SiAM: A hybrid of single index models and additive models

Single-Index Additive Vector Autoregressive Time Series Models

We study a new class of nonlinear autoregressive models for vector time series, where the current vector depends on single-indexes defined on the past lags and the effects of different lags have an additive form. A sufficient condition is provided for stationarity of such models. We also study estimation of the proposed model using P-splines, hypothesis testing, asymptotics, selection of the or...

On the Identifiability of Additive Index Models

In this paper, we investigate the identifiability of the additive index model, also known as projection pursuit regression. Although a flexible regression tool, additive index models can be hard to interpret in practice due to a lack of identifiability. As noted by Horowitz (1998), “it is an open question whether there are identifying restrictions that yield useful forms”, in reference to addit...

a study on thermodynamic models for simulation of 1,3 butadiene purification columns

attempts have been made to study the thermodynamic behavior of 1,3 butadiene purification columns with the aim of retrofitting those columns to more energy efficient separation schemes. 1,3 butadiene is purified in two columns in series through being separated from methyl acetylene and 1,2 butadiene in the first and second column respectively. comparisons have been made among different therm...

Target setting in additive models with preferences and interval data

High dimensional single index models

This paper addresses the problem of fitting nonlinear regression models in high-dimensional situations, where the number of predictors, p, is large relative to the number of observations, n. Most of the research in this area has been conducted under the assumption that the regression function has a simple additive structure. This paper focuses instead on single index models, which are becoming ...