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Displaying 41 – 60 of 218

Characterizations based on length-biased weighted measure of inaccuracy for truncated random variables

Chanchal Kundu (2014)

Applications of Mathematics

In survival studies and life testing, the data are generally truncated. Recently, authors have studied a weighted version of Kerridge inaccuracy measure for truncated distributions. In the present paper we consider weighted residual and weighted past inaccuracy measure and study various aspects of their bounds. Characterizations of several important continuous distributions are provided based on weighted residual (past) inaccuracy measure.

Characterizations of a mixture of two general distributions

Lingappaiah, G.S. (1978)

Portugaliae mathematica

Characterizations of continuous distributions through inequalities involving the expected values of selected functions

Faranak Goodarzi, Mohammad Amini, Gholam Reza Mohtashami Borzadaran (2017)

Applications of Mathematics

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 $w (\cdot)$ -function defined by Cacoullos and Papathanasiou...

Characterizations of distributions by moments of order statistics when the sample size is random

Zofia Grudzień, Dominik Szynal (1995)

Applicationes Mathematicae

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.

J.S. Hwang, G.D. Lin (1988)

Metrika

Characterizations of exponential distributions by spacings of generalized order statistics

Erhard Cramer, Udo Kamps, Mohammad Z. Raqab (2003)

Applicationes Mathematicae

-3Properties of spacings of generalized order statistics based on IFR and DFR distributions are shown to characterize exponential distributions.

Characterizations of Normality in Translation Classes by Properties of Bayes Estimators.

W. jr. Eberl (1986)

Metrika

Characterizations of power distributions via moments of order statistics and record values

Z. Grudzień, D. Szynal (1999)

Applicationes Mathematicae

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

Slobodanka Janjić (1986)

Publications de l'Institut Mathématique

Characterizations of the exponential distribution based on certain properties of its characteristic function

Simos G. Meintanis, George Iliopoulos (2003)

Kybernetika

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

Piotr Pawlas, Dominik Szynal (2000)

Applicationes Mathematicae

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

Zofia Grudzień, Dominik Szynal (1997)

Applicationes Mathematicae

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

Czesław Stępniak (2007)

Applicationes Mathematicae

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.

Ramón Ardanuy Albajar, Jesús F. López Fidalgo (1997)

Extracta Mathematicae

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.

P. Findeisen (1982)

Metrika

Circular Polya distributions of order $k$ .

Tripsiannis, Gregory A., Philippou, Andreas N. (2003)

International Journal of Mathematics and Mathematical Sciences

Commentaire de l'article : «The relevation transform and a generalization of the gamma distribution function»

M. Girault (1976)

RAIRO - Operations Research - Recherche Opérationnelle

Comparison of the most typical probability distributions with respect to spread

Jaromír Antoch (1978)

Časopis pro pěstování matematiky

Conditional Distributions and the Bivariate Normal Distribution.

E. Castillo, J. Galambos (1989)

Metrika

Conditions for bimodality and multimodality of a mixture of two unimodal densities

Šárka Došlá (2009)

Kybernetika

Conditions for bimodality of mixtures of two unimodal distributions are investigated in some special cases. Based on general characterizations, explicit criteria for the parameters are derived for mixtures of two Cauchy, logistic, Student, gamma, log-normal, Gumbel and other distributions.

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