Search results for "nonlinear regression"
showing 10 items of 21 documents
Pedotransfer functions for estimating soil water retention curve of Sicilian soils
2019
Pedotransfer functions (PTFs) make use of routinely surveyed soil data to estimate soil properties but their application to soils different from those used for their development can yield inaccurate estimates. This investigation aimed at evaluating the water retention prediction accuracy of eight existing PTFs using a database of 217 Sicilian soils exploring 11 USDA textural classes. PTFs performance was assessed by root mean square differences (RMSD) and average differences (AD) between estimated and measured data. Extended Nonlinear Regression (ENR) technique was adopted to recalibrate or develop four new PTFs and Wind’s evaporation method was applied to validate the effectiveness of the …
Note. Kinetic parameters of Bacillus stearothermophilus spores under isothermal and non-isothermal heating conditions Nota. Parámetros cinéticos del …
1998
Thermobacteriological studies using Bacillus stearotherrnophilus spores were carried out by heating the spores under isothermal and non-isothermal conditions followed by an isothermal period. Ex perimental data obtained after isothermal heating were analyzed using a two-step linear regression procedure and a one-step nonlinear regression method. Results obtained using both analytical tech niques were close, but the 90% interval of confidence for predictions was lower when the one-step nonlinear regression was used. These results indicated the convenience of using the one-step nonlin ear regression method to obtain thermal kinetic parameters for bacterial spores. Also, the parameters obtain…
The regression Tsetlin machine: a novel approach to interpretable nonlinear regression
2019
Relying simply on bitwise operators, the recently introduced Tsetlin machine (TM) has provided competitive pattern classification accuracy in several benchmarks, including text understanding. In this paper, we introduce the regression Tsetlin machine (RTM), a new class of TMs designed for continuous input and output, targeting nonlinear regression problems. In all brevity, we convert continuous input into a binary representation based on thresholding, and transform the propositional formula formed by the TM into an aggregated continuous output. Our empirical comparison of the RTM with state-of-the-art regression techniques reveals either superior or on par performance on five datasets. Thi…
Information content of data and variables and types of weighting in least-squares regression methods
1990
Abstract Algorithms are given for evaluating the relative amount of useful information related to a particular parameter which is carried by individual data points and intervals of the variables. The algorithms provide an efficient means of using the information contained in a set of data. Applications to the optimization of weighting in regression methods are described. Several informational and combined informational-statistical types of weighting are studied as a means of improving the accuracy and precision of the parameters obtained by non-linear regression.
Evaluating the predictive power of sun-induced chlorophyll fluorescence to estimate net photosynthesis of vegetation canopies: A SCOPE modeling study
2016
Abstract Progress in imaging spectroscopy technology and data processing can enable derivation of the complete sun-induced chlorophyll fluorescence (SIF) emission spectrum. This opens up opportunities to fully exploit the use of the SIF spectrum as an indicator of photosynthetic activity. Simulations performed with the coupled fluorescence–photosynthesis model SCOPE were used to determine how strongly canopy-leaving SIF can be related to net photosynthesis of the canopy (NPC) for various canopy configurations. Regression analysis between SIF retrievals and NPC values produced the following general findings: (1) individual SIF bands that were most sensitive to NPC were located around the fir…
Analysis of the sensitivity to the systematic error in least-squares regression models
2004
An algorithm that calculates the sensitivity to the systematic error of the fitted parameters of a least-squares regression model, with respect to the known parameters, is developed. The algorithm can be applied to mechanistic and empirical models, obtained by linear and non-linear regression, including principal component and partial least-squares. It can be useful in identifying those parameters or calibration regions that can influence other parameters and the response mostly, and thus, whose accuracy should be particularly procured. Other applications are the weighing of experimental points and the comparison of different models and regression methods in terms of its ability of amplifyi…
Integer Weighted Regression Tsetlin Machines
2020
The Regression Tsetlin Machine (RTM) addresses the lack of interpretability impeding state-of-the-art nonlinear regression models. It does this by using conjunctive clauses in propositional logic to capture the underlying non-linear frequent patterns in the data. These, in turn, are combined into a continuous output through summation, akin to a linear regression function, however, with non-linear components and binary weights. However, the resolution of the RTM output is proportional to the number of clauses employed. This means that computation cost increases with resolution. To address this problem, we here introduce integer weighted RTM clauses. Our integer weighted clause is a compact r…
Modelling of Adequate Costs of Utilities Services
2016
The paper propose methodology for benchmark modelling of adequate costs of utilities services, which is based on the data analysis of the factual cases (key performance indicators of utilities as the predictors). The proposed methodology was tested by modelling of Latvian water utilities with three tools: (1) a classical version of the multi-layer perceptron with error back-propagation training algorithm was sharpened up with task-specific monotony tests, (2) the fitting of the generalized additive model using the programming language R ensured the opportunity to evaluate the statistical significance and confidence bands of predictors, (3) the sequential iterative nonlinear regression proce…
The Analysis of Auxological Data by Means of Nonlinear Multivariate Growth Curves
1999
In this paper we treat the problem to analyse a data set constituted by multivariate growth curves for different subjects; thus in this context we deal with 3-way data tables. Nevertheless, it is not possible using factorial techniques proposed to deal with 3-way data matrices, because the observations are generally not equally spaced; moreover a multilevel approach founded on polynomial models is not suitable to deal with intrinsic nonlinear models. We propose a non-factorial technique to analyse auxological data sets using an intrinsic nonlinear multivariate growth model with autocorrelated errors. The application to a real data set of growing children gave easily interpretable results.
Multivariate versus univariate calibration for nonlinear chemiluminescence data
2001
Abstract Multivariate calibration is tested as an alternative to model chromium(III) concentration versus chemiluminescence registers obtained from luminol-hydrogen peroxide reaction. The multivariate calibration approaches included have been: conventional linear methods (principal component regression (PCR) and partial least squares (PLS)), nonlinear methods (nonlinear variants and variants of locally weighted regression) and linear methods combined with variable selection performed in the original or in the transformed data (stepwise multiple linear regression procedure). Both the direct and inverse univariate approaches have been also tested. The use of a double logarithmic transformatio…