Généralisation du paradoxe de Simpson
Generalization of the Kappa coeficient for ordinal categorical data, multiple observers and incomplete designs.
This paper presents a generalization of the kappa coefficient for multiple observers and incomplete designs. This generalization involves ordinal categorical data and includes weights which permit pondering the severity of disagreement. A generalization for incomplete designs of the kappa coefficient based on explicit definitions of agreement is also proposed. Both generalizations are illustrated with data from a medical diagnosis pilot study.
Generalized duration measures in a risk immunization setting. Implementation of the Heath-Jarrow-Morton model
The aim of this paper is to set different lower bounds on the change of the expected net cash flow value at time H > 0 in general term structure models, referring to the studies of Fong and Vasiček (1984), Nawalkha and Chambers (1996), and Balbás and Ibáñez (1998) among others. New immunization strategies are derived with new risk measures: generalized duration and generalized M-absolute of Nawalkha and Chambers, and exponential risk measure. Furthermore, examples of specific one-factor HJM models...
Genetic linkage analysis: An irregular statistical problem.
Geometry, statistics and decision making in gene therapy.
Gini indices and the moments of the share density function
The expected value of the share density of the income distribution can be expressed in terms of the Gini index. The variance of the share density of the income distribution is interesting because it gives a relationship between the first and the second order Gini indices. We find an expression for this variance and, as a result, we obtain some nontrivial bounds on these Gini indices. We propose new statistics on the income distribution based on the higher moments of the share density function. These...
Global correlation and uncertainty accounting
For a high dimensional field of random variables, global correlation is defined as the ratio of average covariance and average variance, and its elementary properties are studied. Global correlation is used to harmonize uncertainty assessments at global and local scales. It can be estimated by the correlation of random aggregations of fixed size of disjoint sets of random variables. Illustrative applications are given using crop loss per county per year and forest carbon.
Grüss-type bounds for the covariance of transformed random variables.