Statistics least-squares

All of the experimental studies on the formation of Zr sulphate complexes have been re-interpreted in the present review (using a statistical (least squares) regression technique) since in the majority of cases either the model or the selected stability constants assigned by the authors are not supported by the data (see Appendix A) or no uncertainties were assigned in the original work. 

After an alignment of a set of molecules known to bind to the same receptor a comparative molecular field analysis CoMFA) makes it possible to determine and visuahze molecular interaction regions involved in hgand-receptor binding [51]. Further on, statistical methods such as partial least squares regression PLS) are applied to search for a correlation between CoMFA descriptors and biological activity. The CoMFA descriptors have been one of the most widely used set of descriptors. However, their apex has been reached. 

The previously mentioned data set with a total of 115 compounds has already been studied by other statistical methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis, and the Partial Least Squares (PLS) method [39]. Thus, the choice and selection of descriptors has already been accomplished. 

For many applications, quantitative band shape analysis is difficult to apply. Bands may be numerous or may overlap, the optical transmission properties of the film or host matrix may distort features, and features may be indistinct. If one can prepare samples of known properties and collect the FTIR spectra, then it is possible to produce a calibration matrix that can be used to assist in predicting these properties in unknown samples. Statistical, chemometric techniques, such as PLS (partial least-squares) and PCR (principle components of regression), may be applied to this matrix. Chemometric methods permit much larger segments of the spectra to be comprehended in developing an analysis model than is usually the case for simple band shape analyses. 

Statistical methods used in kinetic analyses have generally been based on a least-squares treatment. Reed and Theriault [494] have considered the application of this approach to data which obeys the first-order... 

This expression constitutes an improvement. There are two advantages. First, the statistical reliability of the data analysis improves, because the variance in [A] is about constant during the experiment, whereas that of the quantity on the left side of Eq. (3-27) is not. Proper least-squares analysis requires nearly constant variance of the dependent variable. Second, one cannot as readily appreciate what the quantity on the left of Eq. (3-27) represents, as one can do with [A]t. Any discrepancy can more easily be spotted and interpreted in a display of (A] itself. 

We have seen that minimizing the likelihood penalty ml(x) enforces agreement with the data. Exact expression of ml(x) should depends on the known statistics of the noise. However, if the statistics of the noise is not known, using a least-squares penalty is more robust (Lane, 1996). In the following, and for sake of simplicity, we will assume Gaussian stationary noise ... 

Huyberechts, S., A. Halleux, and P, Kruys,Bu//. Soc. Chim. Beiges, 64, 203 (1955). Linnik, Yu. V., Me tod Naimenshikh Kvadratov i Osnovy Matematichesko-Statisticheskoi Teorii Obrabotki Nablyudenii (Method of Least Squares and Principles of Mathematico-Statistical Theory of Data Processing), Gos. Izdatelstvo Fiz. Mat. Literatury, Moscow, 1958. 

Since that time thousands of QSARs, covering a wide and diverse range of end points, have been published [9] most of these have used MLR, but numerous other statistical techniques have also been used, such as partial least squares, principal component analysis, artificial neural networks, decision trees, and discriminant analysis [f4]. 

Several techniques from statistics, such as partial least-squares regression, and from artificial intelligence, such as artificial neural networks have been used to learn empirical input/ output relationships. Two of the most significant disadvantages of these approaches are the following ... 

Norinder, U., Osterberg, T. Theoretical calculation and prediction of drug transport processes using simple parameters and partial least squares projections to latent structures (PLS) statistics. The use of electrotopological state indices./. Pharm. Sci. 2001, 90, 1075-1085. 

Y. Escoufier and S. Junca, Least squares approximation of frequencies or their logarithms. Int. Statistical Rev., 54 (1986) 279-283. 

H. Wold, Soft modelling by latent variables the non-linear iterative partial least squares (NIPALS) algorithm. In Perspectives in Probability and Statistics, J. Gani (Ed.). Academic Press, London, 1975, pp. 117-142. 

The quantities AUMC and AUSC can be regarded as the first and second statistical moments of the plasma concentration curve. These two moments have an equivalent in descriptive statistics, where they define the mean and variance, respectively, in the case of a stochastic distribution of frequencies (Section 3.2). From the above considerations it appears that the statistical moment method strongly depends on numerical integration of the plasma concentration curve Cp(r) and its product with t and (r-MRT). Multiplication by t and (r-MRT) tends to amplify the errors in the plasma concentration Cp(r) at larger values of t. As a consequence, the estimation of the statistical moments critically depends on the precision of the measurement process that is used in the determination of the plasma concentration values. This contrasts with compartmental analysis, where the parameters of the model are estimated by means of least squares regression. 

The application of optimisation techniques for parameter estimation requires a useful statistical criterion (e.g., least-squares). A very important criterion in non-linear parameter estimation is the likelihood or probability density function. This can be combined with an error model which allows the errors to be a function of the measured value. A simple but flexible and useful error model is used in SIMUSOLV (Steiner et al., 1986 Burt, 1989). 

The converged parameter values represent the Least Squares (LS), Weighted LS or Generalized LS estimates depending on the choice of the weighting matrices Q,. Furthermore, if certain assumptions regarding the statistical distribution of the residuals hold, these parameter values could also be the Maximum Likelihood (ML) estimates. 

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