Department of Economics

AUTHOR

Stewart Connie

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Statistics , Applied Research - 2023

Flexible Non-parametric Regression Models for Compositional Response Data with Zeros

Compositional data arise in many real-life applications and versatile methods for properly analyzing this type of data in the regression context are needed. When parametric assumptions do not hold or are difficult to verify, non-parametric regression models can provide a convenient alternative method for prediction. To this end, we consider an extension to the classical k-NN regression, termed a-k-NN regression, that yields a highly flexible non-parametric regression model for compositional data through the use of the a-transformation.

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Statistics , Data Analytics - 2018

A Folded Model for Compositional Data Analysis

A folded type model is developed for analyzing compositional data based that provides a new and flexible class of distributions for modeling data defined on the simplex sample space. Despite its rather seemingly complex structure, employment of the EM algorithm guarantees efficient parameter estimation.

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