BIVARIATE INTERPOLATING POLYNOMIALS AND SPLINES (I)

The multivariate splines which were first presented by deBooor as a complete theoretical system have intrigued many mathematicians who have devoted many works in this field which is still in the process of development.The author of this paper is interested in the area of inter- polation with special emphasis on the interpolation methods and their approximation orders. But such B-splines(both univariate and multivariate)do not interpolated directly,so I ap- proached this problem in another way which is to extend my interpolating spline of degree 2n-1 in univariate case(See[7])to multivariate case.I selected triangulated region which is inspired by other mathematicians'works(e.g.[2]and[3])and extend the interpolating polynomials from univariate to m-variate case(See[10])In this paper some results in the case m=2 are discussed and proved in more concrete details.Based on these polynomials,the interpolating splines(it is defined by me as piecewise polynomials in which the unknown par- tial derivatives are determined under certain continuous conditions)are also discussed.The approximation orders of interpolating polynomials and of cubic interpolating splines are inverstigated.We lunited our discussion on the rectangular domain which is partitioned into equal right triangles.As to the case in which the rectangular domain is partitioned into unequal right triangles as well as the case of more complicated domains,we will discuss in the next pa- per.

SPLINE INTEGRAL OPERATORS EXACT ON POLYNOMIALS

We study integral spline operators of order k. exact on polynomials of degree 2m. with 0≤2m<k, having the form T_(k,t)^((m))f=∑ i∈J [∫_lf(x)C_(l,k)^(x)dx]N_IK, where {N_(l,k),i∈J} is the classical Bspline basis associated with the sequence t of knots on the interval I and C_(l,k)~is a linear combination of B-splines {N_(l+l,k),-m≤j≤m}. We prove a general theorem of eristence and uniqueness. Then we study the L^D -norms of these operators and error bounds for smooth furlctions f. We then obtain partial results about the L~∞--boundedness of T_(k,t)^((m)), independently of the pertition t. We also give the complete description of these operators in the case of a uniform partition of the real line.

Real option pricing method for R&D investment under changing risk-free rate and discount rate

The polynomial spline model, which belongs to the static term structure model of interest rates, is studied. Every cash flow of the project is discounted relatively accurately by obtaining the discount rate from the static term structure model of interest rates. A simple basic model, which belongs to the dynamic term structure model, is studied, and the option pricing formula under changing risk-free rates is obtained by bringing it into the option pricing formula. Both dynamic and static term structure models are estimated by the use of the data of buy-back rates and the Shanghai Stock Exchange, and an example is given to compare the differences between the traditional method and the method under the changes in the interest rates and the discount rates.

Composite Quantile Estimation in Partial Functional Linear Regression Model Based on Polynomial Spline

In this paper,we consider composite quantile regression for partial functional linear regression model with polynomial spline approximation.Under some mild conditions,the convergence rates of the estimators and mean squared prediction error,and asymptotic normality of parameter vector are obtained.Simulation studies demonstrate that the proposed new estimation method is robust and works much better than the least-squares based method when there are outliers in the dataset or the random error follows heavy-tailed distributions.Finally,we apply the proposed methodology to a spectroscopic data sets to illustrate its usefulness in practice.

Smooth interpolation on homogeneous matrix groups for computer animation

Homogeneous matrices are widely used to represent geometric transformations in computer graphics, with interpo- lation between those matrices being of high interest for computer animation. Many approaches have been proposed to address this problem, including computing matrix curves from curves in Euclidean space by registration, representing one-parameter curves on manifold by rational representations, changing subdivisional methods generating curves in Euclidean space to corresponding methods working for matrix curve generation, and variational methods. In this paper, we propose a scheme to generate rational one-parameter matrix curves based on exponential map for interpolation, and demonstrate how to obtain higher smoothness from existing curves. We also give an iterative technique for rapid computing of these curves. We take the computation as solving an ordinary differential equation on manifold numerically by a generalized Euler method. Furthermore, we give this algorithm’s bound of the error and prove that the bound is proportional to the shift length when the shift length is sufficiently small. Compared to direct computation of the matrix functions, our Euler solution is faster.

Some extremal properties of multivariate polynomial splines in the metric L_p(R^d)

We constructed a kind of continuous multivariate spline operators as the approximation tools of the multivariate functions on the Rd instead of the usual multivariate cardinal interpolation oper-ators of splines, and obtained the approximation error by this kind of spline operators. Meantime, by the results, we also obtained that the spaces of multivariate polynomial splines are weakly asymptoti-cally optimal for the Kolmogorov widths and the linear widths of some anlsotropic Sobolev classes of smooth functions on Rd in the metric Lp(Rd).

A survey on the local refinable splines

This paper provides a survey of local refinable splines,including hierarchical B-splines,T-splines,polynomial splines over T-meshes,etc.,with a view to applications in geometric modeling and iso-geometric analysis.We will identify the strengths and weaknesses of these methods and also offer suggestions for their using in geometric modeling and iso-geometric analysis.

EFFICIENT ESTIMATION OF SEEMINGLY UNRELATED ADDITIVE NONPARAMETRIC REGRESSION MODELS

This paper is concerned with the estimating problem of seemingly unrelated(SU)nonparametric additive regression models.A polynomial spline based two-stage efficient approach is proposed to estimate the nonparametric components,which takes both of the additive structure and correlation between equations into account.The asymptotic normality of the derived estimators are established.The authors also show they own some advantages,including they are asymptotically more efficient than those based on only the individual regression equation and have an oracle property,which is the asymptotic distribution of each additive component is the same as it would be if the other components were known with certainty.Some simulation studies are conducted to illustrate the finite sample performance of the proposed procedure.Applying the proposed procedure to a real data set is also made.

FUNCTIONAL COEFFICIENT AUTOREGRESSIVE CONDITIONAL ROOT MODEL

This paper proposes an extended model based on ACR nmdel： Functional coefficient autoregressive conditional root model （FCACR）. Under some assumptions, the authors show that the process is geometrically ergodic, stationary and all moments of the process exist. The authors use the polynomial spline function to approximate the functional coefficient, and show that the estimate is consistent with the rate of convergence Op（hv＋1 ＋ n-1/3）. By simulation study, the authors discover the proposed method can approximate well the real model. Furthermore, the authors apply the model to real exchange rate data analysis.