Sharing and cooperation in geo-information technology

Aziz, T. Lukman

Bibliographic data

Monograph

Persistent identifier:: 856479470

Author:: Aziz, T. Lukman

Title:: Sharing and cooperation in geo-information technology

Sub title:: ISPRS Commission VI Symposium, April 15 - 17, 1999, Bandung, Indonesia

Scope:: 1 Online-Ressource (130 Seiten)

Year of publication:: 1999

Place of publication:: London

Publisher of the original:: RICS Books

Identifier (digital):: 856479470

Illustration:: Illustrationen, Diagramme

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: WG VI/3: INTERNATIONAL COOPERATION AND TECHNOLOGY TRANSFER

Write comment:: Der Aufsatz "Promotion Of The General Understanding And Improvement Of Public Relations, [...] ist im Original nicht enthalten.

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: DISCRETE MATHEMATIC FOR SPATIAL DATA CLASSIFICATION AND UNDERSTANDING. Luigi Mussio, Rossella Nocera, Daniela poli

Document type:: Monograph

Structure type:: Chapter

78 around the best half part of the observations in each step of linear adjustment. The number could be increased until 2/3 of the observations, preserving the reliability of the observations, globally and locally, according to geodesists community suggestions, if the amount of suspected outliers isn’t too large. The breakdown point desreases, obviously, but not too much, so that the procedure continues to be effective. These methods are grouped together and generalized by means of the definition of the S-estimators. The strategy of application of the presented procedure is an adjustment of the best observation, after a prelimunary least squares adjustment. Successively the suspected outliers which don’t show blunders, leverages or small outliers are forward accepted by using the Hawkins test. The degrees of freedom are equal to the number of observations actually processed at the present step of adjustment (remember that this number is always less than the number of observations) minus the number of unknowns parameters. The expected value is computed taking into account the residuals, the recursive, tire variances of the residuals and the squares sigma zero. Furthermore the critical values, for a parametric test on two sides, are derived from the extremal Hawkins probability distribution. A. Distribution- free inference (Mussio, et al, 1994) Distribution-free significance tests (sometimes called noil- parametric significance tests, although the two terms are not synonyms) collect a broad category of significance tests which can be used instead of classical significance tests. There exist many different significance distribution-free tests, as above mentioned. Nevertheless for the sake of brevity, only few multiple significance distribution-free tests are presented in the follows, concerning : • goodness of fit of population distributions • independence against interdependence or dependence (e.g. collinearily) • homogeneity of variance components for several variances • variance analysis of several means, Where the last two tests are repeated for independent and correlated samples, respectively. A. Kolmogoroff-Smimoff s test on the goodness of fit of population distributions. UUU It is a powerful test and its use is rccommendable instead of classical chi-square goodness of fit of population distribution test UUU The test criterion compares the largest absolute difference between cumulative sample frequencies and population distribution with Kolmogoroff-Smimoff s extremal distribution critical values, where the degress olT freedom are the size of the samples 13-1 MU Kolmogoroff-Smimoffs test on the independence against interdependence. MU The same test performed for the goodness of fit of population distributions can be used to the test the independence against interdependence. Indeed because the goodness of fit hasn’t limitations concerning the dimension of domain of population distributions, these could be derived by multiplying marginal frequencies. UUU Thus comparing each multidimensional frequency with the product of the coiresponding marginal frequencies, the test on the independence against interdependence is performed. 'Hie test criterion is, obviously, the same explained for the test on the goodness of fit of population distributions, moreover because the uniqueness of the test, its power is conserved. 13-2 Modified Wilcoxon-Wilcox’s tes according Lawlay on tire independence against dependence (e.g. collinearity) This lest must be used instead of classical Hotelling’s test, when population distributions aren’t normal. It is a generalization of Spearman’s rank tes for one Spearman’s rank correlation coefficient under the same hypotheses. Let remember that the Spearman’s rank correlation coefficient is a function of the sum of rank difference squares, where the ranks are assigned separately to the largest one and then compared, element by clement, in terms of their difference. Furthermore when the null hypothesis is the standardized value of the Spearman’s correlation coefficient with the student’s t distribution critical value, where the degrees of freedom are the size of sample minus two The test criterion compares a quadratic function of the standardized Spearman’s rank correlation coefficient squares with chi-square distribution critical value, where tire degrees of freedom are the number of terms of the quadratic combinations. C-l UUU Kruskall-Wallis’ test on the homogeneity of variance components for several variances of independent samples This test must be used instead of classical Barlctt’s test, when population distributions aren’t normal, although samples must be independent. It is a generalization of Siegel-'furkey’s rank lest for two variances under the same hypotheses. Therefore the ranks are assigned from the smallest absolute residual to the largest one, after having put all samples together and sorting their residuals, so that the dispersion of each appears immediately. The test criterion compares a quadratic function of the sums of the ranks of the samples with chisquare distribution critical value, where the degrees of freedom are the number of samples minus one. C-2 Friedman’s test on the homogeneity of variance components for several variances of correlated samples UUU This test is less powerful than the above mentioned Kruskall-Wallis’ test, but it penniLs to analyze correlated samples. This is a generalization of Thompson’s sign test for two variances under the same hypotheses. Therefore the rank are assigned to the absolute residuals, element by element across the samples in increasing order, after having paired all samples. The test criterion compares a quadratic function of the sums of the ranks of the samples with chi-square distribution critical values, where the degrees of freedom arc the number ot samples minus one.

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