Characterizations of a mixture of two general distributions
Characterizations of Archimedean -copulas
We present three characterizations of -dimensional Archimedean copulas: algebraic, differential and diagonal. The first is due to Jouini and Clemen. We formulate it in a more general form, in terms of an -variable operation derived from a binary operation. The second characterization is in terms of first order partial derivatives of the copula. The last characterization uses diagonal generators, which are “regular” diagonal sections of copulas, enabling one to recover the copulas by means of an...
Characterizations of continuous distributions through inequalities involving the expected values of selected functions
Nanda (2010) and Bhattacharjee et al. (2013) characterized a few distributions with help of the failure rate, mean residual, log-odds rate and aging intensity functions. In this paper, we generalize their results and characterize some distributions through functions used by them and Glaser’s function. Kundu and Ghosh (2016) obtained similar results using reversed hazard rate, expected inactivity time and reversed aging intensity functions. We also, via -function defined by Cacoullos and Papathanasiou...
Characterizations of distributions by moments of order statistics when the sample size is random
We give characterizations of the uniform distribution in terms of moments of order statistics when the sample size is random. Special cases of a random sample size (logarithmic series, geometrical, binomial, negative binomial, and Poisson distribution) are also considered.
Characterizations of Distributions Via Two Expected Values of Order Statistics.
Characterizations of exponential distributions by spacings of generalized order statistics
-3Properties of spacings of generalized order statistics based on IFR and DFR distributions are shown to characterize exponential distributions.
Characterizations of multinomial distributions based on conditional distributions.
Characterizations of Normality in Translation Classes by Properties of Bayes Estimators.
Characterizations of power distributions via moments of order statistics and record values
Power distributions can be characterized by equalities involving three moments of order statistics. Similar equalities involving three moments of k-record values can also be used for such a characterization. The case of samples with random sizes is also considered.
Characterizations of some Distributions Connected Wtth Extremal-type Distributions
Characterizations of the exponential distribution based on certain properties of its characteristic function
Two characterizations of the exponential distribution among distributions with support the nonnegative real axis are presented. The characterizations are based on certain properties of the characteristic function of the exponential random variable. Counterexamples concerning more general possible versions of the characterizations are given.
Characterizations of the inverse Weibull distribution and generalized extreme value distributions by moments of kth record values
We give characterization conditions for the inverse Weibull distribution and generalized extreme value distributions by moments of kth record values.
Characterizations of uniform and exponential distributions via moments of the kth record values randomly indexed
We characterize uniform and exponential distributions via moments of the kth record statistics. Too and Lin's (1989) results are contained in our approach.
Characterizing experimental designs by properties of the standard quadratic forms of observations
For any orthogonal multi-way classification, the sums of squares appearing in the analysis of variance may be expressed by the standard quadratic forms involving only squares of the marginal and total sums of observations. In this case the forms are independent and nonnegative definite. We characterize all two-way classifications preserving these properties for some and for all of the standard quadratic forms.
Characterizing the general multivariate normal distribution through the conditional distributions.
In this paper we give a characterization of the multivariate normal distribution through the conditional distributions in the most general case, which include the singular distribution.
Charakterisierung der zweiseitigen Exponentialverteilung.
Charakteristická funkce Kendallova koeficientu korelace pořadí
Checking proportional rates in the two-sample transformation model
Transformation models for two samples of censored data are considered. Main examples are the proportional hazards and proportional odds model. The key assumption of these models is that the ratio of transformation rates (e. g., hazard rates or odds rates) is constant in time. A~method of verification of this proportionality assumption is developed. The proposed procedure is based on the idea of Neyman's smooth test and its data-driven version. The method is suitable for detecting monotonic as well...
Chi-squared goodness-of-fit test for the family of logistic distributions
Choice of a Survival Model for Patients With a Brain Tumour.