Successful modeling of observational data requires jointly discovering the determinants underlying process and observations from which it can be reliably estimated, given near impossibility pre-specifying both. To do so avoiding many potential problems, including substantive omitted variables; unmodeled non-stationarity misspecified dynamics in time series; non-linearity; inappropriate conditio...

Regularization schemes for regression have been widely studied in learning theory and inverse problems. In this paper, we study distribution (DR) which involves two stages of sampling, aims at regressing from probability measures to real-valued responses over a reproducing kernel Hilbert space (RKHS). Recently, theoretical analysis on DR has carried out via ridge several behaviors observed. How...

Gradient boosting algorithms construct a regression predictor using linear combination of “base learners”. Boosting also offers an approach to obtaining robust non-parametric estimators that are scalable applications with many explanatory variables. The algorithm is based on two-stage approach, similar what done for regression: it first minimizes residual scale estimator, and then improves by o...

Many biological high-throughput datasets, such as targeted amplicon-based and metagenomic sequencing data, are compositional. A common exploratory data analysis task is to infer robust statistical associations between high-dimensional microbial compositions habitat- or host-related covariates. To address this, a general regression framework RobRegCC (Robust Regression with Compositional Covaria...

Abstract Real-world datasets are often characterised by outliers; data items that do not follow the same structure as rest of data. These outliers might negatively influence modelling In analysis it is, therefore, important to consider methods robust outliers. this paper we develop a regression method finds largest subset can be approximated using sparse linear model given precision. We show yi...