Studies on the Preparation of Polymer Spherical Symmetry GRIN Sphere and Controlling its Gradient Index Distribution

In this paper, a polymer spherical symmetry GRIN sphere lens were prepared by the suspension-diffusion-copolymerization(SDC) technique, selecting methyl methacrylate(MMA) as monomer M1 and acrylic 2,2,2-trifluoroethyl ester(3FEA) as M2. The radial distribution of refractive index of the lens was measured by the shearing interference method, which demonstrated that the quadratic refractive-index distribution was formed in the sphere lens, and its Δn=0.019.

THE PLANCHEREL FORMULA FOR THE LINE BUNDLES ON SL(n + 1, R)/S(GL(1, R) × GL(n, R))

In this paper we obtain the Plancherel formula for the spaces of L2-sections of the line bundles over the pseudo-Riemannian symmetric space G/H where G=SL（n ＋ 1, R） and H=S（GL（1, R）×GL（n, R））. The Plancherel formula is given in an explicit form by means of spherical distributions associated with the character χλ of the subgroup H. We follow the method of Faraut, Kosters and van Dijk.

TESTING INEQUALITY CONSTRAINTS IN A LINEAR REGRESSION MODEL WITH SPHERICALLY SYMMETRIC DISTURBANCES

This paper develops a Wald statistic for testing the validity of multivariate inequality constraints in linear regression models with spherically symmetric disturbances,and derive the distributions of the test statistic under null and nonnull hypotheses.The power of the test is then discussed.Numerical evaluations are also carried out to examine the power performances of the test for the case in which errors follow a multivariate student-t(Mt) distribution.

Spherical Logistic Distribution

Some of the spherical distributions can be constructed through proper transformation of the densities on plane.Since the logistic density on the Euclidean space has similar behavior to the normal distribution,it is of interest to extend it for spherical data.In this paper,we introduce spherical logistic distribution on the unit sphere and then study relevant statistical inferences including parameters estimation through method of moments and maximum likelihood techniques.It is shown that the spherical logistic distribution is a multimodal distribution with the marginal logistic density function.Proposed density has rotational symmetry property and this plays a key role to drive some important results related to first two moments.To investigate the proposed density in more details,some simulation studies along with analyzing real-life data are also considered.

Generalized T3-plot for testing high-dimensional normality

A new dimension-reduction graphical method for testing high- dimensional normality is developed by using the theory of spherical distributions and the idea of principal component analysis. The dimension reduction is realized by projecting high-dimensional data onto some selected eigenvector directions. The asymptotic statistical independence of the plotting functions on the selected eigenvector directions provides the principle for the new plot. A departure from multivariate normality of the raw data could be captured by at least one plot on the selected eigenvector direction. Acceptance regions associated with the plots are provided to enhance interpretability of the plots. Monte Carlo studies and an illustrative example show that the proposed graphical method has competitive power performance and improves the existing graphical method significantly in testing high-dimensional normality.