International Business Machines Corporation
DIVIDE-AND-CONQUER FRAMEWORK FOR QUANTILE REGRESSION

Abstract:

A method is presented for estimating conditional quantile values of a response variable distribution. The method includes acquiring training data with first values and second values, a list of quantile levels, a lower bound of the second values, and an upper bound of the second values and transforming the list of quantile levels into a tree-structure by recursively dividing an interval in a range between 0 and 1 into sub-intervals by using the list of quantile levels such that each node of the tree-structure is associated with a tuple of three quantile levels. The method further includes training a neural network for each node in the tree-structure and estimating a relative quantile value for each of the first values by using a first estimated quantile value as a lower bound and a second estimated quantile value as an upper bound.