Abstract: Systems and methods are provided for performing an estimation using an iteratively reweighted least squares technique on a scenario defined by a design matrix, a response variable vector, and a parameter to be estimated. A system and method can be configured to include a root data processor, where a least squares estimate is calculated by the root data processor during each of a plurality of processing iterations. A plurality of node data processors can also be configured to: update values of a weight matrix according to a current state of the least squares estimate, determine a first intermediate value based on the design matrix and the weight matrix, determine a second intermediate value based on the design and weight matrices and the response variable vector. The root data processor calculates an updated least squares estimate based on the intermediate values.

Abstract: Systems and methods are provided for performing an estimation using an iteratively reweighted least squares technique on a scenario defined by a design matrix, a response variable vector, and a parameter to be estimated. A system and method can be configured to include a root data processor, where a least squares estimate is calculated by the root data processor during each of a plurality of processing iterations. A plurality of node data processors can also be configured to: update values of a weight matrix according to a current state of the least squares estimate, determine a first intermediate value based on the design matrix and the weight matrix, determine a second intermediate value based on the design and weight matrices and the response variable vector. The root data processor calculates an updated least squares estimate based on the intermediate values.