On the normality a posteriori for exponential distributions,using the Bayesian estimation

Summary The Bayesian estimation problem for the parameter θ of an exponential probability distribution is considered, when it is assumed that θ has a natural conjugate prior density and a loss-function depending on the squared error is used. It is shown that, with probability one, the posterior density of the Bayesian—centered and scaled parameter converges pointwise to the normal probability density. The weak convergence of the posterior distributions to the normal distribution follows directly. Both correct and incorrect models are studied and the asymptotic normality is stated respectively.     

Note on the estimation of crossing intensity for Laplace moving average

In this note upcrossing intensity of a level u by a non-Gaussian process—the Laplace Moving Average (LMA)—is estimated by means of a saddle point approximation of Rice’s formula. A LMA-process is defined by power spectral density, skewness and kurtosis parameters. The method is illustrated by measurements of sea surface elevation and stresses in a vessel.     

A preliminary test procedure for the scale parameter of exponential distribution when the selection parameter is unknown

Summary A preliminary test estimator is considered for the scale parameter of the two-parameter exponential distribution with unknown selection parameter, where the distribution does not satisfy the regularity condition of Wilks' theorem—the density is not differentiable. A method of specifying the level of significance of the preliminary test based on is proposed AIC. This work was partly supported by Scientific Research Fund No. 58450058 from the Ministry of Education of Japan. The Institute of Statistical Mathematics     

On the Two-Dimensional Navier—Stokes Equations with the Free Boundary Condition

In this article we consider the two-dimensional Navier—Stokes equations with free boundary condition (open surface), and derive a number of different results: a new orthogonal property for the nonlinear term, improved a priori estimates on the solution, an upper bound on the dimension of the attractor which agrees with the conventional theory of turbulence; finally, for elongated rectangular domains, an improved Lieb—Thirring (collective Sobolev) inequality leads to an upper bound on the dimension of the attractor which might be optimal. Accepted 11 July 1996     

Comparison of two kinds of Hankel type operators

We compare two kinds of Hankel type operators, the “BJP-operators” introduced by Boman—Janson—Peetre and the “Z-operators” considered by Zhang. We show that every BJP-operator comes as an orthogonal sum ofZ-operators.     

The implicit application of a rational filter in the RKS method

The implicitly restarted Arnoldi method implicitly applies a polynomial filter to the Arnoldi vectors by use of orthogonal transformations. In this paper, an implicit filtering by rational functions is proposed for the rational Krylov method. This filtering is performed in an efficient way. Two applications are considered. The first one is the filtering of unwanted eigenvalues using exact shifts. This approach is related to the use of exact shifts in the implicitly restarted Arnoldi method. Second, eigenvalue problems can have an infinite eigenvalue without physical relevance. This infinite eigenvalue can corrupt the eigensolution. An implicit filtering is proposed for avoiding such corruptions. The work of Gorik De Samblanx and Adhemar Bultheel was supported by the National Fund for Scientific Research (NFWO), project Lanczos, grant #2.0042.93 and by the Human Capital and Mobility project ROLLS of the European Community under contract ERBCHRXCT930416. The research by Karl Meerbergen was supported by the Belgian programme on Interuniversity Poles of Attraction (IUAP 17), initiated by the Belgian State—Prime Minister's Service—Federal Office for Scientific, Technical and Cultural Affairs and the project Iterative Methods in Scientific Computing, contract number HCM network CHRCCT93-0420, coordinated by CERFACS, Toulouse, France.     

Minimum-Variance Multitaper Spectral Estimation on the Sphere

We develop a method to estimate the power spectrum of a stochastic process on the sphere from data of limited geographical coverage. Our approach can be interpreted either as estimating the global power spectrum of a stationary process when only a portion of the data are available for analysis, or estimating the power spectrum from local data under the assumption that the data are locally stationary in a specified region. Restricting a global function to a spatial subdomain—whether by necessity or by design—is a windowing operation, and an equation like a convolution in the spectral domain relates the expected value of the windowed power spectrum to the underlying global power spectrum and the known power spectrum of the localization window. The best windows for the purpose of localized spectral analysis have their energy concentrated in the region of interest while possessing the smallest effective bandwidth as possible. Solving an optimization problem in the sense of Slepian (1960) yields a family of orthogonal windows of diminishing spatiospectral localization, the best concentrated of which we propose to use to form a weighted multitaper spectrum estimate in the sense of Thomson (1982). Such an estimate is both more representative of the target region and reduces the estimation variance when compared to estimates formed by any single bandlimited window. We describe how the weights applied to the individual spectral estimates in forming the multitaper estimate can be chosen such that the variance of the estimate is minimized.     

Some systems of multivariable orthogonal q-Racah polynomials

In 1991 Tratnik derived two systems of multivariable orthogonal Racah polynomials and considered their limit cases. q-Extensions of these systems are derived, yielding systems of multivariable orthogonal q-Racah polynomials, from which systems of multivariable orthogonal q-Hahn, dual q-Hahn, q-Krawtchouk, q-Meixner, and q-Charlier polynomials follow as special or limit cases. Dedicated to Richard Askey on the occasion of his 70th birthday. 2000 Mathematics Subject Classification Primary—33D50; Secondary—33C50 Supported in part by NSERC grant #A6197.     

A numerically stable least squares solution to the quadratic programming problem

The strictly convex quadratic programming problem is transformed to the least distance problem — finding the solution of minimum norm to the system of linear inequalities. This problem is equivalent to the linear least squares problem on the positive orthant. It is solved using orthogonal transformations, which are memorized as products. Like in the revised simplex method, an auxiliary matrix is used for computations. Compared to the modified-simplex type methods, the presented dual algorithm QPLS requires less storage and solves ill-conditioned problems more precisely. The algorithm is illustrated by some difficult problems.      

Real Estate Appraisal: A Double Perspective Data Envelopment Analysis Approach

This paper proposes a new methodology for the assessment of the value range for real estate units. The theoretical basis of the methodology is built on the Data Envelopment Analysis—DEA approach, which has its original concept adapted to the case where the units under assessment consist of transactions among sellers and buyers. The proposed approach—christened Double Perspective-Data Envelopment Analysis (DP-DEA)—is applied to a database comprising the prices and features of the units under assessment. It is shown that the DP-DEA presents some specific advantages when compared to the usual regression analysis method employed in real estate value assessment.     

On the cutting stock problem under stochastic demand

This paper addresses the one-dimensional cutting stock problem when demand is a random variable. The problem is formulated as a two-stage stochastic nonlinear program with recourse. The first stage decision variables are the number of objects to be cut according to a cutting pattern. The second stage decision variables are the number of holding or backordering items due to the decisions made in the first stage. The problem’s objective is to minimize the total expected cost incurred in both stages, due to waste and holding or backordering penalties. A Simplex-based method with column generation is proposed for solving a linear relaxation of the resulting optimization problem. The proposed method is evaluated by using two well-known measures of uncertainty effects in stochastic programming: the value of stochastic solution—VSS—and the expected value of perfect information—EVPI. The optimal two-stage solution is shown to be more effective than the alternative wait-and-see and expected value approaches, even under small variations in the parameters of the problem.     

Mode estimation for discrete distributions

The problem of estimating the mode of a discrete distribution is considered. New characterizations of discrete unimodal and multi-modal distributions are obtained. The proposed mode estimator is essentially the sample mode, modulo appropriate modifications when the sample mode is not well defined. In the case of i.i.d. observations coming from a unimodal discrete distribution, our proposed mode estimator is shown to possess a number of strong asymptotic properties. Many of these results extend to the case of multi-modal discrete distributions as well. Our method also applies — and we have similar asymptotic results — to the problem of mode estimation based on finitely many observations on a Markov chain whose equilibrium distribution is the underlying unimodal distribution. For unimodal discrete distributions, we also propose a consistent large sample test of mode based on the proposed statistic. Applications of mode estimation problem in Monte-Carlo optimization problem using the Hastings Metropolis chain and in prediction problem using binary response variable, specially in the context of dose-response experiments, are also illustrated.     

Adaptation on the space of finite signed measures

Given an i.i.d. sample from a probability measure P on ℝ, an estimator is constructed that efficiently estimates P in the bounded-Lipschitz metric for weak convergence of probability measures, and, at the same time, estimates the density of P — if it exists (but without assuming it does) — at the best possible rate of convergence in total variation loss (that is, in L ¹-loss for densities).      

On the conditional covariance condition in the martingale CLT

The paper develops a method allowing one to figure out how a convergence rate in the martingale central limit theorem depends on the conditional covariance structure of the martingale. The method is based on constructing “stopping projections” that control the behavior of the conditional covariances of martingale differences. A discrete time martingale taking values in either finite or infinite dimensional Hilbert space is considered. Research supported by the International Science Foundation grant LI000. Vilnius University, Naugarduko 24, 2006, Vilnius, Lithuania. Published in Lietuvos Matematikos Rinkinys, Vol. 35, No. 1, pp. 118–131, January—March, 1995.     

Coreduction Homology Algorithm for Regular CW-Complexes

In this paper we present a new algorithm for computing the homology of regular CW-complexes. This algorithm is based on the coreduction algorithm due to Mrozek and Batko and consists essentially of a geometric preprocessing algorithm for the standard chain complex generated by a CW-complex. By employing the concept of S-complexes the original chain complex can—in all known practical cases—be reduced to a significantly smaller S-complex with isomorphic homology, which can then be computed using standard methods. Furthermore, we demonstrate that in the context of non-uniform cubical grids this method significantly improves currently available algorithms based on uniform cubical grids.     

Weak Convergence of Varying Measures and Hermite—Padé Orthogonal Polynomials

It is known that the common denominator of the Hermite—Padé approximants of a mixed Angelesco—Nikishin system shares orthogonality relations with respect to each function in the system. It is less well known that they also satisfy full orthogonality with respect to a varying measure. This problem motivates our interest in extending the class of varying measures with respect to which weak asymptotics of orthogonal polynomials takes place. In particular, for the case of a Nikishin system, we prove weak asymptotics of the corresponding varying measures. October 23, 1997. Date revised: September 23, 1998. Date accepted: November 10, 1998.     

A fast matrix–vector multiplication method for solving the radiosity equation

A “fast matrix–vector multiplication method” is proposed for iteratively solving discretizations of the radiosity equation (I — К)u = E. The method is illustrated by applying it to a discretization based on the centroid collocation method. A convergence analysis is given for this discretization, yielding a discretized linear system (I — K _n )u _n = E _n. The main contribution of the paper is the presentation of a fast method for evaluating multiplications K_n v, avoiding the need to evaluate K_n explicitly and using fewer than O(n ²) operations. A detailed numerical example concludes the paper, and it illustrates that there is a large speedup when compared to a direct approach to discretization and solution of the radiosity equation. The paper is restricted to the surface S being unoccluded, a restriction to be removed in a later paper. This revised version was published online in June 2006 with corrections to the Cover Date.     

On the reversed sub-martingale property of empirical discrepancies in arbitrary sample spaces

The empirical discrepancy is defined as a supremum over a class of functions of a collection of centered sample averages. For uncountable classes the discrepancy need not be measurable, and distributional assertions can become dependent on the structure of the underlying probability space. This paper shows that one such assertion—the reversed sub-martingale property—is valid when interpreted in terms of measurable cover functions for the canonical model, but that it can fail in other constructions of the underlying model.     

The Moment Problem Associated with the q -Laguerre Polynomials

   Abstract. We consider the indeterminate Stieltjes moment problem associated with the q -Laguerre polynomials. A transformation of the set of solutions, which has all the classical solutions as fixed points, is established and we present a method to construct, for instance, continuous singular solutions. The connection with the moment problem associated with the Stieltjes—Wigert polynomials is studied; we show how to come from q -Laguerre solutions to Stieltjes—Wigert solutions by letting the parameter α —> ∞ , and we explain how to lift a Stieltjes—Wigert solution to a q -Laguerre solution at the level of Pick functions. Based on two generating functions, expressions for the four entire functions from the Nevanlinna parametrization are obtained.     

A note on slant submanifolds of nearly trans-Sasakian manifolds

The geometry of slant submanifolds of a nearly trans-Sasakian manifold is studied when the tensor field Q is parallel. It is proved that Q is not parallel on the submanifold unless it is anti-invariant and thus the result of [CABRERIZO, J. L.—CARRIAZO, A.—FERNANDEZ, L. M.—FERNANDEZ, M.: Slant submanifolds in Sasakian manifolds, Glasg. Math. J. 42 (2000), 125–138] and [GUPTA, R. S.—KHURSHEED HAIDER, S. M.—SHARFUDIN, A.: Slant submanifolds of a trans-Sasakian manifold, Bull. Math. Soc. Sci. Math. Roumanie (N.S.) 47 (2004), 45–57] are generalized.