Multivariate calibration with temperature interaction using two-dimensional penalized signal regression
نویسندگان
چکیده
The Penalized Signal Regression (PSR) approach to multivariate calibration (MVC) assumes a smooth vector of coefficients for weighting a spectrum to predict the unknown concentration of a chemical component. B-splines and roughness penalties, based on differences, are used to estimate the coefficients. In this paper, we extend PSR to incorporate a covariate like temperature. A smooth surface on the wavelength– temperature domain is estimated, using tensor products of B-splines and penalties along the two dimensions. A slice of this surface gives the vector of weights at an arbitrary temperature. We present the theory and apply multi-dimensional PSR to a published data set, showing good performance. We also introduce and apply a simplification based on a varying-coefficient model (VCM). D 2003 Elsevier Science B.V. All rights reserved.
منابع مشابه
Multivariate Calibration Stability: a Comparison of Methods
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...
متن کاملMultidimensional Single-Index Signal Regression
In general, linearity is assumed to hold in multivariate calibration (MVC), but this may not be true. We approach the MVC problem using multidimensional penalized signal regression, which can be extended with an explicit link function between linear prediction and response and in the spirit of single-index models. As the twodimensional surface of calibration coefficients is smoothly and general...
متن کاملComparison of Ordinal Response Modeling Methods like Decision Trees, Ordinal Forest and L1 Penalized Continuation Ratio Regression in High Dimensional Data
Background: Response variables in most medical and health-related research have an ordinal nature. Conventional modeling methods assume predictor variables to be independent, and consider a large number of samples (n) compared to the number of covariates (p). Therefore, it is not possible to use conventional models for high dimensional genetic data in which p > n. The present study compared th...
متن کاملSimultaneous spectrophotometric determination of ampicillin and penicillin in human plasma using multivariate calibration
An analytical methodology based on spectrophotometric and partial least squares (PLS) algorithm for thesimultaneous determination of ampicillin and penicillin in human plasma was developed and validated. Themultivariate model was developed as a binary calibration model and it was built and validated with anindependent set of synthesis and real samples in presence of matrix. It is shown how a de...
متن کاملPenalized Estimators in Cox Regression Model
The proportional hazard Cox regression models play a key role in analyzing censored survival data. We use penalized methods in high dimensional scenarios to achieve more efficient models. This article reviews the penalized Cox regression for some frequently used penalty functions. Analysis of medical data namely ”mgus2” confirms the penalized Cox regression performs better than the cox regressi...
متن کامل