We present two Bayesian compressive sensing (BCS) imputation methods, BCS-on-Signal
and BCS-on-IMF, and compare to temporal and spatio-temporal methods. We build sparse …

Spatio-temporal forecasting has various applications in climate, transportation, geo-
statistics, sociology, economics and in many other fields of study. The modelling of …

Supplement to “Spatio-temporal assimilation of modelled catchment loads with monitoring
data in the Great Barrier Reef”. The supplementary material contains additional information …

In this article, we propose a regression model for sparse high‐dimensional data from
aggregated store‐level sales data. The modeling procedure includes two sub‐models of …

SUMMARY The univariate Box-Jenkins models ended up being extremely helpful in
expansive range of time series analysis. Since these models are univariate, they are …

Understanding how health care costs vary across different demographics and health
conditions is essential to developing policies for health care cost reduction. It may not be …

With the rapid advances of data acquisition techniques, spatio-temporal data are becoming
increasingly abundant in a diverse array of disciplines. Here, we develop spatio-temporal …

In this study, we develop a new semiparametric approach to model geostatistical data
measured repeatedly over time. In addition, we draw inferences about the parameters and …

Incorporating covariates in the second‐structure of spatial processes is an effective way of
building flexible nonstationary covariance models. Fitting these covariances requires …

Statistical Methods for Data with Complex Dependence Structure by Ting Fung Ma A
dissertation submitted in partial fulfillment o Page 1 Statistical Methods for Data with …