Data analytics, statistics, and more
Use a stochastic partial differential equation (SPDE) approach with Integrated Nested Laplace Approximation (INLA) to model the spatio-temporal behavior of PM10 concentrations measured in the North-Italian region of Piemonte.
The performance of Bayesian inference using a stochastic partial differential equation (SPDE) approach with Integrated Nested Laplace Approximation (INLA) for predicting zinc concentrations in soil at unsampled locations is compared with those obtained using kriging.
Multivariate statistical methods provide a means of exploring complex data sets for patterns and relationships from which hypotheses can be generated and subsequently tested. This post explores methods to manage non-detects when applying multivariate procedures to investigate (dis)similarities among data objects based on a set of descriptors.
Develope an unstratified, equal probability sample design and a stratified, equal probability sample design using Generalized Random Tessellation Stratified (GRTS) sampling.
Time-series analysis and forecasting is an important area of machine learning because many predictive learning problems involve a time component. This post examines time-series analysis using indoor air concentrations of trichloroethene and various explanatory varibles collected over time at a single location.