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PLS

SAVE CONDITIONS AT WHICH PHIS CALCULATED TL = T PL-P RETURN... [Pg.301]

Some methods that paitly cope with the above mentioned problem have been proposed in the literature. The subject has been treated in areas like Cheraometrics, Econometrics etc, giving rise for example to the methods Partial Least Squares, PLS, Ridge Regression, RR, and Principal Component Regression, PCR [2]. In this work we have chosen to illustrate the multivariable approach using PCR as our regression tool, mainly because it has a relatively easy interpretation. The basic idea of PCR is described below. [Pg.888]

The two exponential tenns are complex conjugates of one another, so that all structure amplitudes must be real and their phases can therefore be only zero or n. (Nearly 40% of all known structures belong to monoclinic space group Pl c. The systematic absences of (OlcO) reflections when A is odd and of (liOl) reflections when / is odd identify this space group and show tiiat it is centrosyimnetric.) Even in the absence of a definitive set of systematic absences it is still possible to infer the (probable) presence of a centre of synnnetry. A J C Wilson [21] first observed that the probability distribution of the magnitudes of the structure amplitudes would be different if the amplitudes were constrained to be real from that if they could be complex. Wilson and co-workers established a procedure by which the frequencies of suitably scaled values of F could be compared with the tlieoretical distributions for centrosymmetric and noncentrosymmetric structures. (Note that Wilson named the statistical distributions centric and acentric. These were not intended to be synonyms for centrosyimnetric and noncentrosynnnetric, but they have come to be used that way.)... [Pg.1375]

Vo + V2 and = Vo — 2 (actually, effective operators acting onto functions of p and < )), conesponding to the zeroth-order vibronic functions of the form cos(0 —4>) and sin(0 —(()), respectively. PL-H computed the vibronic spectrum of NH2 by carrying out some additional transformations (they found it to be convenient to take the unperturbed situation to be one in which the bending potential coincided with that of the upper electi onic state, which was supposed to be linear) and simplifications (the potential curve for the lower adiabatic electi onic state was assumed to be of quartic order in p, the vibronic wave functions for the upper electronic state were assumed to be represented by sums and differences of pairs of the basis functions with the same quantum number u and / = A) to keep the problem tiactable by means of simple perturbation... [Pg.509]

The first theoretical handling of the weak R-T combined with the spin-orbit coupling was carried out by Pople [71]. It represents a generalization of the perturbative approaches by Renner and PL-H. The basis functions are assumed as products of (42) with the eigenfunctions of the spin operator conesponding to values E = 1/2. The spin-orbit contribution to the model Hamiltonian was taken in the phenomenological form (16). It was assumed that both interactions are small compared to the bending vibrational frequency and that both the... [Pg.509]

Equilibria in Solution The stability of a protein-ligand complex in solution is measured in terms of the equilibrium constant and the standard free energy of association based on it. For association of species P and L in solution to form a complex PL, i.e., for... [Pg.130]

Another problem is to determine the optimal number of descriptors for the objects (patterns), such as for the structure of the molecule. A widespread observation is that one has to keep the number of descriptors as low as 20 % of the number of the objects in the dataset. However, this is correct only in case of ordinary Multilinear Regression Analysis. Some more advanced methods, such as Projection of Latent Structures (or. Partial Least Squares, PLS), use so-called latent variables to achieve both modeling and predictions. [Pg.205]


See other pages where PLS is mentioned: [Pg.296]    [Pg.300]    [Pg.300]    [Pg.300]    [Pg.302]    [Pg.433]    [Pg.440]    [Pg.440]    [Pg.478]    [Pg.317]    [Pg.317]    [Pg.663]    [Pg.442]    [Pg.493]    [Pg.1114]    [Pg.1272]    [Pg.1273]    [Pg.1853]    [Pg.1934]    [Pg.2019]    [Pg.2643]    [Pg.48]    [Pg.508]    [Pg.509]    [Pg.509]    [Pg.526]    [Pg.130]    [Pg.130]    [Pg.135]    [Pg.136]    [Pg.199]    [Pg.287]    [Pg.287]    [Pg.290]    [Pg.291]    [Pg.291]    [Pg.292]    [Pg.292]    [Pg.294]    [Pg.294]    [Pg.428]    [Pg.214]   
See also in sourсe #XX -- [ Pg.702 ]

See also in sourсe #XX -- [ Pg.248 , Pg.367 ]

See also in sourсe #XX -- [ Pg.702 ]

See also in sourсe #XX -- [ Pg.307 ]

See also in sourсe #XX -- [ Pg.2 , Pg.465 ]

See also in sourсe #XX -- [ Pg.248 , Pg.367 ]

See also in sourсe #XX -- [ Pg.36 , Pg.41 , Pg.83 ]




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B PLS algorithms

Basic Principles of PLS

Characterization by PL

Dynamic Extensions of PLS

For PLS model

GF-PL method

GOLPE (Generating Optimal Linear PLS

Generating optimal linear PLS

Generating optimal linear PLS estimation

Generating optimal linear PLS estimation GOLPE)

Geometrical Description of PLS

Geometrical description of the PLS method

Highly Dispersed Transition Metal Ions in Oxides or Zeotype-Systems by PL Spectroscopy

Interval PLS

Investigation of Cross-Linked Polymer Systems by PL

Kernel PLS

Low temperature PL

Mathematical Description of PLS

Multilinear PLS

Multiway PLS

NIPALS-PLS Algorithm

NIR PL Spectra

Na-Pl zeolite

Non-linear PLS

Nonlinear PLS

Orthogonal PLS

PL Quenching by Doping

PL aquagel

PL intensity

PL law

PL lifetimes

PL quantum yield

PL quenching

PL spectra

PL-SAX

PL/1 program

PLS Partial Least Squares Projections to Latent Structures

PLS Prediction Cross Validation

PLS Projections to Latent Structures

PLS Regression Models

PLS Toolbox

PLS algorithm

PLS analysis

PLS calibration

PLS components

PLS discriminant

PLS in Action

PLS loadings

PLS method

PLS model for assessing common method bias

PLS modeling

PLS predictions

PLS regression

PLS regression and CoMFA

PLS results

PLS score plots

PLS scores

PLS scores images

PLS scores values

PLS statistics

PLS technique

PLS vectors

PLS weights

PLS, Partial least squares

PLS, partial least squares regression

PLS-discriminant analysis

Partial Least Squares (PLS) Analysis and Other Multivariate Statistical Methods

Partial Least Squares Projection of Latent Structures (PLS)

Partial Least Squares Projections to Latent Structures (PLS) in Chemistry

Partial least squares discriminant analysis PLS-DA)

Partial least squares discriminate analysis PLS-DA)

Photoluminescence (PL)

Pl approximation

Pl controllers

Pl<a value

Plots from the PLS model

Polarographic logging (PL)

Polyluminol (PL)

Polynomial PLS

Predictions by PLS

Projection to latent structures (PLS) regression

Q(Pl)-gases

Quadratic PLS

SC-PL method

SPM Methods Based on PLS

Sensor FDD Using PLS and CVSS Models

Significant PLS components

Solvent casting and particulate leaching SC-PL)

The PLS Algorithm

The PLS Approach

Two-Electron Satellites in PL

Two-block PLS

Two-block PLS and indirect QSAR

UVE-PLS

Validity of a PLS model

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