Open Access. Powered by Scholars. Published by Universities.®

Physical Sciences and Mathematics Commons

Open Access. Powered by Scholars. Published by Universities.®

Articles 1 - 3 of 3

Full-Text Articles in Physical Sciences and Mathematics

Is Overexcitability A Differential Personality Attribute Of High-Iq Youth? A Comparison Study Of Identified Gifted And Vocational High School Teenagers Using The Overexcitability Questionnaire (Oeq), Jane Piirto, Lori Beach, Geri Cassone, Robbin Rogers, John Fraas Jul 2000

Is Overexcitability A Differential Personality Attribute Of High-Iq Youth? A Comparison Study Of Identified Gifted And Vocational High School Teenagers Using The Overexcitability Questionnaire (Oeq), Jane Piirto, Lori Beach, Geri Cassone, Robbin Rogers, John Fraas

John W. Fraas

No abstract provided.


Semiparametric Regression: An Exposition And Application To Print Advertising Data, Michael S. Smith, Robert Kohn, Sharat K. Mathur Dec 1999

Semiparametric Regression: An Exposition And Application To Print Advertising Data, Michael S. Smith, Robert Kohn, Sharat K. Mathur

Michael Stanley Smith

A new regression based approach is proposed for modeling marketing databases. The approach is Bayesian and provides a number of significant improvements over current methods. Independent variables can enter into the model in either a parametric or nonparametric manner, significant variables can be identified from a large number of potential regressors and an appropriate transformation of the dependent variable can be automatically selected from a discrete set of pre-specified candidate transformations. All these features are estimated simultaneously and automatically using a Bayesian hierarchical model coupled with a Gibbs sampling scheme. Being Bayesian, it is straightforward to introduce subjective information about …


Adaptive Testing In Arch Models, Douglas G. Steigerwald, Oliver Linton Dec 1999

Adaptive Testing In Arch Models, Douglas G. Steigerwald, Oliver Linton

Douglas G. Steigerwald

Specification tests for conditional heteroskedasticity that are derived under the assumption that the density of the innovation is Gaussian may not be powerful in light of the recent empirical results that the density is not Gaussian. We obtain specification tests for conditional heteroskedasticity under the assumption that the innovation density is a member of a general family of densities. Our test statistics maximize asymptotic local power and weighted average power criteria for the general family of densities. We establish both first-order and second-order theory for our procedures. Simulations indicate that asymptotic power gains are achievable in finite samples.