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1 ometrics (i.e., demonstrated using classical least-squares analysis).
2  analysis, followed by an orthogonal partial least squares analysis.
3  results were correlated by means of partial least squares analysis.
4 and evaluating their accuracy with a partial least-squares analysis.
5 d rate constants were obtained by nonlinear, least-squares analysis.
6                         Generalized weighted least squares analysis accounted for linkage disequilibr
7                           Orthogonal partial least-squares analysis allowed the recovery of a predict
8  Classical Multidimensional Scaling, Partial Least Squares analysis and cluster analysis based on bet
9                     First, combining partial least squares analysis and genetic algorithm, we fitted
10 as been incorporated into a global nonlinear least-squares analysis approach, based upon the Marquard
11 ) and 90 degree angle (alpha)) obtained from least squares analysis are r(C=C) = 1.346(4) A, r(C-C)(r
12  rate constants determined through nonlinear least-squares analysis are also in agreement with protei
13 gle(alpha)) obtained from the combined ED/MW least-squares analysis are r(C-H)(av) = 1.093(6) A, r(C(
14                                      Partial least squares analysis based on 325 measured parameters
15 stic categories were not used in the partial least squares analysis but were helpful for interpreting
16                                   An inverse least-squares analysis coupled to a Michaelis-Menten pro
17  calibration method, two-dimensional partial least-squares analysis, for calibrating single-particle
18 near-infrared (NIR) spectrometry and partial least-squares analysis has been developed for the noninv
19 ing curvature, parameters estimated from the least-squares analysis have varying degrees of uncertain
20                                  In ordinary least squares analysis, higher WGS and lower EA showed a
21 ple comparisons tests and orthogonal partial least-squares analysis identified the level of cyanobact
22 -component lifetime models used in nonlinear least-squares analysis in which each lifetime component
23  a multivariate curve resolution-alternating least-squares analysis method has been used to character
24                        Using two-dimensional least-squares analysis methods, the Raman spectra collec
25              High-quality orthogonal partial least squares analysis models were developed from the ch
26   A multilevel, within-group, sparse partial least squares analysis of covariation of microbial, infl
27 es and estimate B using a nonlinear weighted least squares analysis of nighttime MLS ClO data.
28 ture ionic liquid (RTIL) followed by partial least squares analysis of the data.
29                       Simultaneous nonlinear least squares analysis of the spectra obtained at the tw
30 ent observables through a weighted nonlinear least-squares analysis of a constrained model.
31           Our workflow consists of nonlinear least-squares analysis of steady-state spectroscopic mea
32 eric forms of an analyte followed by partial least-squares analysis of the data.
33                                              Least-squares analysis of the FTIR data of native BNC yi
34                            Global non-linear least-squares analysis of the full kinetic time-courses
35                                              Least-squares analysis of the spectrum indicates an effe
36    The method involves simultaneous (global) least-squares analysis of titrations with Ca(2+), with M
37                                      Partial least squares analysis permitted to correctly classify w
38 ncipal component analysis (PCA), and partial least squares analysis (PLS) revealed that the overall a
39 bination of simulation and global non-linear least-squares analysis provides support for a binding mo
40                                      Partial Least Squares analysis revealed one latent variable that
41                                      Partial least-squares analysis revealed age-related increases in
42 ses and describe novel methods for nonlinear least-squares analysis that overcome these problems.
43      We employed multivariate sparse partial least squares analysis to detect parsimonious associatio
44 adult brain gene expression data and partial least squares analysis to find the weighted gene express
45 etic algorithm and couples them to a partial least squares analysis to predict cellular function, and
46                     The model was refined by least-squares analysis to a nominal resolution of 2.1 A
47                       Simultaneous nonlinear least-squares analysis using "n-step" sequential mechani
48                                    Nonlinear least squares analysis was performed to optimize fitting
49                                Using partial least squares analysis, we identified latent variables r
50                   Using multivariate partial-least-squares analysis, we observed a significant patter
51                              Using a partial least-squares analysis, we uncovered a latent clinical i
52 mately 465-630 nm) were modeled using linear least squares analysis with individual chromophore spect
53                           The sparse partial least squares analysis yielded a phenotype-eye-brain sig
54  (1.2 +/- 0.4) x 104 M-1 s-1 using nonlinear least-squares analysis, yielding an equilibrium binding