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Full-Text Articles in Physical Sciences and Mathematics
Separation Property Of Solutions For A Semilinear Elliptic Equation, Yi Liu, Yi Li, Yinbin Deng
Separation Property Of Solutions For A Semilinear Elliptic Equation, Yi Liu, Yi Li, Yinbin Deng
Yi Li
In this paper, we study the following elliptic problem[formula]where K(x) is a given function in Cα(n\0) for some fixed α∈(0, 1), p>1 is a constant. Some existence, monotonicity and asymptotic expansion at infinity of solutions of (*) are discussed.
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.