Purpose: preliminary discussion on model prediction precision in the partial least squares regression analysis method; Method: introduce current development conditions of partial least squares regression analysis, analyze problems of traditional regression analysis method such as multiple linear regression analysis, introduce the mathematic principle and modeling method of the partial least squ...

Abstract Hedonic models in environmental valuation studies have grown in terms of number of transactions and number of explanatory variables.We focus on the practical challenge of model reduction, when aiming for reliable parsimonious models, sensitive to omitted variable bias and multicollinearity. We evaluate two common model reduction approaches in an empirical case. The first relies on a pr...

In classical multiple linear regression analysis problems will occur if the regressors are either multicollinear or if the number of regressors is larger than the number of observations. In this note a new method is introduced which constructs orthogonal predictor variables in a way to have a maximal correlation with the dependent variable. The predictor variables are linear combinations of the...

Batch-to-batch iterative learning control of a fed-batch fermentation process using batchwise linearised models identified from process operation data is presented in this paper. Due to model-plant mismatches and the present of unknown disturbances, off-line calculated control policy may not be optimal when implemented to the real process. The repetitive nature of batch process allows informati...

In the multivariate calibration framework we revisit and investigate the prediction performance of three high-dimensional modeling strategies: partial least squares, principal component regression and P-spline signal regression. Specifically we are interested in comparing the stability and robustness of prediction under differing conditions, e.g. training the model under one temperature and usi...

partial least square and principal component regression methods were applied to various mixtures of Allura Red and Brilliant Blue to determine the concentrations. Colorants, at the same time, were analyzed with UV-spectrophotometry in chemical separation. The obtained experimental data have been evaluated by chemometric methods as Partial Least Squares (PLS) and Principle Component Regressi...

When combining a set of learned models to form an improved estimator, the issue of redundancy in the set of models must be addressed. Existing methods for addressing this problem have failed to perform robustly, especially as the redundancy in the set of learned models increases. Recently, a variant of principal components regression, PCR*, demonstrated that these limitations could be overcome ...

an evaluation of the relationship between crop yield and soil properties would be useful in estimating the fluctuations in yield, and in an implementation of correct field management. the study was conducted in farmer operated wheat fields in sorkhankalateh district, 25 km northeast of gorgan, golestan province, iran. soil samples (0-30 cm depth) were collected just after crop planting at the e...

The paper investigates how innovation differs across manufacturing industries in Europe. It uses the rich set of information available from the CIS-SIEPI database, a new dataset which includes CIS2 data on the innovative activity of 22 manufacturing sectors in ten European countries. The results of principal component and cluster analyses suggest the existence of four distinct sectoral patterns...

Multiple regression with correlated predictor variables is relevant to a broad range of problems in the physical, chemical, and engineering sciences. Chemometricians, in particular, have made heavy use of principal components regression and related procedures for predicting a response variable from a large number of highly correlated predictors. In this paper we develop a general theory that gu...