Search results for "Linear regression"
showing 10 items of 375 documents
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…
Modelling of Reflectance Spectra of Skin Phototypes III
2011
In dermatology, study of human skin colour is related to skin phototype (SPT) in which the Fitzpatrick's scale is the most used skin photo type classification. Assessment of skin response to UV for various reasons plays an important role in dermatology. This is however not easy to be performed because of two reasons. Firstly, skin areas may have different skin tone resulting in different reflectance spectra and secondly, different modalities may produce different reflectance spectra. We hypothesize that the underlying pattern of reflectance spectra must be similar regardless of the modalities use and the skin areas where it is obtained, for a particular person. An observational clinical stu…
Generalized Multitarget Linear Regression with Output Dependence Estimation
2019
Multitarget regression has recently received attention in the context of modern, large-scale problems in which finding good enough solutions in a timely manner is crucial. Different proposed alternatives use a combination of regularizers that lead to different ways of solving the problem. In this work, we introduce a general formulation with several regularizers. This leads to a biconvex minimization problem and we use an alternating procedure with accelerated proximal gradient steps to solve it. We show that our formulation is equivalent but more efficient than some previously proposed approaches. Moreover, we introduce two new variants. The experimental validation carried out, suggests th…
Constrained Clusterwise Linear Regression
2005
In market segmentation, Conjoint Analysis is often used to estimate the importance of a product attributes at the level of each single customer, clustering, successively, the customers whose behavior can be considered similar. The preference model parameter estimation is made considering data (usually opinions) of a single customer at a time, but these data are usually very few as each customer is called to express his opinion about a small number of different products (in order to simplify his/her work). In the present paper a Constrained Clusterwise Linear Regression algorithm is presented, that allows simultaneously to estimate parameters and to cluster customers, using, for the estimati…
Bayesian model averaging and weighted-average least squares: Equivariance, stability, and numerical issues
2011
In this article, we describe the estimation of linear regression models with uncertainty about the choice of the explanatory variables. We introduce the Stata commands bma and wals, which implement, respectively, the exact Bayesian model-averaging estimator and the weighted-average least-squares estimator developed by Magnus, Powell, and Prüfer (2010, Journal of Econometrics 154: 139–153). Unlike standard pretest estimators that are based on some preliminary diagnostic test, these model-averaging estimators provide a coherent way of making inference on the regression parameters of interest by taking into account the uncertainty due to both the estimation and the model selection steps. Spec…
TOPS-MODE approach for the prediction of blood-brain barrier permeation.
2004
The blood-brain barrier permeation has been investigated by using a topological substructural molecular design approach (TOPS-MODE). A linear regression model was developed to predict the in vivo blood-brain partitioning coefficient on a data set of 119 compounds, treated as the logarithm of the blood-brain concentration ratio. The final model explained the 70% of the variance and it was validated through the use of an external validation set (33 compounds of the 119, MAE = 0.33), a leave-one-out crossvalidation (q(2) = 0.65, S(press) = 0.43), fivefold full crossvalidation (removing 28 compounds in each cycle, MAE = 33, RMSE = 0.43) and the prediction of +/- values for an external test set …
Multiobjective optimization of building energy consumption and thermal comfort based on integrated BIM framework with machine learning-NSGA II
2022
Detailed parametric analysis and measurements are required to reduce building energy usage while maintaining acceptable thermal conditions. This research suggested a system that combines Building Information Modeling (BIM), machine learning, and the non-dominated sorting genetic algorithm-II (NSGA II) to investigate the impact of building factors on energy usage and find the optimal design. A plugin is developed to receive sensor data and export all necessary information from BIM to MSSQL and Excel. The BIM model was imported to IDA Indoor Climate and Energy (IDA ICE) to execute an energy consumption simulation and then a pairwise test to produce the sample data set. To study the data set a…
Identification of linear parameter varying models
2002
We consider identification of a certain class of discrete-time nonlinear systems known as linear parameter varying system. We assume that inputs, outputs and the scheduling parameters are directly measured, and a form of the functional dependence of the system coefficients on the parameters is known. We show how this identification problem can be reduced to a linear regression, and provide compact formulae for the corresponding least mean square and recursive least-squares algorithms. We derive conditions on persistency of excitation in terms of the inputs and scheduling parameter trajectories when the functional dependence is of polynomial type. These conditions have a natural polynomial i…
Sensitivity and uncertainty analysis of an integrated ASM2d MBR model for wastewater treatment
2018
Abstract An integrated membrane bioreactor (MBR) model was previously proposed and tested. The model provides a comprehensive and detailed description of the nitrogen biological removal processes with respect to up-to-date literature. This paper presents a sensitivity and uncertainty analysis aimed at identifying the key factors affecting the variability of the model predictions. The Standardized Regression Coefficients (SRC) method was adopted for the sensitivity analysis. The uncertainty analysis was employed by running Monte Carlo simulations by varying only the value of the key factors affecting the model outputs. The sensitivity analysis combined with the uncertainty analysis applied h…
Ranking drivers of global carbon and energy fluxes over land
2015
The accurate estimation of carbon and heat fluxes at global scale is paramount for future policy decisions in the context of global climate change. This paper analyzes the relative relevance of potential remote sensing and meteorological drivers of global carbon and energy fluxes over land. The study is done in an indirect way via upscaling both Gross Primary Production (GPP) and latent energy (LE) using Gaussian Process regression (GPR). In summary, GPR is successfully compared to multivariate linear regression (RMSE gain of +4.17% in GPP and +7.63% in LE) and kernel ridge regression (+2.91% in GPP and +3.07% in LE). The best GP models are then studied in terms of explanatory power based o…