Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 4 of 4
Full-Text Articles in Statistical Methodology
An Extension Of Euclidean Distance Matrix Analysis Applied To Craniofacial Growth Prediction, Mark K. Batesole
An Extension Of Euclidean Distance Matrix Analysis Applied To Craniofacial Growth Prediction, Mark K. Batesole
Loma Linda University Electronic Theses, Dissertations & Projects
This study is [sic] introduces a new extension of Euclidean Distance Matrix Analysis (EDMA) as applied to growth prediction analysis. Using EDMA eliminates the presupposition of a set growth pattern, which is introduced by traditional superimposition techniques. When EDMA is extended to analyze prediction methodologies, using a non-age-matched growth sample, some shortcomings become evident. These involve bootstrapping techniques, relative difference in growth, and absence of clinical, real world measure. To overcome these issues, a statistical approach using the Wilcoxen signed rank test, absolute difference in growth, and a new method to evaluate the error in a prediction methodology's landmark identification …
Weighted Simplex Procedures For Determining Boundary Points And Constants For The Univariate And Multivariate Power Methods, Todd C. Headrick, Shlomo S. Sawilowsky
Weighted Simplex Procedures For Determining Boundary Points And Constants For The Univariate And Multivariate Power Methods, Todd C. Headrick, Shlomo S. Sawilowsky
Todd Christopher Headrick
The power methods are simple and efficient algorithms used to generate either univariate or multivariate non-normal distributions with specified values of (marginal) mean, standard deviation, skew, and kurtosis. The power methods are bounded as are other transformation techniques. Given an exogenous value of skew, there is an associated lower bound of kurtosis. Previous approximations of the boundary for the power methods are either incorrect or inadequate. Data sets from education and psychology can be found to lie within, near or outside the boundary of the power methods. In view of this, we derived necessary and sufficient conditions using the Lagrange …
Semiparametric Regression: An Exposition And Application To Print Advertising Data, Michael S. Smith, Robert Kohn, Sharat K. Mathur
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
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.