Higher Accuracy for Bayesian and Frequentist Inference: Large Sample Theory for Small Sample Likelihood

Recent likelihood theory produces $p$-values that have remarkable accuracy and wide applicability. The calculations use familiar tools such as maximum likelihood values (MLEs), observed information and parameter rescaling. The usual evaluation of such $p$-values is by simulations, and such simulations do verify that the global distribution of the $p$-values is uniform(0, 1), to high accuracy in repeated sampling. The derivation of the $p$-values, however, asserts a stronger statement, that they have a uniform(0, 1) distribution conditionally, given identified precision information provided by the data. We take a simple regression example that involves exact precision information and use large sample techniques to extract highly accurate information as to the statistical position of the data point with respect to the parameter: specifically, we examine various $p$-values and Bayesian posterior survivor $s$-values for validity. With observed data we numerically evaluate the various $p$-values and $s$-values, and we also record the related general formulas. We then assess the numerical values for accuracy using Markov chain Monte Carlo (McMC) methods. We also propose some third-order likelihood-based procedures for obtaining means and variances of Bayesian posterior distributions, again followed by McMC assessment. Finally we propose some adaptive McMC methods to improve the simulation acceptance rates. All these methods are based on asymptotic analysis that derives from the effect of additional data. And the methods use simple calculations based on familiar maximizing values and related informations. The example illustrates the general formulas and the ease of calculations, while the McMC assessments demonstrate the numerical validity of the $p$-values as percentage position of a data point. The example, however, is very simple and transparent, and thus gives little indication that in a wide generality of models the formulas do accurately separate information for almost any parameter of interest, and then do give accurate $p$-value determinations from that information. As illustration an enigmatic problem in the literature is discussed and simulations are recorded; various examples in the literature are cited.Comment: Published in at http://dx.doi.org/10.1214/07-STS240 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Random Feature Maps via a Layered Random Projection (LaRP) Framework for Object Classification

The approximation of nonlinear kernels via linear feature maps has recently gained interest due to their applications in reducing the training and testing time of kernel-based learning algorithms. Current random projection methods avoid the curse of dimensionality by embedding the nonlinear feature space into a low dimensional Euclidean space to create nonlinear kernels. We introduce a Layered Random Projection (LaRP) framework, where we model the linear kernels and nonlinearity separately for increased training efficiency. The proposed LaRP framework was assessed using the MNIST hand-written digits database and the COIL-100 object database, and showed notable improvement in object classification performance relative to other state-of-the-art random projection methods.Comment: 5 page

The yield and post-yield behavior of high-density polyethylene

An experimental and analytical evaluation was made of the yield and post-yield behavior of high-density polyethylene, a semi-crystalline thermoplastic. Polyethylene was selected for study because it is very inexpensive and readily available in the form of thin-walled tubes. Thin-walled tubular specimens were subjected to axial loads and internal pressures, such that the specimens were subjected to a known biaxial loading. A constant octahederal shear stress rate was imposed during all tests. The measured yield and post-yield behavior was compared with predictions based on both isotropic and anisotropic models. Of particular interest was whether inelastic behavior was sensitive to the hydrostatic stress level. The major achievements and conclusions reached are discussed

Surface roughness influence on the quality factor of high frequency nanoresonators

Surface roughness influences significantly the quality factor of high frequency nanoresonators for large frequency - relaxation times within the non-Newtonian regime, where a purely elastic dynamics develops. It is shown that the influence of sort wavelength roughness, which is expressed by the roughness exponent H for the case of self-affine roughness, plays significant role in comparison with the effect of the long wavelength roughness parameters such as the rms roughness amplitude and the lateral roughness correlation length. Therefore, the surface morphology can play important role in designing high-frequency resonators operating within the non-Newtonian regime.Comment: 13 pages, 4 figures, To appear in J. Appl. Phys. (2008