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1 ulatory networks using a two-stage penalized least squares method.
2 ng and within families, are estimated by the least-squares method.
3  three structural elements are computed by a least-squares method.
4 ent product ion spectra coupled with a total least-squares method.
5 els whose parameters are estimated using the least-squares method.
6 o the preprocessing and modified alternating least-squares methods.
7 odelling background, outperforming classical least-squares methods.
8         The algorithm is based on the Kabsch least-squares method and determines an optimal transform
9 ix formulation proves the equivalence of the least-squares method and the integral curve-fitting.
10 ior sound field using the Helmholtz equation least squares method, and finally to observe the changes
11 on models were constructed using the partial least-squares method, and the variable importance in pro
12  strong connections with popular regularized least-squares methods, and the use of such numerical rec
13  by neighbor joining, minimum evolution, and least-squares method are available, but the methods for
14                                      Inverse least-squares methods are used to develop models that co
15 the existing regression method, the weighted least squares method can be useful in QTL mapping in con
16  associated variables, are developed using a least-squares method combined with a numerical different
17 atrix-based approach employing a constrained least-squares method followed by nonlinear optimization.
18 oblems, a fast and computationally efficient least-squares method for estimating population parameter
19 neation on images reconstructed by using the least-squares method for motion detection with navigator
20 w QSRR model based on a Kernel-based partial least-squares method for predicting UPLC retention times
21 accurate than those obtained by the ordinary least squares method in predicting future MD (MSE: 5.13
22                                   First, the least squares method is employed to obtain the initial v
23                           Thus, the weighted least squares method is shown to be a safe linear regres
24             With two regression methods (the least-squares method [LSM] and the Deming II [DM] method
25  analysis of the unfolded spectra by partial least squares methods (PLS1 and PLS2) revealed quantitat
26 genetically desirable property that weighted least-squares methods should satisfy, and provide a comp
27 ent RLC circuit model is developed using the least squares method, showing strong agreement with full
28                               The use of the least squares method to calculate the best-fitting line
29                    Kindred used non-negative least squares method to fit the model, which not only in
30 e regression was performed using the partial least squares method to quantify the starch in the sprea
31 CellPie employs the accelerated hierarchical least squares method to significantly reduce the computa
32 tly admixed population and use a generalized least-squares method to fit the model to two different d
33 nm were spectrally unmixed by using a linear least-squares method to obtain sPA images.
34 fitted to a two-state model by a non-linear, least-squares method to obtain the transition temperatur
35 using a two-pole Debye model, and a weighted least squares method was employed to optimise the model
36        In a prospective analysis, the linear least-squares method was used to evaluate the time cours
37 action fields together with a simple partial least-squares method were found to be adequate to descri
38 uctural parameters are optimized by weighted least-squares methods when the model scattering intensit
39 ost commonly used algorithm is the classical least squares method, which is popular due to its speed
40        We compare the novel algorithm to the least squares method with a low-order polynomial residua
41      The data were analyzed using a standard least squares method with an effect screening emphasis,
42 hold Budget Survey and applies a three-stage least squares method with instrumental variables to esti
43 y principal component regression and partial least squares methods with analysis of uncertainties.
44 5 A resolution by full matrix block diagonal least-squares methods with anisotropic temperature facto
45                      The non-negative linear least-squares method yields the best results for deconvo