Regressional Characterization of the Generalized Inverse Gaussian Population

A characterization of the generalized inverse Gaussian population is obtained from a condition of constant regression of a suitable statistic on a linear one.     

Goodness-of-Fit Tests for the Inverse Gaussian Distribution Based on the Empirical Laplace Transform

This paper considers two flexible classes of omnibus goodness-of-fit tests for the inverse Gaussian distribution. The test statistics are weighted integrals over the squared modulus of some measure of deviation of the empirical distribution of given data from the family of inverse Gaussian laws, expressed by means of the empirical Laplace transform. Both classes of statistics are connected to the first nonzero component of Neyman's smooth test for the inverse Gaussian distribution. The tests, when implemented via the parametric bootstrap, maintain a nominal level of significance very closely. A large-scale simulation study shows that the new tests compare favorably with classical goodness-of-fit tests for the inverse Gaussian distribution, based on the empirical distribution function.     

Representative Agent Pricing of Financial Assets Based on L��vy Processes with Normal Inverse Gaussian Marginals

The aim of the paper is to test the assumption of normal inverse Gaussian returns from speculative investments. We construct an asset pricing model where price processes are pure jump processes having associated returns with marginal distributions of this particular type. The resulting model is not complete, and we employ a partial equilibrium framework with a representative agent. The model is confronted with some stylized facts, like the equity premium puzzle, and the results seem promising.     

Asymptotics and bootstrap for inverse Gaussian regression

This paper studies regression, where the reciprocal of the mean of a dependent variable is considered to be a linear function of the regressor variables, and the observations on the dependent variable are assumed to have an inverse Gaussian distribution. The large sample theory for the pseudo maximum likelihood estimators is available in the literature, only when the number of replications increase at a fixed rate. This is inadequate for many practical applications. This paper establishes consistency and derives the asymptotic distribution for the pseudo maximum likelihood estimators under very general conditions on the design points. This includes the case where the number of replications do not grow large, as well as the one where there are no replications. The bootstrap procedure for inference on the regression parameters is also investigated.Research supported in part by NSF Grant DMS-9208066.Research supported in part by NSERC of Canada.     

The S-Matrix Structure and the Inverse Scattering Transform in the J-Matrix Approach

Using the analytic properties of the S-matrix, we obtain a system of inverse scattering transform equations for nonlocal potentials with Laguerre form factors. Coulomb repulsion can be present in the system.     

Inverse Operators, q-Fractional Integrals, and q-Bernoulli Polynomials

We introduce operators of q-fractional integration through inverses of the Askey–Wilson operator and use them to introduce a q-fractional calculus. We establish the semigroup property for fractional integrals and fractional derivatives. We study properties of the kernel of q-fractional integral and show how they give rise to a q-analogue of Bernoulli polynomials, which are now polynomials of two variables, x and y. As q→1 the polynomials become polynomials in x−y, a convolution kernel in one variable. We also evaluate explicitly a related kernel of a right inverse of the Askey–Wilson operator on an L² space weighted by the weight function of the Askey–Wilson polynomials.     

Self-intersection local time for Gaussian ′( )-processes: Existence, path continuity and examples

In this paper we develop a criterion for existence or non-existence of self-intersection local time (SILT) for a wide class of Gaussian ′( ^d)-valued processes, we show that quite generally the SILT process has continuous paths, and we give several examples which illustrate existence of SILT for different ranges of dimensions (e.g., d ≤ 3, d ≤ 7 and 5 ≤ d ≤ 11 in the Brownian case). Some of the examples involve branching and exhibit “dimension gaps”. Our results generalize the work of Adler and coauthors, who studied the special case of “density processes” and proved that SILT paths are cadlag in the Brownian case making use of a “particle picture” approximation (this technique is not available for our general formulation).     

Inverse Spectra with Two and Three Maps

It is shown that, for any 1 n < , there exist four maps of the n-dimensional cube to itself such that the limit of any inverse sequence of n-cubes is the limit of some sequence with only these four bonding maps. A universal continuum in the class of all limits of sequences of n-cubes is constructed as the limit of an inverse sequence of n-cubes with one bonding map. All compact sets of trivial shape are represented by using only three maps of the Hilbert cube to itself. Two maps of the closed interval to itself such that any Knaster continuum can be obtained as the limit of an inverse sequence with only these two bonding maps are constructed.     

Moduli of continuity of a class of N-parameter Gaussian processes and their fast points

We study the moduli of continuity of a class of N-parameter Gaussian processes and get some results on＇the packing dimension of the set of their fast points.     

基于偏正态分布联合位置、尺度与偏度模型的极大似然估计

本文提出了基于偏正态分布联合位置、尺度与偏度模型,通过极大似然迭代算法给出了联合模型参数的估计方法,最后通过随机模拟和实例研究说明了提出的模型与方法的有效性。     

Conditional Iterative Proportional Fitting for Gaussian Distributions

A Gaussian version of the iterative proportional fitting procedure (IFP-P) was applied by Speed and Kiiveri to solve the likelihood equations in graphical Gaussian models. The calculation of the maximum likelihood estimates can be seen as the problem to find a Gaussian distribution with prescribed Gaussian marginals. We extend the Gaussian version of the IPF-P so that additionally given conditionals of Gaussian type are taken into account. The convergence of both proposed procedures, called conditional iterative proportional fitting procedures (CIPF-P), is proved.     

Log-det approximation based on uniformly distributed seeds and its application to Gaussian process regression

Maximum likelihood estimation (MLE) of hyperparameters in Gaussian process regression as well as other computational models usually and frequently requires the evaluation of the logarithm of the determinant of a positive-definite matrix (denoted by C hereafter). In general, the exact computation of is of O(N³) operations where N is the matrix dimension. The approximation of could be developed with O(N²) operations based on power-series expansion and randomized trace estimator. In this paper, the accuracy and effectiveness of using uniformly distributed seeds for approximation are investigated. The research shows that uniform-seed based approximation is an equally good alternative to Gaussian-seed based approximation, having slightly better approximation accuracy and smaller variance. Gaussian process regression examples also substantiate the effectiveness of such a uniform-seed based log-det approximation scheme.     

Asymptotic properties of some goodness-of-fit tests based on the L 1-norm

Some goodness-of-fit tests based on the L ₁-norm are considered. The asymptotic distribution of each statistic under the null hypothesis is the distribution of the L ₁-norm of the standard Wiener process on [0,1]. The distribution function, the density function and a table of some percentage points of the distribution are given. A result for the asymptotic tail probability of the L ₁-norm of a Gaussian process is also obtained. The result is useful for giving the approximate Bahadur efficiency of the test statistics whose asymptotic distributions are represented as the L ₁-norms of Gaussian processes.     

Normalizing transformations of some statistics of Gaussian ARMA processes

In this paper, the authors investigate Edgeworth type expansions of certain transformations of some statistics of Gaussian ARMA processes. They also investigate transformations which will make the second order part of the Edgeworth expansions vanish. Some numerical studies are made and they show that the above transformations give better approximations than the usual approximation.This work is supported by Contract N00014-K-0292 of the Office of Naval Research and Contract F49620-85-C-0008 of the Air Force Office of Scientific Research. The United States Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright notation hereon.The work of this author was done at the Center for Multivariate Analysis. His permanent address is Department of Mathematics, Hiroshima University, Naka-ku, Hiroshima 730, Japan.     

The rate of convergence for spectra of GUE and LUE matrix ensembles

We obtain optimal bounds of order O(n ⁻¹) for the rate of convergence to the semicircle law and to the Marchenko-Pastur law for the expected spectral distribution functions of random matrices from the GUE and LUE, respectively. Research supported by the DFG-Forschergruppe FOR 399/1. Partially supported by INTAS grant N 03-51-5018, by RFBR grant N 02-01-00233, and by RFBR-DFG grant N 04-01-04000.     

Convexity of parametric Bézier surfaces in terms of Gaussian curvature signatures

The shape of a smooth surfaceS in ℝ³ is governed by its Gaussian curvatureK. In this paper, the notion ofGC signature of a smooth surfaceS is introduced. This scalar-valued functionf has the same sign asK and becomes a polynomial ifS is a parametric polynomial surface. Hence, the study of convexity of such surfacesS (or nonparametric polynomialB) reduces to the study of positivity of the corresponding polynomialsf. Efficient computational schemes are developed for expressing the Bézier coefficients of the Bernstein-Bézier formulation off in terms of those of the parametric polynomialb (orB) that definesS. This approach differs from the usual consideration of positive definiteness of the Hessian matrix corresponding to the parametric polynomialb (orB). Research supported by NSF Grant DMS-92-06928 and ARO Contract DAAH 04-93-G-0047.     

A Note on the Use of V and U Statistics in Nonparametric Models of Regression

We establish the asymptotic equivalence of V and U statistics when the statistic’s kernel depends on n. Combined with a lemma of B. Lee this result provides conditions under which U statistics projections and V statistics are asymptotically equivalent. The use of this equivalence in nonparametric regression models is illustrated with several examples; the estimation of conditional variances, skewness, kurtosis and the construction of a nonparametric R-squared measure.     

On decoupling,series expansions,and tail behavior of chaos processes

Some important aspects of chaos random variables such as decoupling, an almost sure representation (a Karhunen-Loeve expansion) and integrability are discussed here, the first being a tool for, and the third as a consequence of, the second. The main goal in this note is to learn about the structure of the limit laws ofU-processes.Research partially supported by National Science Foundation Grant No. DMS-9000132 and University of Connecticut Grant No. G12-913501.     

Moment-Based Approximations of Distributions Using Mixtures: Theory and Applications

There are a number of cases where the moments of a distribution are easily obtained, but theoretical distributions are not available in closed form. This paper shows how to use moment methods to approximate a theoretical univariate distribution with mixtures of known distributions. The methods are illustrated with gamma mixtures. It is shown that for a certain class of mixture distributions, which include the normal and gamma mixture families, one can solve for a p-point mixing distribution such that the corresponding mixture has exactly the same first 2p moments as the targeted univariate distribution. The gamma mixture approximation to the distribution of a positive weighted sums of independent central ² variables is demonstrated and compared with a number of existing approximations. The numerical results show that the new approximation is generally superior to these alternatives.     

On nonparametric tests for symmetry inRm

This paper considers the problem for testing symmetry of a distribution inR ^m based on the empirical distribution function. Limit theorems which play important roles for investigating asymptotic behavior of such tests are obtained. The limit processes of the theorems are multiparameter Wiener process. Based on the limit theorems, nonparametric tests are proposed whose asymptotic distributions are functionals of a multiparameter standard Wiener process. The tests are compared asymptotically with each other in the sense of Bahadur.