Graph estimation with joint additive models

Graph Estimation with Joint Additive Models.

In recent years, there has been considerable interest in estimating conditional independence graphs in high dimensions. Most previous work has assumed that the variables are multivariate Gaussian, or that the conditional means of the variables are linear; in fact, these two assumptions are nearly equivalent. Unfortunately, if these assumptions are violated, the resulting conditional independenc...

Estimation in Additive Models with Highly or Nonhighly Correlated Covariates

Motivated by normalizing DNA microarray data and by predicting the interest rates, we explore nonparametric estimation of additive models with highly correlated covariates. We introduce two novel approaches for estimating the additive components, integration estimation and pooled backfitting estimation. The former is designed for highly correlated covariates, and the latter is useful for nonhig...

Recursive estimation in autoregressive models with additive outliers

This work deals with recursive robust estimation in autoregressive models that are contaminated by additive outliers. The importance of such procedures in applied time series is obvious: (i) the recursive character of the estimation allows to treat time series in real time (on-line) updating previous estimates by means of simple calculations after delivering new observations; (ii) robustness of...

Model-assisted Estimation of Forest Resources with Generalized Additive Models

Multi-phase surveys are often conducted in forest inventory, with the goal of estimating forested area and tree characteristics over large regions. This article describes how design-based estimation of such quantities, based on information gathered during ground visits of sampled plots, can be made more precise by incorporating auxiliary information available from remote sensing. The relationsh...

Target setting in additive models with preferences and interval data