Statistical methods in physico-chemical characterization of newly synthesized compounds.
Djaković-Sekulić, Tatjana, Lozanov-Crvenković, Zagorka, Perišić-Janjić, Nada (2008)
Novi Sad Journal of Mathematics
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Djaković-Sekulić, Tatjana, Lozanov-Crvenković, Zagorka, Perišić-Janjić, Nada (2008)
Novi Sad Journal of Mathematics
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Love, Robert F., Üster, Halit (2001)
Journal of Applied Mathematics and Decision Sciences
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Nguyen Bac-Van
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The regression model X(t),Y(t);t=1,...,n with random explanatory variable X is transformed by prescribing a partition of the given domain S of X-values and specifyingThrough the conditioningthe initial model with i.i.d. pairs (X(t),Y(t)),t=1,...,n, becomes a conditional fixed-design modelwhere the response variables are independent and distributed according to the mixed conditional distribution of Y given X at the observed value .Afterwards, we investigate the casewhich...
Hausen, Jürgen, Herppich, Elaine, Süss, Hendrik (2011)
Documenta Mathematica
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Aldea, Costel (2005)
Acta Universitatis Apulensis. Mathematics - Informatics
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M. O. Omeike, A. U. Afuwape (2010)
Kragujevac Journal of Mathematics
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Drezner, Zvi, Marcoulides, George A., Stohs, Mark Hoven (2001)
Journal of Applied Mathematics and Decision Sciences
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Baird, Paul, Gallardo, Luis (2002)
Beiträge zur Algebra und Geometrie
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Anabela Marques, Ana Sousa Ferreira, Margarida G.M.S. Cardoso (2013)
Biometrical Letters
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In Discrete Discriminant Analysis one often has to deal with dimensionality problems. In fact, even a moderate number of explanatory variables leads to an enormous number of possible states (outcomes) when compared to the number of objects under study, as occurs particularly in the social sciences, humanities and health-related elds. As a consequence, classi cation or discriminant models may exhibit poor performance due to the large number of parameters to be estimated. In the present...
Đorđe Kadijević (2005)
The Teaching of Mathematics
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Martynov, L.M. (2001)
Siberian Mathematical Journal
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Petr Novák (2013)
Kybernetika
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The Accelerated Failure Time model presents a way to easily describe survival regression data. It is assumed that each observed unit ages internally faster or slower, depending on the covariate values. To use the model properly, we want to check if observed data fit the model assumptions. In present work we introduce a goodness-of-fit testing procedure based on modern martingale theory. On simulated data we study empirical properties of the test for various situations.