Search results for "Nonlinear regression"

showing 10 items of 21 documents

Plasma clearance of human low-density lipoprotein in human apolipoprotein B transgenic mice is related to particle diameter.

2004

To test for intrinsic differences in metabolic properties of low-density lipoprotein (LDL) as a function of particle size, we examined the kinetic behavior of 6 human LDL fractions ranging in size from 251 to 265 A injected intravenously into human apolipoprotein (apo) B transgenic mice. A multicompartmental model was formulated and fitted to the data by standard nonlinear regression using the Simulation, Analysis and Modeling (SAAM II) program. Smaller sized LDL particles (251 to 257 A) demonstrated a significantly slower fractional catabolic rate (FCR) (0.050 +/- 0.045 h(-1)) compared with particles of larger size (262 to 265 A) (0.134 +/- -0.015 h(-1), P.03), and there was a significant …

Genetically modified mouseAdultMalemedicine.medical_specialtySimvastatinApolipoprotein BMetabolic Clearance RateEndocrinology Diabetes and MetabolismPlasma clearance low-density lipoprotein apolipoprotein B trangenic miceMice Transgenicchemistry.chemical_compoundMiceEndocrinologyInternal medicineBlood plasmamedicineAnimalsHumansParticle SizeApolipoproteins BPravastatinbiologyCatabolismMiddle AgedLipoproteins LDLEndocrinologychemistryLow-density lipoproteinModels Animalbiology.proteinRegression Analysislipids (amino acids peptides and proteins)Particle sizeNonlinear regressionLipoproteinMetabolism: clinical and experimental
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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…

Gompertz functionHydrostatic pressureAnalytical chemistryColony Count MicrobialFood ContaminationMicrobiologyModels BiologicalMicrobiologyRoot mean squareCronobacter sakazakiiLinear regressionHydrostatic PressureAnimalsHumansModels StatisticalbiologyChemistryLinear modelInfant NewbornInfantEnterobacterbiology.organism_classificationInfant FormulaKineticsMilkInfant formulaConsumer Product SafetyFood MicrobiologyInfant FoodNonlinear regressionFood Science
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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…

Materials sciencebiologySpecific heatMineralogyThermodynamicsConductivitybiology.organism_classificationKinetic energyThermalDegradation (geology)AsparagusTexture (crystalline)Nonlinear regressionFood ScienceJournal of Food Science
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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…

Regularized least squaresSeries (mathematics)business.industryComputer scienceShadowKernel regressionCloud computingbusinessFocus (optics)Nonlinear regressionRemote sensingDomain (software engineering)MultiTemp 2013: 7th International Workshop on the Analysis of Multi-temporal Remote Sensing Images
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Thermal inactivation kinetics of Bacillus stearothermophilus spores using a linear temperature program.

1999

A systematic study of the inactivation kinetics of Bacillus stearothermophilus spores was carried out in nonisothermic heating conditions using a linear temperature increase program and analyzing the experimental data by means of a one-step nonlinear regression. The D and z values estimated are close to those obtained in isothermic conditions and estimated by using a two-step model, first D values are calculated, and then in the second step a z value is deduced (D(121 degrees C) = 3.08 and 4.38 min, respectively, and z = 7 and 7.9 degrees C, respectively). No convergence problems were observed when using the one-step nonlinear regression proposed. The results indicated that the methodology …

Spores BacterialHot TemperaturebiologyChemistryfungiKineticsColony Count MicrobialTemperatureBacillusThermodynamicsbiology.organism_classificationKinetic energyMicrobiologyEndosporeMicrobiologySporeDisinfectionGeobacillus stearothermophilusThermalZ-valueNonlinear regressionFood ScienceJournal of food protection
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On the ambiguous consequences of omitting variables

2015

This paper studies what happens when we move from a short regression to a long regression (or vice versa), when the long regression is shorter than the data-generation process. In the special case where the long regression equals the data-generation process, the least-squares estimators have smaller bias (in fact zero bias) but larger variances in the long regression than in the short regression. But if the long regression is also misspecified, the bias may not be smaller. We provide bias and mean squared error comparisons and study the dependence of the differences on the misspecification parameter.

Statistics::TheoryMean squared errorjel:C52Regression dilutionjel:C51Local regressionjel:C13Regression analysisOmitted-variable biasCross-sectional regressionStatistics::ComputationOmitted variables Misspecification Least-squares estimators Bias Mean squared errorStatistics::Machine LearningStatisticsEconometricsStatistics::MethodologyRegression diagnosticNonlinear regressionMathematics
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Oxygen Consuming Regions in EMT60/Ro Multicellular Tumour Spheroids Determined by Nonlinear Regression Analysis of Experimental PO2 Profiles

1987

Malignant cells can be studied in vitro, in a tumour-like microenvironment, by growing multicellular tumour spheroids in culture (Sutherland, McCredie and Inch, 1971). Franko and Sutherland (1979) utilized diffusion theory to explain the viable rim thicknesses of spheroids measured histologically. Without PO2 profiles, however, an unequivocal interpretation of their results was not possible. Systematic studies of the PO2 profiles in spheroids have since been made with oxygen microelectrodes by several groups (Carlsson et al., 1979; Kaufman et al., 1981; Mueller-Klieser and Sutherland, 1982a,b). Based on these measurements, new analyses utilizing diffusion theory are being developed to chara…

Steady stateMaterials scienceDiffusion equationStereochemistryMathematical analysisSpheroidOxygen transportRadiusDiffusion (business)Fick's laws of diffusionNonlinear regression
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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…

TeledeteccióGeography Planning and Developmentlcsh:G1-922Least squaresCross-validationValidación cruzadaProcesos gausianosHold-outAnàlisi de regressióLinear regressionStatisticsPartial least squares regressionEarth and Planetary Sciences (miscellaneous)MLRAbusiness.industryCross-validationRegression analysisPattern recognitionRegresión de Kernel RidgeAprendizaje automáticoRegressionK-foldHold-OutGeographyk-foldPrincipal component regressionArtificial intelligencebusinessKernel Ridge regressionNonlinear regressionGaussian process regressionlcsh:Geography (General)Revista de Teledetección
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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.

benchmark univariate statistics linear and nonlinear regression probability distributions
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Species–area relationships in continuous vegetation: Evidence from Palaearctic grasslands

2019

Aim Species-area relationships (SARs) are fundamental scaling laws in ecology although their shape is still disputed. At larger areas, power laws best represent SARs. Yet, it remains unclear whether SARs follow other shapes at finer spatial grains in continuous vegetation. We asked which function describes SARs best at small grains and explored how sampling methodology or the environment influence SAR shape. Location Palaearctic grasslands and other non-forested habitats. Taxa Vascular plants, bryophytes and lichens. Methods We used the GrassPlot database, containing standardized vegetation-plot data from vascular plants, bryophytes and lichens spanning a wide range of grassland types throu…

curvesshapesspecies– area relationship (SAR)Michaelis–Menten functionBiomeGrasslandVegetation typelogarithmic functionTaxonomic rankLichenNested‐plot Samplinggeography.geographical_feature_categorypower lawEcologyVDP::Landbruks- og Fiskerifag: 900biologyEcologySettore BIO/02 - Botanica SistematicaPalaearctic grasslandspecies-area relationship (SAR)Grasslandddc:nonlinear regressionscale dependenceMichaelis–Menten Functionlogarithmic function; Michaelis–Menten function; minimal area; nested-plot sampling; nonlinear regression; Palaearctic grassland; plant biodiversity; power law; scaling law; species–area relationship (SAR)environmentnested‐plot sampling570Evolutionscaling lawSpecies-area relationshipminimal areadiversityspecies–area relationship (SAR)Behavior and Systematicsspecies- area relationship (SAR)ddc:570577: Ökologienested-plot samplingEcology Evolution Behavior and Systematics580geographymodelfungiBiology and Life Sciences500Species diversityPlant communitySpecies–area Relationship (SAR)Earth and Environmental SciencesMichaelis-Menten functionplant biodiversitySpecies richnessrichness
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