Applied Statistics

This work compares current moment-matching techniques used to calibrate inertial sensors and identifies the optimal technique from a theoretical (and applied) perspective

This work studies the Californian worker's compensation termination schemes and shows that the current schemes are inadequate and need to be adjusted

This work combines the information from multiple signals to improve the estimation of stochastic model estimation for sensor calibration

This publication contributes to work in applied statistics, signal processing, with a focus reflected in its title: A Two-Step Computationally Efficient Procedure for IMU Classification and Calibration.

This publication contributes to work in applied statistics, model selection, with a focus reflected in its title: Is Nonmetastatic Cutaneous Melanoma Predictable Through Genomic Biomarkers?.

This publication contributes to work in applied statistics, model selection, with a focus reflected in its title: Differentiating Inflammatory Bowel Diseases by Using Genomic Data: Dimension of the Problem and Network Organization.

This publication contributes to work in applied statistics, model selection, with a focus reflected in its title: A Paradigmatic Regression Algorithm for Gene Selection Problems.

This publication contributes to work in applied statistics, signal processing, model selection, with a focus reflected in its title: An Inertial Sensor Calibration Platform to Estimate and Select Error Models.

This publication contributes to work in applied statistics, signal processing, with a focus reflected in its title: A Computationally Efficient Platform for Inertial Sensor Calibration.

This publication contributes to work in applied statistics, signal processing, model selection, with a focus reflected in its title: Automatic and Computationally Efficient Method for Model Selection in Inertial Sensor Calibration.