An improved independent component regression (M-ICR) algorithm is proposed by constructing joint latent variable (LV) based regressors, and a quantitative statistical analysis procedure is designed using a bootstrap technique for model validation and performance evaluation. First, the drawbacks of the conventional regression modeling algorithms are analyzed. Then the proposed M-ICR algorithm is formulated for regressor design. It constructs a dual-objective optimization criterion function, simultaneously incorporating quality-relevance and independence into the feature extraction procedure. This ties together the ideas of partial-least squares (PLS), and independent component regression (ICR) under the same mathematical umbrella. By adjusting the controllable suboptimization objective weights, it adds insight into the different roles of quality-relevant and independent characteristics in calibration modeling, and, thus, provides possibilities to combine the advantages of PLS and ICR. Furthermore, a quantitative statistical analysis procedure based on a bootstrapping technique is designed to identify the effects of LVs, determine a better model rank and overcome ill-conditioning caused by model over-parameterization. A confidence interval on quality prediction is also approximated. The performance of the proposed method is demonstrated using both numerical and real world data. (C) 2009 American Institute of Chemical Engineers AIChE J, 56: 1519-1535, 2010