My research interests include Bayesian statistics, complex lifetime data modeling, causal inference, mediation analysis, and missing data problems. I am also interested in applied research in general and have had some experience on biostatistics, environmental and behavioral sciences. For more details, please refer to my .
Bayesian statistics is a theory in the field of statistics based on the Bayesian interpretation of probability where probability expresses a degree of belief in an event. Bayesian methods update prior beliefs on distributions or model parameters upon observation of data. Specifically, I have used Bayesian nonparametric methods in obtaining inferences for lifetime modeling and Bayesian latent variable methods for missing data imputation. Here Bayesian nonparametric methods refer to classes of flexible priors on the space of distributions, for example, Polya trees, Dirichlet processes, and splines.
Broadly, lifetime modeling is a category of research dealing with time-to-event data, which typically records the lengths of times among events of interest, such as mechanical failures, heart attacks, and cancer relapses. Often subjects are monitored over multiple time points as well as their locations; hence, longitudinal and spatial dimensions are added to the data correlation structure. A significant number of scientific questions arise in understanding the effects of risk factors on time distributions of events. Besides inferential results on the risk factors' effects, statistical studies also typically extend to diagnostics on model assumptions and to predictions for unknown responses of interest. I have five publications on lifetime modeling or hypothesis tests.
Causal inference is the process of concluding a causal connection based on the conditions of the occurrence of an effect and mediation analysis searches for mechanisms behind causes and effects. Several application studies in which I am participating, aim to investigate the effects of heavy metal exposures on Navajo native Indian children's neuro-developmental assessments. Causal inference plays a key role in disentangling the effects of metals.
Missing data often occur in data collection due to nonresponse- no information provided for one or more items or a whole unit, mistakes made in data entry, and even sample corruption. Most methods and theories were developed for full data, and they could be much more complicated if they are needed to handle missing data properly. I have one publication on extending methods for missing data.
My biostatistical/environmental collaborations have focused on investigating the effects of heavy metal exposures on Navajo native Indian children's neuro-developmental assessments (work with UNM METALS Superfund Research Program Center). I have coauthored a paper on neuro-developmental assessments and several manuscripts are under development.
I am interested in developing and applying statistical methods for psychology experiments. I have one manuscript to appear on analysis of covariance with heterogeneity of regression and a random Covariate.
Li, L. Mclouth, C., Delaney, H. (2019). Analysis of Covariance with Heterogeneity of Regression and a Random Covariate: The Variance of the Estimated Treatment Effect at Selected Covariate Values. Multivariate Behavioral Research, to appear.