Small area estimation using reduced rank regression models

Estimation Procedure for Reduced Rank Regression, PLSSVD

This paper presents a procedure for coefficient estimation in a multivariate regression model of reduced rank in the presence of multicollinearity. The procedure permits the prediction of the dependent variables taking advantage of both Partial Least Squares (PLS) and Singular Value Decomposition (SVD) methods, which is denoted by PLSSVD. Global variability indices and prediction error sums are...

Small area estimation using skew normal models

Valmária Rocha da Silva ∗ Fernando Antônio da Silva Moura † Abstract The main aim of this work is to propose two important connected extensions of the Fay and Heriot (1979) area level small area estimation model that might be of practical and theoretical interests. The first extension allows for the sampling error to be non-symmetrically distributed. This is important for the case that the samp...

Nonparametric Small Area Estimation Using Penalized Spline Regression

We propose a new small area estimation approach that combines small area random effects with a smooth, nonparametrically specified trend. By using penalized splines as the representation for the nonparametric trend, it is possible to express the small area estimation problem as a mixed effect regression model. We show how this model can be fitted using existing model fitting approaches such as ...

Local Polynomial Regression for Small Area Estimation

Estimation of small area means in the presence of area level auxiliary information is considered. A class of estimators based on local polynomial regression is proposed. The assumptions on the area level regression are considerably weaker than standard small area models. Both the small area mean functions and the between area variance function are modeled as smooth functions of area level covar...

Nonparametric regression methods for small area estimation