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Parametric Family (parametric + family)

Distribution by Scientific Domains
Mathematics and Statistics83%

Selected Abstracts

Life Distributions: Structure of Nonparametric, Semiparametric, and Parametric Families by Albert W. Marshall, Ingram Olkin

INTERNATIONAL STATISTICAL REVIEW, Issue 2 2008
Martin Crowder
No abstract is available for this article. [source]

A unified view on skewed distributions arising from selections

THE CANADIAN JOURNAL OF STATISTICS, Issue 4 2006
Reinaldo B. Arellano-Valle
Abstract Parametric families of multivariate nonnormal distributions have received considerable attention in the past few decades. The authors propose a new definition of a selection distribution that encompasses many existing families of multivariate skewed distributions. Their work is motivated by examples that involve various forms of selection mechanisms and lead to skewed distributions. They give the main properties of selection distributions and show how various families of multivariate skewed distributions, such as the skew-normal and skew-elliptical distributions, arise as special cases. The authors further introduce several methods of constructing selection distributions based on linear and nonlinear selection mechanisms. Une perspective intégrée des lois asymétriques issues de processus de sélection Les familles paramétriques de lois multivariées non gaussiennes ont suscité beaucoup d'intér,t depuis quelques décennies. Les auteurs proposent une nouvelle définition du concept de loi de sélection qui englobe plusieurs familles connues de lois asymétriques multivariées. Leurs travaux sont motivés par diverses situations faisant intervenir des mécanismes de sélection et conduisant à des lois asymétriques. Ds mentionnent les principales propriétés des lois de sélection et montrent comment diverses familles de lois asymétriques multivariées telles que les lois asymétriques normales ou elliptiques émergent comme cas particuliers. Les auteurs présentent en outre plusieurs méthodes de construction de lois de sélection fondées sur des mécanismes linéaires ou non linéaires. [source]

Spatiotemporal generation of long-range dependence models and estimation

ENVIRONMETRICS, Issue 2 2006
M. P. Frías
Abstract A parametric family of spatiotemporal models displaying separable isotropic long-range dependence, in space and time, is introduced in a fractional generalized framework. The weak-sense implementation of estimation methods based on the integrated periodogram, the variogram and the wavelet transform, to estimate the long-memory parameter vector is discussed. The construction of separable and non-separable anisotropic long-range dependence spatiotemporal processes is also described considering fractional integration filters. Copyright © 2005 John Wiley & Sons, Ltd. [source]

Explaining the characteristics of the power (CRRA) utility family

HEALTH ECONOMICS, Issue 12 2008
Peter P. WakkerArticle first published online: 22 JAN 200
Abstract The power family, also known as the family of constant relative risk aversion (CRRA), is the most widely used parametric family for fitting utility functions to data. Its characteristics have, however, been little understood, and have led to numerous misunderstandings. This paper explains these characteristics in a manner accessible to a wide audience. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Minimum , -divergence estimation for arch models

JOURNAL OF TIME SERIES ANALYSIS, Issue 1 2006
S. Ajay Chandra
Abstract., This paper considers a minimum , -divergence estimation for a class of ARCH(p) models. For these models with unknown volatility parameters, the exact form of the innovation density is supposed to be unknown in detail but is thought to be close to members of some parametric family. To approximate such a density, we first construct an estimator for the unknown volatility parameters using the conditional least squares estimator given by Tjøstheim [Stochastic processes and their applications (1986) Vol. 21, pp. 251,273]. Then, a nonparametric kernel density estimator is constructed for the innovation density based on the estimated residuals. Using techniques of the minimum Hellinger distance estimation for stochastic models and residual empirical process from an ARCH(p) model given by Beran [Annals of Statistics (1977) Vol. 5, pp. 445,463] and Lee and Taniguchi [Statistica Sinica (2005) Vol. 15, pp. 215,234] respectively, it is shown that the proposed estimator is consistent and asymptotically normal. Moreover, a robustness measure for the score of the estimator is introduced. The asymptotic efficiency and robustness of the estimator are illustrated by simulations. The proposed estimator is also applied to daily stock returns of Dell Corporation. [source]

Martingales and large deviations for binary search trees

RANDOM STRUCTURES AND ALGORITHMS, Issue 2 2001
Jean Jabbour-Hattab
Abstract We establish an almost sure large deviations theorem for the depth of the external nodes of binary search trees (BSTs). To achieve this, a parametric family of martingales is introduced. This family also allows us to get asymptotic results on the number of external nodes at deepest level. © 2001 John Wiley & Sons, Inc. Random Struct. Alg., 19: 112,127, 2001 [source]