Forecasting of air quality in Delhi using principal component regression technique

Robust Principal Component Regression

In this note we introduce a method for robust principal component regression. Robust principal components are computed from the predictor variables, and they are used afterwards for estimating a response variable by performing robust linear multiple regression. The performance of the method is evaluated at a test data set from geochemistry. Then it is used for the prediction of censored values ...

Forecast comparison of principal component regression and principal covariate regression

Forecasting with many predictors is of interest, for instance, in macroeconomics and finance. This paper compares two methods for dealing with many predictors, that is, principal component regression (PCR) and principal covariate regression (PCovR). The forecast performance of these methods is compared by simulating data from factor models and from regression models. The simulations show that, ...

Estimation of Ambient Air Quality in Delhi

Bootstrapping Principal Component Regression Models

Bootstrap methods can be used as an alternative for cross-validation in regression procedures such as principal component regression (PCR). Several bootstrap methods for the estimation of prediction errors and confidence intervals are presented. It is shown that bootstrap error estimates are consistent with cross-validation estimates but exhibit less variability. This makes it easier to select ...

Sketching for Principal Component Regression

Principal component regression (PCR) is a useful method for regularizing linear regression. Although conceptually simple, straightforward implementations of PCR have high computational costs and so are inappropriate when learning with large scale data. In this paper, we propose efficient algorithms for computing approximate PCR solutions that are, on one hand, high quality approximations to the...