E., ‘Developing and implementing the UN’s probabilistic population projections as a milestone for Bayesian demography: An interview with Adrian Raftery.’ Demographic Research, 2024. 51, pp.1-16.
• Kiang M.V., Alexander M.J.. ‘Invited commentary: Motivating better methods — and better data collection — for measuring prevalence of drug misuse’, American Journal of Epidemiology. 2024. doi: 10.1093/aje/kwae156.
• Schlüter, B. S., Alburez-Gutierrez, D., Bibbins-Domingo, K., Alexander, M. J., and Kiang, M. V. ‘Youth experiencing parental death due to drug poisoning and firearm violence in the US, 1999-2020’. JAMA, 2024.
• Chong, M.Y.C., and Alexander, M.. ‘Estimating the timing of stillbirths in countries worldwide using a Bayesian hierarchical penalized splines regression model’. Journal of the Royal Statistical Society, Series C, 2024. qlae017.
• Katz, L., Chong, M., and Alexander, M.. ‘Measuring short-term mobility patterns in North America using Facebook Advertising data, with an application to adjusting Covid-19 mortality rates’. Demographic Research, 2024: 50(10), 291-394.
• Yeung, J., Alexander, M., and Riffe, T. ‘Bayesian implementation of Rogers-Castro model migration schedules: An alternative technique for parameter estimation’. Demographic Research, 2023: 49(42), 1201-1228.
• Alexander, M., and Alkema, L., ‘A Bayesian cohort component projection model to estimate adult populations at the subnational level in data-sparse settings’, Demography, 2022: 59(5), 1713-1737.
• Maheu-Giroux, M., Sardinha, L., Stockl, H., Meyer, S., Godin, A., Alexander, M., and Garcia-Moreno, C. ‘A framework to model global, regional, and national estimates of intimate partner violence’, BMC Medical Research Methodology, 2022: 22(1), 1-17.
• Susmann, H., Alexander, M., and Alkema, L., Temporal models for demographic and global health outcomes in multiple populations: Introducing a new framework to review and standardize documentation of model assumptions and facilitate model comparison, International Statistical Review, 2022: 90(3), 437-467.
• Alexander, M. and Root, L. ‘Competing effects on the average age of infant death’. Demography, 2022: 59(2), 587-605.
• Kiang, M.V., Tsai A.C., Alexander M., Rehkopf D.H., Basu S. ‘Racial/Ethnic Disparities in Opioid-Related Mortality in the USA, 1999–2019: the Extreme Case of Washington DC.’ Journal of Urban Health, 2021: 98(6), 589.
• Wilson, T., Grossman, I., Alexander, M., Rees, P., and Temple, J. ‘Methods for small area population forecasts: state-of-the-art and research needs’, Population Research and Policy Review, 2021: 41, 865-898.
• Kiang M.V., Chen J.T., Krieger N., Buckee C.O., Alexander, M., Baker J.T., Buckner R.L., Coombs III G., Rich-Edwards J.W., Carlson K.W., and Onnela J-P. ‘Sociodemographic characteristics of missing data in digital phenotyping’. Scientific Reports, 2021: 11(1),15408.
• Alexander, M., Polimis, K., and Zagheni, E., ‘Combining social media and survey data to nowcast migrant stocks in the United States’, Population Research and Policy Review, 2020: 41, 1-28.
• Alexander, M., Polimis, K., and Zagheni, E., ‘The impact of Hurricane Maria on out-migration from Puerto Rico: Evidence from Facebook data’, Population and Development Review, 2019: 45(3), 617-630.
• Hug, L., Alexander, M., You, D., and Alkema, L. ‘National, regional, and global levels and trends in neonatal mortality between 1990 and 2017, with scenario-based projections to 2030: a systematic analysis by the United Nations Inter-agency Group for Child Mortality Estimation’, Lancet Global Health, 2019, 7(6): 710-720.
• Kiang, M.V., Basu S., Chen J., and Alexander, M. ‘Assessment of Changes in the Geographical Distribution of Opioid-Related Mortality Across the United States by Opioid Type, 1999-2016’. JAMA Network Open. 2019, 2(2):e190040.
• Gemmill, A., Kiang, M.V., and Alexander, M., ‘Trends in pregnancy-associated mortality involving opioids in the United States, 2007–2016’, American Journal of Obstetrics and Gynecology, 2019, 220(1): 115-116.
• Alexander, M., Kiang, M.V., and Barbieri M., ‘Trends in Black and White Opioid Mortality in the United States, 1979–2015’, Epidemiology, 2018, 29(5): 707–715.
• Alexander, M., and Alkema, L., ‘Global Estimation of Neonatal Mortality using a Bayesian Hierarchical Splines Regression Model’, Demographic Research, 2018, 38(15): 335–372.
• Alexander, M., Zagheni, E., and Barbieri, M., ‘A Flexible Bayesian Model for Estimating Subnational Mortality’, Demography, 2017, 54(6): 2025–2041.
• Howlett, M., Gray, M., and Hunter, B., ‘Wages, government payments and other income of Indigenous and non-Indigenous Australians’, Australian Journal of Labour Economics, 2016, 19(2): 53–76.
• Hunter, B., Howlett, M., and Gray, M., ‘The Economic Impact of the Mining Boom on Indigenous and Non-Indigenous Australians’, Asia & the Pacific Policy Studies, 2015, 2(3): 517–530.
• Gray, M., Howlett, M., and Hunter, B., ‘Labour Market Outcomes for Indigenous Australians’, The Economic and Labour Relations Review, 2014, 25(3): 497–517.
• Biddle, N., Howlett, M., Hunter, B., and Paradies, Y., ‘Labour Market and Other Discrimination Facing Indigenous Australians’, Australian Journal of Labour Economics, 2013, 16(1): 91–113.

Book chapters

• Alexander, M. ‘Using social media advertising data to estimate migration trends over time’. In Big Data Applications in Geography and Planning. Edward Elgar Publishing. 2021.

Working papers, projects, and pre-prints

• Pejchinovska, M., and Alexander, M.. ‘A Bayesian framework to account for misclassification error and uncertainty in the estimation of abortion incidence’.
• Badolato, L., Decter-Frain, A. G., Irons, N., Miranda, M. L., Walk, E., Zhalieva, E., Alexander, M., Basellini, U., Zagheni, E. (2023). ‘Predicting individual-level longevity with statistical and machine learning methods.’.
• Alexander, M. and Root, L. ‘Racial disparities in fetal and infant outcomes: a multiple-decrement life table approach’.
• Alexander, M., ‘Decomposing dimensions of mortality inequality’.
• Chong, M., Alburez-Gutierrez, D., Del Fava, E., Alexander, M., & Zagheni, E. (2022). ‘Identifying and correcting bias in big crowd-sourced online genealogies.
• Alexander, M., Chong, M.Y.C., and Pejcinovska, M. ‘Estimating causes of maternal death in data-sparse contexts’.
• Alexander, M., Wildeman, C., Roehrkasse, A., and Rudlang-Perman, K. ‘Forecasting child welfare outcomes in the United States’. Shiny app; Paper presented at PAA 2022.
• Alexander, R., and Alexander, M.. ‘The Increased Effect of Elections and Changing Prime Ministers on Topics Discussed in the Australian Federal Parliament between 1901 and 2018’.
• Alexander, M., and Goldstein, J., ‘Deaths without denominators: using a matched dataset to study mortality patterns in the United States’.

Working papers where there is not so much working happening

• Alexander, M., ‘Temporal smoothers for use in demographic estimation and projection’. This paper turned into another paper (Susmann et al., above). I wrote an R package to accompany the paper, which can be found on my GitHub. One day I might translate the models from JAGS to Stan.
• Chung, P.H., and Alexander, M., ‘Kin Dependency Ratios: An Extension and Application of the Goodman Method for Estimating the Availability of Kin’.

Talks

Here are recordings of some talks:

• Using Facebook advertising data to estimate migration
• Overcoming barriers to sharing code
• Estimating subnational populations in data-sparse contexts

Code and other fun projects

• A package to fit Rogers-Castro migration models, rcbayes, is on CRAN.
• I am a contributor to the DemoTools R package.
• I made a children’s book in R called ’D is for Demography’. My toddler prefers ‘The Very Hungry Caterpillar’ or ‘Wombat Stew’.
• This repo on my GitHub has various bits and pieces of code, mostly related to mortality, but some other stuff too.
• Shiny app to explore baby names in Australia, Ontario, and USA