least-squares data fitting
Scattered data fitting using least squares with interpolation method
... Scattered data fitting is a big issue in numerical ...the data are contaminated by noise and some are ...noisy data, and the traditional least squares method may lose accuracy at ...
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Computer program of nonlinear, curved regression for ‘probacent’ probability equation in biomedicine
... the least sum of squares is designed to de- termine the constant values of γ in ...better fitting and more accurate than those obtained by the least maximum-difference principle as suggested ...
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APPLICATION OF RESTRICTED LEAST SQUARES TO ECONOMETRIC DATA
... after fitting the more general ...restricted least squares estimator to covariance misspecification where the restrictions may or may not be ...
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Performance of Differential Evolution Method in Least Squares Fitting of Some Typical Nonlinear Curves
... the Least Squares ...related data and the estimated values, y ˆ , of the predicted variable (y or the dependent variable) has been ...associated data it is a rather simple example of nonlinear ...
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Least Squares Fitting of Chacón Gielis Curves by the Particle Swarm Method of Optimization
... It is well-known that the Jacobian elliptic functions may profitably be replaced by Weierstrass’s elliptic functions. The latter are simpler and elegant for developing a general theory of non-linear processes. However, ...
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An application of the least squares plane fitting interpolation process to image reconstruction and enhancement
... The registration process determines the x- and y-shifts between any two images. By reiterating the procedure for an additional reference image, a second estimation for the relative positions is made. The replication of ...
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Total Least Squares Fitting the Three-Parameter Inverse Weibull Density
... weighted least squares fitting of the three- parameter inverse Weibull ...weighted least squares estimate for a three-parameter Weibull ...and Data Analysis, 52:4502-4511, ...
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Smoothing Yield Curve Data by Least Squares and Concavity or Convexity
... It is worthwhile to note that the piecewise linear fit that is obtained by our data fitting method, economizes on storage requirements of the yield over time dataset. Indeed, the method defines the time ...
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Adaptive Least Square Approximation For Fitting Scattered Data
... N data points, and we want to fit them with a RBF approximant within a given ...the data with a RBF approximant based on few initial knots, and then find the “worst” data location with the largest ...
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SELECTION OF REFERENCE PLANE BY THE LEAST SQUARES FITTING METHODS
... paper least square fitting methods (cylinder, polynomial) and commercial filters (Gaussian filter, Gaussian regression filter and ro- bust Gaussian regression filter) for areal form removal were compared ...
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CURVE FITTING: STEP-WISE LEAST SQUARES METHOD
... of least squares is indispensible and is widely used method for curve fitting to numerical ...of least squares was first discovered by the French mathematician Legendre in 1805 ...
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A graph optimization approach for motion estimation using inertial measurement unit data
... IMU data, the proposed motion estimation method corrects the accumulated errors via least-squares fitting with constraints that are obtained from IMU measurements and from prior knowledge of ...
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Consistent least squares fitting of ellipsoids
... the Belgian office for Scientific, Technical and Cultural Affairs, promoting Scientific and Technical Collaboration with Central and Eastern Europe. This paper presents research results of the Belgian Programme on ...
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Data-driven Calibration of Penalties for Least-Squares Regression
... instance be estimated with the global or local Rademacher complexities mentioned in Section 1. However, a natural question is whether the slope heuristics of Section 2.3, upon which Algo- rithm 1 relies, can be extended ...
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The Karush-Kuhn-Tucker Test of Convexity of Univariate Observations and Certain Economic Applications
... The paper is organized as follows. In Section 2 we specify the type of a test statistic for convexity. Specifically, we test linearity against convexity, which requires the calculation of the Lagrange and the ...
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A Computational Method for the Karush-Kuhn- Tucker Test of Convexity of Univariate Observations and Certain Economic Applications
... Fifty data points were retrieved from the World Income Inequality Database of the ...Gini data yields an estimate of the rate of income inequality change over a long time period ...
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Optical properties of the TeO2-PbO-Li2O-Nd2O3 glass system
... by fitting using a least-squares method to the electric dipole contributions of the experimental oscillator strength of the observed transitions to the calculated ones ...
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Least squares estimation of hydraulic conductivity from field data
... The modeling of groundwater ow is a cru- cial step in any quantitative analysis of subsur- face contamination. From site characterization to remediation, ow models provide important decision-making tools. The main ...
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On the Equivalence of the Weighted Least Squares and the Generalised Least Squares Estimators, with Applications to Kernel Smoothing
... The Henderson filter (see Henderson, 1916, Kenny and Durbin, 1982, Loader, 1999, Ladiray and Quenneville, 2001) arises as the weighted least squares estimator of a local cubic trend at time t using 2h + 1 ...
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Least-Squares Policy Iteration
... LSPI was implemented 9 using a combination of Matlab and C and was tested on the following problems: chain walk, inverted pendulum balancing, and bicycle balancing and riding. The chain walk class of problems has no ...
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