Displaying similar documents to “ k -Depth-nearest Neighbour Method and its Performance on Skew-normal Distributons”

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.

Construction of multivariate distributions: a review of some recent results.

José María Sarabia, Emilio Gómez-Déniz (2008)

SORT

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The construction of multivariate distributions is an active field of research in theoretical and applied statistics. In this paper some recent developments in this field are reviewed. Specifically, we study and review the following set of methods: (a) Construction of multivariate distributions based on order statistics, (b) Methods based on mixtures, (c) Conditionally specified distributions, (d) Multivariate skew distributions, (e) Distributions based on the method of the variables...

Parameter estimation of S-distributions with alternating regression.

I-Chun Chou, Harald Martens, Eberhard O. Voit (2007)

SORT

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We propose a novel 3-way alternating regression (3-AR) method as an effective strategy for the estimation of parameter values in S-distributions from frequency data. The 3-AR algorithm is very fast and performs well for error-free distributions and artificial noisy data obtained as random samples generated from S-distributions, as well as for traditional statistical distributions and for actual observation data. In rare cases where the algorithm does not immediately converge, its enormous...

Core functions and core divergences of regular distributions

Zdeněk Fabián, Igor Vajda (2003)

Kybernetika

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On bounded or unbounded intervals of the real line, we introduce classes of regular statistical families, called Johnson families because they are obtained using generalized Johnson transforms. We study in a rigorous manner the formerly introduced concept of core function of a distribution from a Johnson family, which is a modification of the well known score function and which in a one-to-one manner represents the distribution. Further, we study Johnson parametrized families obtained...

Improving predictive distributions.

Morris H. DeGroot (1980)

Trabajos de Estadística e Investigación Operativa

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Consider a sequence of decision problems S, S, ... and suppose that in problem S the statistician must specify his predictive distribution F for some random variable X and make a decision based on that distribution. For example, X might be the return on some particular investment and the statistician must decide whether or not to make that investment. The random variables X, X, ... are assumed to be independent and completely unrelated. It is also assumed that each predictive distribution...

Three methods for constructing reference prior distributions.

Eusebio Gómez Sánchez-Manzano, Miguel A. Gómez Villegas (1990)

Revista Matemática de la Universidad Complutense de Madrid

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Three methods are proposed for constructing reference prior densities for certain biparametric distribution families. These densities represent approximations to the Bayesian concept of noninformative distribution.