Applied Statistics

A Multi-Model Framework to Explore ADHD Diagnosis From Neuroimaging Data

This publication contributes to work in applied statistics, model selection, with a focus reflected in its title: A Multi-Model Framework to Explore ADHD Diagnosis From Neuroimaging Data.

Artificial Neural Network-Empowered Projected Future Rainfall Intensity-Duration-Frequency Curves Under Changing Climate

This publication contributes to work in applied statistics, with a focus reflected in its title: Artificial Neural Network-Empowered Projected Future Rainfall Intensity-Duration-Frequency Curves Under Changing Climate.

Stellar Blend Image Classification Using Computationally Efficient Gaussian Processes (MuyGPs)

This publication contributes to work in applied statistics, model selection, with a focus reflected in its title: Stellar Blend Image Classification Using Computationally Efficient Gaussian Processes (MuyGPs).

Honey Bee Colony Loss Linked to Parasites, Pesticides and Extreme Weather Across the United States

This publication contributes to work in applied statistics, with a focus reflected in its title: Honey Bee Colony Loss Linked to Parasites, Pesticides and Extreme Weather Across the United States.

Winter Weather Predicts Honey Bee Colony Loss at the National Scale

This publication contributes to work in applied statistics, with a focus reflected in its title: Winter Weather Predicts Honey Bee Colony Loss at the National Scale.

Within-field variability in nutrients for site-specific agricultural management in irrigated cornfield

This work underlines how spatial variability in nutrients causes spatial variability in plant growth and crop yield across cornfields, confirming that these need to be considered when delineating management zones before adopting management practices

Evidence of antagonistic predictive effects of miRNAs in breast cancer cohorts through data-driven networks

This work attempts to explain some contradictory results that have been found regarding the oncogenic or protective roles of miRNAs in breast cancer progression

Stochastic Sensor Calibration

There is considerable research on improving navigation accuracy of unmanned (aerial) vehicles. This direction of research focuses on improving stochastic sensor calibration to integrate navigation filters and produce more accurate navigation performance.

Chameleon microRNAs in Breast Cancer: Their Elusive Role as Regulatory Factors in Cancer Progression

This publication contributes to work in applied statistics, model selection, with a focus reflected in its title: Chameleon microRNAs in Breast Cancer: Their Elusive Role as Regulatory Factors in Cancer Progression.

Swag: A Wrapper Method for Sparse Learning

This publication contributes to work in applied statistics, model selection, with a focus reflected in its title: Swag: A Wrapper Method for Sparse Learning.