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Divergence Measures (divergence + measure)

Distribution by Scientific Domains

Mathematics and Statistics	42%

Selected Abstracts

A NEW FAMILY OF DIVERGENCE MEASURES FOR TESTS OF FIT

AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 2 2010
K. Mattheou
Summary The aim of this work is to investigate a new family of divergence measures based on the recently introduced Basu, Harris, Hjort and Jones (BHHJ) measure of divergence (Biometrika,85, 549,559). The new family is investigated in connection with hypothesis testing problems, and new test statistics are proposed. Simulations are performed to check the appropriateness of the proposed test statistics. [source]

Predictive ability of models for calving difficulty in US Holsteins

JOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 3 2009
E.L. De Maturana
Summary The performance of alternative threshold models for analyzing calving difficulty (CD) in Holstein cows was evaluated in terms of predictive ability. Four models were considered, with CD classified into either three or four categories and analysed either as a single trait or jointly with gestation length (GL). The data contained GL and CD records from 90 393 primiparous cows, sired by 1122 bulls and distributed over 935 herd-calving year classes. Predictive ability of each model was evaluated using four criteria: mean squared error of the difference between observed and predicted CD scores; a Kullback-Leibler divergence measure between the observed and predicted distributions of CD scores; Pearson's correlation between observed and predicted CD scores and ability to correctly classify bulls as above or below average for incidence of CD. In general, the four models had similar predictive abilities. The joint analysis of CD with GL produced little, if any, improvement in predictive ability over univariate models. In light of the small difference in predictive ability between models treating CD with three or four categories and considering that a greater number of categories can provide more information, analysis of CD classified into four categories seems warranted. [source]

Alternative tilts for nonparametric option pricing

THE JOURNAL OF FUTURES MARKETS, Issue 10 2010
M. Ryan Haley
This study generalizes the nonparametric approach to option pricing of Stutzer, M. (1996) by demonstrating that the canonical valuation methodology introduced therein is one member of the Cressie,Read family of divergence measures. Alhough the limiting distribution of the alternative measures is identical to the canonical measure, the finite sample properties are quite different. We assess the ability of the alternative divergence measures to price European call options by approximating the risk-neutral, equivalent martingale measure from an empirical distribution of the underlying asset. A simulation study of the finite sample properties of the alternative measure changes reveals that the optimal divergence measure depends upon how accurately the empirical distribution of the underlying asset is estimated. In a simple Black,Scholes model, the optimal measure change is contingent upon the number of outliers observed, whereas the optimal measure change is a function of time to expiration in the stochastic volatility model of Heston, S. L. (1993). Our extension of Stutzer's technique preserves the clean analytic structure of imposing moment restrictions to price options, yet demonstrates that the nonparametric approach is even more general in pricing options than originally believed. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:983,1006, 2010 [source]

Default Bayesian Priors for Regression Models with First-Order Autoregressive Residuals

JOURNAL OF TIME SERIES ANALYSIS, Issue 3 2003
Malay Ghosh
Abstract. The objective of this paper is to develop default priors when the parameter of interest is the autocorrelation coefficient in normal regression models with first-order autoregressive residuals. Jeffreys' prior as well as reference priors are found. These priors are compared in the light of how accurately the coverage probabilities of Bayesian credible intervals match the corresponding frequentist coverage probabilities. It is found that the reference priors have a definite edge over Jeffreys' prior in this respect. Also, the credible intervals based on these reference priors seem superior to similar intervals based on certain divergence measures. [source]