Applied Mathematics, Physics
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021 Mathematical Papers Related to Astronomy and Astrophysics, Computing Application of multivariate analysis to the parameters of astrophysical objects. M. Fracassini, G. Manzotti, L. E. Pasinetti, G. Raffaelli, E. Antonello, L. Pastori. ESA Spec. Pub!., ESA SP-201, p. 21-25 (1983). - See Abstr. 012.003. Multivariate analysis has been applied to the following astrophysical objects: Ultrashort Period Cepheids; Stars in the photometric system of the Geneva Observatory; Pulsars; Globular clusters. Some results are reported.
021.001
Some applications of multiple regression analysis. E. Antonello. ESA Spec. Pub!., ESA SP-201, p. 145-148 (1983).- See Abstr. 012.003. A method for the search of possible nonlinear relations between a dependent and a combination of independent variables is presented. Some applications of the method to stellar groups are discussed.
021.005
The discriminant analysis in astronomy. E. Antonello, M. Fracassini, G. Manzotti, L. E. Pasinetti, L. Pastori, G. Raffaelli. ESA Spec. Pub!., ESA SP-201, p. 157-159 (1983).- See Abstr. 012.003. The discriminant analysis is a method of the multivariate statistical analysis which is useful to search for observational and physical parameters discriminating among distinct groups of stars. Results of the application of the method to variable (o Scuti) and non variable stars and to the Oosterhoff groups of globular clusters are shown.
021.006 Some comments on time series. Y. G. Biraud. ESA Spec. Pub!., ESA SP-201, p. 35 (1983).- SeeAbstr. 012.003. Some brief comments are presented concerning the analysis of stationary time series using Auto Regressive, Moving Average, ARMA, ARIMA.. modeling. The difficulties encountered in the application of such methods are emphasized although their results are encouraging.
021.002
021.003 J. Pelt.
Phase dispersion minimization methods for estimation of periods from unequally spaced sequences of data.
ESA Spec. Pub!., ESA SP-201, p. 37-42 (1983). - See Abstr. 012.003. There are three well-known families of period estimation methods for uneven sequences of observations: Fourier transform methods, least-squares estimation methods and phase dispersion minimization methods. For simple harmonic waveforms all three methods give essentially equivalent results. However, for more complicated waveforms (and this is the rule for astronomical data) differences arise. The author tries to demonstrate that the phase dispersion methods generally outperform the others. It is possible to construct more sensitive statistics than with Fourier transform methods. For least-squares methods, the point of difference is the execution time of related computer programs. Moreover, the phase dispersion methods can be used to simulate the other two. 021.004 Classification methods: an introductory survey. M. J. Kurtz. ESA Spec. Pub!., ESA SP-201, p. 47-58 (1983). - See Abstr. 012.003. Classification techniques lie at the heart of a number of disciplines. Pattern recognition in particular has become a large subfield of engineering, some examples of it
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