Characterizations of probability distributions via bivariate regression of record values

Bairamov et al. (Aust N Z J Stat 47:543-547, 2005) characterize the exponential distribution in terms of the regression of a function of a record value with its adjacent record values as covariates. We extend these results to the case of non-adjacent covariates. We also consider a more general setting involving monotone transformations. As special cases, we present characterizations involving weighted arithmetic, geometric, and harmonic means.Comment: accepted in Metrik

Characterizations of Pareto, Weibull and Power Function Distributions Based On Generalized Order Statistics

Characterizations of probability distributions by different regression conditions on generalized order statistics has attracted the attention of many researchers. We present here, characterization of Pareto and Weibull distributions based on the conditional expectation of generalized order statistics extending the characterization results reported by Jin and Lee (2014). We also present a characterization of the power function distribution based on the conditional expectation of lower generalized order statistics

Characterizations of Certain Continuous Univariate Distributions Based on the Conditional Distribution of Generalized Order Statistics

The problem of characterizing probability distributions is an interesting problem which has recently attracted the attention of many researchers. Various characterization results have been established in different directions as reported in the literature. We present here, various characterizations of certain univariate continuous distributions based on the conditional distribution of generalized order statistics

Characterizations of Student's t-distribution via regressions of order statistics

Utilizing regression properties of order statistics, we characterize a family of distributions introduced by Akhundov, Balakrishnan, and Nevzorov (2004), that includes the t-distribution with two degrees of freedom as one of its members. Then we extend this characterization result to t-distribution with more than two degrees of freedom.Comment: To appear in "Statistics

New UU-empirical tests of symmetry based on extremal order statistics, and their efficiencies

We use a characterization of symmetry in terms of extremal order statistics which enables to build several new nonparametric tests of symmetry. We discuss their limiting distributions and calculate their local exact Bahadur efficiency under location alternative which is mostly high.Comment: 17 page

Characterizations of Distributions via Conditional Expectation of Generalized Order Statistics

Characterizations of probability distributions by different regression conditions on generalized order statistics has attracted the attention of many researchers. We present here, characterizations of certain continuous distributions based on the conditional expectation of generalized order statistics

Evaluation of Physical Finger Input Properties for Precise Target Selection

The multitouch tabletop display provides a collaborative workspace for multiple users around a table. Users can perform direct and natural multitouch interaction to select target elements using their bare fingers. However, physical size of fingertip varies from one person to another which generally introduces a fat finger problem. Consequently, it creates the imprecise selection of small size target elements during direct multitouch input. In this respect, an attempt is made to evaluate the physical finger input properties i.e. contact area and shape in the context of imprecise selection

Characterizations of Levy Distribution via Sub-Independence of the Random Variables and Truncated Moments

The concept of sub-independence is based on the convolution of the distributions of the random variables. It is much weaker than that of independence, but is shown to be sufficient to yield the conclusions of important theorems and results in probability and statistics. It also provides a measure of dissociation between two random variables which is much stronger than uncorrelatedness. Following Ahsanullah and Nevzorov (2014), we present certain characterizations of Levy distribution based on: (i) the sub-independence of the random variables; (ii) a simple relationship between two truncated moments; (iii) conditional expectation of certain function of the random variable. In case of independence, characterization (i) reduces to that of Ahsanullah and Nevzorov (2014)