Summary
My doctoral research was in the intersection of model-based clustering and incomplete data via multiple imputation.
- Clustering: Finding grouping patterns in data, with no prior knowledge of group labels.
- Model-based clustering: Finding groups, and describing them with statistical models.
- Incomplete data: Holes or gaps in a data set result in incomplete records, which are often thrown out before analysis begins.
- Multiple imputation: Create multiple “guesses” at the missing data values, and obtain a single result for your analysis that takes into account all guesses.
I am always looking for new projects and collaborations! If you have an idea, please send me an email: larosec@newpaltz.edu
Publications & Research
- Larose, C., Harel, O., Kordas, K., Dey, D. K. (2016) Latent class analysis of incomplete data via an entropy-based criterion. Statistical Methodology 32, 107 – 121.
- Belliveau, T., Jette, A. M., Seetharama, S., Axt, J., Rosenblum, D., Larose, D., Houlihan, B., Slavin, M., Larose, C. (2016) Developing artificial neural network models to predict functioning one year after traumatic spinal cord injury. Archives of Physical Medicine and Rehabilitation. In press.
- Larose, C., Dey, D. K., Harel, O. The Impact of Incomplete Data on Measures of Uncertainty. Submitted.
- Larose, C., Harel, O., Yan, J. Estimating Regression Coefficient Change in Incomplete Data. In preparation.
- Larose, C., Dey, D. K., Harel, O. Clustering incomplete normal mixture data without pre-specification of the number of clusters. In preparation.