My research interest lies in statistics and biostatistics. The primary area includes measurement error models, kernel estimation, validation data in statistics and time series. I’m also interested in neural networks and functional data analysis as well as their applications in genetics.
Publications
Geng, P. (2022). Estimation of functional-coefficient autoregressive models with measurement error, Journal of Multivariate Analysis. https://doi.org/10.1016/j.jmva.2022.105077
Geng, P. and Koul, H.L. (2022). Weighted empirical minimum distance estimators in linear errors-in-variables regression models, Journal of StatisticalPlanning and Inference, 219, 147-174.
Koul, H.L. and Geng, P. (2019). Weighted empirical minimum distance estimators in Berkson measurement error regression models, Springer Proceedings in Mathematics & Statistics – Analytical Methods in Statistics, 31-71.
Geng, P., Tong, X. and Lu, Q. (2019). An integrative U method for joint analysis of multi-level omic data, BMC-Genetics 20, 40. doi:10.1186/s12863-019-0742-z.
Geng, P. and Koul, H.L. (2019). Minimum distance model checking in Berkson measurement error models using validation data, TEST, 28(3), 879-899.
Geng, P. and Koul, H.L. (2017). Model checking in Tobit regression with measurement errors using validation data, Journal of Statistical Planning and Inference, 190, 15-31.
Geng, P. and Sakhanenko, L. (2016). Parameter estimation for the logistic regression model under case-control study, Statistics and Probability Letters, 109, 168-177.