Displaying similar documents to “Weighted halfspace depth”

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

Chanchal Kundu (2014)

Applications of Mathematics

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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.

k -Depth-nearest Neighbour Method and its Performance on Skew-normal Distributons

Ondřej Vencálek (2013)

Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica

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In the present paper we investigate performance of the k -depth-nearest classifier. This classifier, proposed recently by Vencálek, uses the concept of data depth to improve the classification method known as the k -nearest neighbour. Simulation study which is presented here deals with the two-class classification problem in which the considered distributions belong to the family of skewed normal distributions.

A weighted version of Gamma distribution

Kanchan Jain, Neetu Singla, Rameshwar D. Gupta (2014)

Discussiones Mathematicae Probability and Statistics

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Weighted Gamma (WG), a weighted version of Gamma distribution, is introduced. The hazard function is increasing or upside-down bathtub depending upon the values of the parameters. This distribution can be obtained as a hidden upper truncation model. The expressions for the moment generating function and the moments are given. The non-linear equations for finding maximum likelihood estimators (MLEs) of parameters are provided and MLEs have been computed through simulations and also for...

Concept of Data Depth and Its Applications

Ondřej Vencálek (2011)

Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica

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Data depth is an important concept of nonparametric approach to multivariate data analysis. The main aim of the paper is to review possible applications of the data depth, including outlier detection, robust and affine-equivariant estimates of location, rank tests for multivariate scale difference, control charts for multivariate processes, and depth-based classifiers solving discrimination problem.

Weighting, likelihood ratio order and life distributions

Magdalena Skolimowska, Jarosław Bartoszewicz (2006)

Applicationes Mathematicae

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We use weighted distributions with a weight function being a ratio of two densities to obtain some results of interest concerning life and residual life distributions. Our theorems are corollaries from results of Jain et al. (1989) and Bartoszewicz and Skolimowska (2006).