Research

My research interest lies in the intersection of the theory of survey sampling and statistical learning. I aim to develop rigorous statistical methods so that researchers increase the precision of their analysis by integrating auxiliary data sets, incorporating machine learning algorithms or interacting with modern language models.

Publications

  1. Leedy, C., Haziza, D., Dagdoug, M., Salvati, N., Reluga, K. (2026+). Machine learning in Fay–Herriot models with exact MSPE estimation. In progress.
  2. Leedy, C., Chen, H. (2026+). Small Area Estimation with Time Series. In progress.
  3. Leedy, C., Kim, J.K. (2026+). Bregman Calibration for Generalized Two-Phase Sampling. In progress.
  4. Leedy, C., Kim, J.K. (2026+). Data Integration Using Propensity Score Weighting Under Missing at Random. Under review in the Journal of the Royal Statistical Society Series A: Statistics in Society.
  5. Blanchard, J.D., Leedy, C., Wu, Y. (2020). On rank awareness, thresholding, and MUSIC for joint sparse recovery. Applied and Computational Harmonic Analysis, 48(1), 482–495.

Talks

  • Small Area Estimation and Data IntegrationInvited Talk, Statistics Canada, Ottawa, Canada — November 2026
  • Small area estimation with machine learning algorithmsInvited Talk, SAE 2026, Bucharest, Romania — June 2026
  • Nonparametric methods within area-level modelsInvited Talk, SSC Annual Meeting, Hamilton, Canada — June 2026
  • Debiased Calibration for Generalized Two-Phase SamplingPoster, Joint Statistical Meetings, Portland, USA (SRMS Poster Award) — August 2024
  • Debiased Nonparametric Regression in Two-Phase SamplingInvited Talk, CANSSI-CRT Workshop on Modern Methods in Survey Sampling, Ottawa, Canada — July 2024