Beta-Liouville Regression and Applications

摘要

A novel regression algorithm has been proposed, Beta-Liouville regression, to solve regression of compositional data, where the prediction is multi-dimensional and sums to unity. Applications include market-share data mining in relation to its score in Google-trends and smart building occupancy estimation. The study of Google-trends in relation to market-shares gives a good estimate of whether the company's investment in advertisements have yielded any results in improving their market-shares. Secondly, occupant behaviour in buildings gives useful insights on the required levels of air conditioning, lighting and even initiating help during emergency. Sensors to estimate the occupancy of a smart building include microphone, door/window positions, motion detection, power consumption. The Beta-Liouville regression algorithm is compared to ordinary least squares regression with compositional transformations and Dirichlet regression.