To estimate the true treatment effect on a censored outcome in observational studies, potential confounding effect and complex heterogeneity in the treatment assignment have to be properly adjusted. In this article, w...To estimate the true treatment effect on a censored outcome in observational studies, potential confounding effect and complex heterogeneity in the treatment assignment have to be properly adjusted. In this article, we demonstrate that the partial least squares method could be a valuable tool in this regard. It is showed that the partial least squares method not only can adequately account for the heterogeneity in treatment assignment, but also be robust to treatment assignment model misspecifications. Numerical results show that the partial least squares estimator is more efficient and robust. A real data set is analyzed to illustrate the proposed method.展开更多
A fast, non-destructive and eco-friendly method was developed to simultaneously determine the oil and water contents of soybean based on low field nuclear magnetic resonance（LF-NMR） relaxometry combined with chemome...A fast, non-destructive and eco-friendly method was developed to simultaneously determine the oil and water contents of soybean based on low field nuclear magnetic resonance（LF-NMR） relaxometry combined with chemometrics, such as partial least squares regression（PLSR）. The Carr-Purcell-Meiboom-Gill（CPMG） magnetiza- tion decay data of ten soybean samples were acquired by LF-NMR and directly applied to the PLSR analysis. Cali- bration models were established via PLSR with full cross-validation based on the reference values obtained by the Soxhlet extraction method for measuring oil and oven-drying method for measuring water. The results indicate that the calibration models are satisfactory for both oil and water determinations; the root mean squared errors of cross-validation（RMSECV） for oil and water are 0.2285% and 0.0178%, respectively. Furthermore, the oil and water contents in unknown soybean samples were predicted by the PLSR models and the results were compared with the reference values. The relative errors of the predicted oil and water contents were in ranges of 1.25%---4.96% and 0.44%--2.49%, respectively. These results demonstrate that the combination of LF-NMR relaxometry with chemo- metrics shows great potential for the simultaneous determination of contents of oil and water in soybean with high accuracy.展开更多
基金Supported by the National Natural Science Foundation of China(11501578,11701571)
文摘To estimate the true treatment effect on a censored outcome in observational studies, potential confounding effect and complex heterogeneity in the treatment assignment have to be properly adjusted. In this article, we demonstrate that the partial least squares method could be a valuable tool in this regard. It is showed that the partial least squares method not only can adequately account for the heterogeneity in treatment assignment, but also be robust to treatment assignment model misspecifications. Numerical results show that the partial least squares estimator is more efficient and robust. A real data set is analyzed to illustrate the proposed method.
基金Supported by the National Natural Science Foundation of China（No.91227126） and the National Special Fund for Key Scientific Instrument and Equipment Development of China（No.2013YQ17046307）.
文摘A fast, non-destructive and eco-friendly method was developed to simultaneously determine the oil and water contents of soybean based on low field nuclear magnetic resonance（LF-NMR） relaxometry combined with chemometrics, such as partial least squares regression（PLSR）. The Carr-Purcell-Meiboom-Gill（CPMG） magnetiza- tion decay data of ten soybean samples were acquired by LF-NMR and directly applied to the PLSR analysis. Cali- bration models were established via PLSR with full cross-validation based on the reference values obtained by the Soxhlet extraction method for measuring oil and oven-drying method for measuring water. The results indicate that the calibration models are satisfactory for both oil and water determinations; the root mean squared errors of cross-validation（RMSECV） for oil and water are 0.2285% and 0.0178%, respectively. Furthermore, the oil and water contents in unknown soybean samples were predicted by the PLSR models and the results were compared with the reference values. The relative errors of the predicted oil and water contents were in ranges of 1.25%---4.96% and 0.44%--2.49%, respectively. These results demonstrate that the combination of LF-NMR relaxometry with chemo- metrics shows great potential for the simultaneous determination of contents of oil and water in soybean with high accuracy.
