Built-in bleeder system in laminated plastic structures

Method is described for interplying resin absorbing, glass bleeder cloth with the layup for fabricating laminated nonflammable plastic structures with wrinkle-free surfaces

Smoothed L-estimation of Regression Function

The Nadaraya-Watson nonparametric estimator of regression is known to be highly sensitive to the presence of outliers in data.This sensitivity can be reduced, for example, by using local L-estimates of regression.Whereas the local L-estimation is traditionally done using an empirical conditional distribution function, we propose to use instead a smoothed conditional distribution function.The asymptotic distribution of the proposed estimator is derived under mild ¯-mixing conditions, and additionally, we show that the smoothed L-estimation approach provides computational as well as statistical ¯nite-sample improvements.Finally, the proposed method is applied to the modelling of implied volatilitynonparametric regression;L-estimation;smoothed cumulative distribution function

Identification and Estimation of Nonseparable Single-Index Models in Panel Data with Correlated Random Effects

Abstract: The identification of parameters in a nonseparable single-index models with correlated random effects is considered in the context of panel data with a fixed number of time periods. The identification assumption is based on the correlated random-effect structure: the distribution of individual effects depends on the explanatory variables only by means of their time-averages. Under this assumption, the parameters of interest are identified up to scale and could be estimated by an average derivative estimator based on the local polynomial smoothing. The rate of convergence and asymptotic distribution of the proposed estimator are derived along with a test whether pooled estimation using all available time periods is possible. Finally, a Monte Carlo study indicates that our estimator performs quite well in finite samples.

The Determinants of VAT Introduction:A Spatial Duration Analysis

Abstract: The spatial survival models typically impose frailties, which characterize unobserved heterogeneity, to be spatially correlated. This specification relies highly on a pre-determinate covariance structure of the errors. However, the spatial effect may not only exist in the unobserved errors, but it can also be present in the baseline hazards and the dependent variables. A new spatial survival model with these three possible spatial correlation structures is explored and used to investigate the determinants of value-added tax implementation in 92 countries over the period 1970–2008 using the proposed model. The estimation results suggest the presence of a significant copycat effect among neighboring countries for both contiguity and distance weight matrices.

Smoothed L-estimation of Regression Function

The Nadaraya-Watson nonparametric estimator of regression is known to be highly sensitive to the presence of outliers in data.This sensitivity can be reduced, for example, by using local L-estimates of regression.Whereas the local L-estimation is traditionally done using an empirical conditional distribution function, we propose to use instead a smoothed conditional distribution function.The asymptotic distribution of the proposed estimator is derived under mild ¯-mixing conditions, and additionally, we show that the smoothed L-estimation approach provides computational as well as statistical ¯nite-sample improvements.Finally, the proposed method is applied to the modelling of implied volatilit

Operator Transformations Between Exactly Solvable Potentials and Their Lie Group Generators

One may obtain, using operator transformations, algebraic relations between the Fourier transforms of the causal propagators of different exactly solvable potentials. These relations are derived for the shape invariant potentials. Also, potentials related by real transformation functions are shown to have the same spectrum generating algebra with Hermitian generators related by this operator transformation.Comment: 13 pages with one Postscript figure, uses LaTeX2e with revte

Self‐consistent intermediate Hamiltonians : A coupled cluster type formulation of the singles and doubles configuration interaction matrix dressing

This paper presents a new self‐consistent dressing of a singles and doubles configuration interaction matrix which insures size‐consistency, separability into closed‐shell subsystems if localized molecular orbitals (MOs) are used, and which includes all fourth order corrections. This method yields, among several schemes, a reformulation of the coupled cluster method, including fully the cluster operators of single and double excitations, and partially those of the triples (Bartlett’s algorithm named CCSDT‐1a). Further improvement can be easily included by adding exclusion principle violating corrections. Since it leads to a matrix diagonalization, the method behaves correctly in case of near degeneracies between the reference determinant and some doubles. Due to its flexibility this formulation offers the possibility of consistent combination with less expensive treatments for the study of very large [email protected] ; [email protected]

Calculating the random guess scores of multiple-response and matching test items

For achievement tests, the guess score is often used as a baseline for the lowest possible grade for score to grade transformations and setting the cut scores. For test item types such as multiple-response, matching and drag-and-drop, determin-ing the guess score requires more elaborate calculations than the more straight-forward calculation of the guess score for True-False and multiple-choice test item formats. For various variants of multiple-response and matching types with respect to dichotomous and polytomous scoring, methods for determining the guess score are presented and illustrated with practical applications. The implica-tions for theory and practice are discussed

The use of Fe-30% Ni and Fe-30% Ni-Nb alloys as model systems for studying the microstructural evolution during the hot deformation of austenite

The development of physically-based models of microstructural evolution during thermomechanical processing of metallic materials requires knowledge of the internal state variable data, such as microstructure, texture, and dislocation substructure characteristics, over a range of processing conditions. This is a particular problem for steels, where transformation of the austenite to a variety of transformation products eradicates the hot deformed microstructure. This article reports on a model Fe-30wt% Ni-based alloy, which retains a stable austenitic structure at room temperature, and has, therefore, been used to model the development of austenite microstructure during hot deformation of conventional low carbon-manganese steels. It also provides an excellent model alloy system for microalloy additions. Evolution of the microstructure and crystallographic texture was characterized in detail using optical microscopy, X-ray diffraction (XRD), SEM, EBSD, and TEM. The dislocation substructure has been quantified as a function of crystallographic texture component for a variety of deformation conditions for the Fe-30% Ni-based alloy. An extension to this study, as the use of a microalloyed Fe-30% Ni-Nb alloy in which the strain induced precipitation mechanism was studied directly. The work has shown that precipitation can occur at a much finer scale and higher number density than hitherto considered, but that pipe diffusion leads to rapid coarsening. The implications of this for model development are discussed