Search results for "nonlinear regression"
showing 10 items of 21 documents
On the accuracy of three statistical softwares
2005
In this paper we compare the accuracy of three packages that are commonly used for statistical calculations: Excel of Microsoft, version XP Edition 2003, Statistica of Statsoft, version 6, and R, an open-source free software, available on the web, version 1.9.0. To assess the accuracy of each software in different statistical areas, we are going to use benchmarks expressly developed for this aim. The obtained results show a superiority of R in comparison with the other two softwares.
Kinetic Parameters for Thermal Degradation of Green Asparagus Texture by Unsteady-state Method
1998
An unsteady-state method was developed for estimating texture degradation during heating-cooling of green asparagus spears. The method used a mathematical model of heat transmission for time-temperature history estimation, and a nonlinear regression of texture measurements of asparagus spears to estimate kinetic parameters. The specific heat, conductivity and convective coefficient of green asparagus were determined experimentally and used In the mathematical model for temperature estimation. Values obtained were Ea = 76.19±0.13 kJ/mol and k 1158°C = 0.00528±0.00005 s -1 . Good agreement was found between predicted and observed texture values. The method was compared with the classical stea…
Pressure inactivation kinetics of Enterobacter sakazakii in infant formula milk
2007
Survival curves of Enterobacter sakazakii inactivated by high hydrostatic pressure were obtained at four pressure levels (250, 300, 350, and 400 MPa), at temperatures below 30 degrees C, in buffered peptone water (BPW; 0.3%, wt/vol) and infant formula milk (IFM; 16%, wt/vol). A linear model and four nonlinear models (Weibull, log-logistic, modified Gompertz, and Baranyi) were fitted to the data, and the performances of the models were compared. The linear regression model for the survival curves in BPW and IFM at 250 MPa has fitted regression coefficient (R2) values of 0.940 to 0.700, respectively, and root mean square errors (RMSEs) of 0.770 to 0.370. For the other pressure levels, the lin…
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…
Adsorption of anionic dyes onto natural, thermally and chemically modified smectite clays
2014
Abstract The aim of this study was to determine the adsorption capacity of the smectite clays (from the overburden of the lignite deposit in Belchatow) for two anionic dyes, i.e. Reactive Blue 81 (RB-81) and Direct Blue 74 (DB-74). Additionally, the influence of the thermal and chemical (acid and alkali) clay modifications on the amount of bonded dyes was investigated. The adsorption capacity of the clay (natural and modified) was different for studied dyes and depended on the initial concentration and modification type. All the modified clays adsorbed the dyes at pH>pHPZC as the negatively charged surfaces of their particles (in accordance with the formula: AOH ↔ AO- + H+) prevented the…
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.
A kernel regression approach to cloud and shadow detection in multitemporal images
2013
Earth observation satellites will provide in the next years time series with enough revisit time allowing a better surface monitoring. In this work, we propose a cloud screening and a cloud shadow detection method based on detecting abrupt changes in the temporal domain. It is considered that the time series follows smooth variations and abrupt changes in certain spectral features will be mainly due to the presence of clouds or cloud shadows. The method is based on linear and nonlinear regression analysis; in particular we focus on the regularized least squares and kernel regression methods. Experiments are carried out using Landsat 5 TM time series acquired over Albacete (Spain), and compa…
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…
Análisis de métodos de validación cruzada para la obtención robusta de parámetros biofísicos
2015
[EN] Non-parametric regression methods are powerful statistical methods to retrieve biophysical parameters from remote sensing measurements. However, their performance can be affected by what has been presented during the training phase. To ensure robust retrievals, various cross-validation sub-sampling methods are often used, which allow to evaluate the model with subsets of the field dataset. Here, two types of cross-validation techniques were analyzed in the development of non-parametric regression models: hold-out and k-fold. Selected non-parametric linear regression methods were least squares Linear Regression (LR) and Partial Least Squares Regression (PLSR), and nonlinear methods were…