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A new approach to inter-rater agreement through stochastic orderings: the discrete case

Giovagnoli, Alessandra; Marzialetti, Johnny; Wynn, Henry - In: Metrika 67 (2008) 3, pp. 349-370

Persistent link: https://www.econbiz.de/10005375984

Breakdown concepts for contingency tables

Kuhnt, Sonja - In: Metrika 71 (2010) 3, pp. 281-294

Persistent link: https://www.econbiz.de/10008515484

Log-concavity and monotonicity of hazard and reversed hazard functions of univariate and multivariate skew-normal distributions

Gupta, Ramesh; Balakrishnan, N. - In: Metrika 75 (2012) 2, pp. 181-191

Persistent link: https://www.econbiz.de/10010539340

Large sample properties for a class of copulas in bivariate survival analysis

Romeo, José; Tanaka, Nelson; Pedroso-de-Lima, Antonio; … - In: Metrika 76 (2013) 8, pp. 997-1015

This work is concerned with asymptotic properties of the bivariate survival function estimator using the functional relationship between marginal survival functions and a class of copulas for the dependence structure. Specifically, we study consistency and weak convergence of the bivariate...

Persistent link: https://www.econbiz.de/10010728109

Spatial local M-estimation under association

Jia, Chen; Lixin, Zhang; Degui, Li - In: Metrika 67 (2008) 1, pp. 11-29

Persistent link: https://www.econbiz.de/10005178944

The association between two random elements: A complete characterization and odds ratio models

Osius, Gerhard - In: Metrika 60 (2004) 3, pp. 261-277

For random elements X and Y (e.g. vectors) a complete characterization of their association is given in terms of an … fittings). Restricting only the odds ratio function but not the marginals leads to semi-parmetric association models for which … statistical inference is available for samples drawn conditionally on either X or Y. Log-bilinear association models for random …

Persistent link: https://www.econbiz.de/10005598747

On the stochastic and dependence properties of the three-state systems

Ashrafi, S.; Asadi, M. - In: Metrika 78 (2015) 3, pp. 261-281

Suppose that a system has three states up, partial performance and down. We assume that for a random time <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$T_1$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <msub> <mi>T</mi> <mn>1</mn> </msub> </math> </EquationSource> </InlineEquation> the system is in state up, then it moves to state partial performance for time <InlineEquation ID="IEq2"> <EquationSource Format="TEX">$$T_2$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <msub> <mi>T</mi> <mn>2</mn> </msub> </math> </EquationSource> </InlineEquation> and then the system fails and goes to state down. We also denote the...</equationsource></equationsource></inlineequation></equationsource></equationsource></inlineequation>

Persistent link: https://www.econbiz.de/10011241005