For regularly spaced one-dimensional data wavelet shrinkage has proven to be a compelling method for nonparametric function estimation. We argue that this is not the case for irregularly spaced data in two or higher dimensions. This article develops three methods for the multiscale analysis of irregularly spaced data based on the recently developed lifting paradigm by “lifting one coefficient a...

In this article, the authors derive likelihood-based exact inference for singly and multiply imputed synthetic data in context of a multivariate regression model. The are generated via Plug-in Sampling method, where unknown parameters model set equal to observed values their point estimators based on original data, drawn from estimated version Simulation studies carried out order confirm theore...

Multivariate Adaptive Regression Splines (MARS) is a supervised learning model in machine learning, not obtained by an ensemble method. Ensemble methods are gathered from samples comprising hundreds or thousands of learners that serve the common purpose improving stability and accuracy algorithms. This study presented REMARS (Random MARS), new MARS selection approach using Random Forest (RF) al...