Reparameterization Method Research Articles

Reparameterization of single difference and undifferenced kinematic GPS positioning models

A series of advantages of single difference (SD) and undifferenced (ZD) models are given as compared with the double difference (DD) model. However, rank defects exist in SD and ZD models. The reparameterization method is provided to resolve this rank defect problem by estimating some combinations of the unknowns rather than the unknowns themselves. The reparameterization of SD and ZD functional models is discussed in detail with their stochastic models. The theoretical confirmation of the equivalence of undifferenced and differenced models is described in a straightforward way. The relationship between SD and ZD residuals is given and verified for some special purposes, e. g. research on the stochastical properties of GPS observations.

Reparameterization of piecewise rational Bezier curves and its applications

. Although the curve segments are C1 continuous in three dimensions, they may be C0 continuous in four dimensions. In this case, the multiplicity of each interior knot cannot be reduced and the B-spline basis function becomes C0 continuous. Using a surface generation method, such as skinning these kinds of rational B-spline curves to construct an interpolatory surface, may generate surfaces with C0 continuity. This paper presents a reparameterization method for reducing the multiplicity of each interior knot to make the curve segments C1 continuous in four dimensions. The reparameterized rational B-spline curve has the same shape and degree as before and also has a standard form. Some applications in skinned surface and ruled surface generation based on the reparameterized curves are shown.

Maximum likelihood estimation for a nearly random walk model

This paper proposes an effective reparameterization method for the maximum likelihood estimation of a nearly random walk ARIMA (1,1,1) model, an important case where standard method of locating the MLE is not satisfactory. This model is equivalent to the permanent and temporary components model that Fama &French (1988) and others used to capture the slow mean reversion behavior of stock prices. The reparameterization method we prppose for estimating the nearly cancelled AR and MA parameters performs satisfactorily. The exact likelihood function based on the transformed parameters is studied. We argue that the region of interest will get magnified and emphasized in the transformed space, thus making the search for MLE more thorough and effective. Substantiai simuiation evidences are provided to demonstrate the effectiveness of the method. The sample size requirement is critical and is discussed in details. For application, this method is applied to estimate a nearly random walk ARIMA (1,1,1) model for NYSE/AMEX value-weighted market return in daily and longer holding-period horizons.