Nonparametric estimation of hazard functions and their derivatives under truncation model

Nonparametric kernel estimators for hazard functions and their derivatives are considered under the random left truncation model. The estimator is of the form of sum of identically distributed but dependent random variables. Exact and asymptotic expressions for the biases and variances of the estimators are derived. Mean square consistency and local asymptotic normality of the estimators are established. Adaptive local bandwidths are obtained by estimating the optimal bandwidths consistently.Research supported by Air Force Grant AFOSR 89-0386. Part of the work of Ülkü Gürler was done while she was a Ph.D. student at the Department of Statistics, the Wharton School of the University of Pennsylvania.     

On the convergence of kernel estimators of probability density functions

Summary The properties of the characteristic function of the fixed-bandwidth kernel estimator of a probability density function are used to derive estimates of the rate of almost sure convergence of such estimators in a family of Hilbert spaces. The convergence of these estimators in a reproducing-kernel Hilbert space is used to prove the uniform convergence of variable-bandwidth estimators. An algorithm employing the fast Fourier transform and heuristic estimates of the optimal bandwidth is presented, and numerical experiments using four density functions are described. This research was supported by the United States Air Force, Air Force Office of Scientific Research, Under Grant Number AFOSR-76-2711.     

Adaptive Lanczos methods for recursive condition estimation

Estimates for the condition number of a matrix are useful in many areas of scientific computing, including: recursive least squares computations, optimization, eigenanalysis, and general nonlinear problems solved by linearization techniques where matrix modification techniques are used. The purpose of this paper is to propose anadaptiveLanczosestimator scheme, which we callale, for tracking the condition number of the modified matrix over time. Applications to recursive least squares (RLS) computations using the covariance method with sliding data windows are considered.ale is fast for relatively smalln-parameter problems arising in RLS methods in control and signal processing, and is adaptive over time, i.e., estimates at timet are used to produce estimates at timet+1. Comparisons are made with other adaptive and non-adaptive condition estimators for recursive least squares problems. Numerical experiments are reported indicating thatale yields a very accurate recursive condition estimator.Research supported by the US Air Force under grant no. AFOSR-88-0285.Research supported by the US Army under grant no. DAAL03-90-G-105.Research supported by the US Air Force under grant no. AFOSR-88-0285.     

Error analysis of a pairwise summation algorithm to compute the sample variance

Summary We give an error analysis of an algorithm for computing the sample variance due to Chan, Golub, and LeVeque [The American Statistician 7 (1983), pp. 242–247]. It is shown that this algorithm is numerically stable. The algorithm computes the sample variance (and the sample mean) using just one pass through the sample data. It is amenable to pairwise summation and thus requires onlyO(logn) parallel steps.Research supported by the Air Force Office of Scientific Research under grant no. AFOSR-88-0161 and by the Office of Naval Research under grant no. N00024-85-C-6041     

Bilinear separation of two sets inn-space

The NP-complete problem of determining whether two disjoint point sets in then-dimensional real spaceR ⁿ can be separated by two planes is cast as a bilinear program, that is minimizing the scalar product of two linear functions on a polyhedral set. The bilinear program, which has a vertex solution, is processed by an iterative linear programming algorithm that terminates in a finite number of steps a point satisfying a necessary optimality condition or at a global minimum. Encouraging computational experience on a number of test problems is reported.This material is based on research supported by Air Force Office of Scientific Research grant AFOSR-89-0410, National Science Foundation grant CCR-9101801, and Air Force Laboratory Graduate Fellowship SSN 531-56-2969.     

Block truncated-Newton methods for parallel optimization

Truncated-Newton methods are a class of optimization methods suitable for large scale problems. At each iteration, a search direction is obtained by approximately solving the Newton equations using an iterative method. In this way, matrix costs and second-derivative calculations are avoided, hence removing the major drawbacks of Newton's method. In this form, the algorithms are well-suited for vectorization. Further improvements in performance are sought by using block iterative methods for computing the search direction. In particular, conjugate-gradient-type methods are considered. Computational experience on a hypercube computer is reported, indicating that on some problems the improvements in performance can be better than that attributable to parallelism alone.Partially supported by Air Force Office of Scientific Research grant AFOSR-85-0222.Partially supported by National Science Foundation grant ECS-8709795, co-funded by the U.S. Air Force Office of Scientific Research.     

High-order adaptive finite element-singly implicit Runge-Kutta methods for parabolic differential equations

We describe an adaptive mesh refinement finite element method-of-lines procedure for solving one-dimensional parabolic partial differential equations. Solutions are calculated using Galerkin's method with a piecewise hierarchical polynomial basis in space and singly implicit Runge-Kutta (SIRK) methods in time. A modified SIRK formulation eliminates a linear systems solution that is required by the traditional SIRK formulation and leads to a new reduced-order interpolation formula. Stability and temporal error estimation techniques allow acceptance of approximate solutions at intermediate stages, yielding increased efficiency when solving partial differential equations. A priori energy estimates of the local discretization error are obtained for a nonlinear scalar problem. A posteriori estimates of local spatial discretization errors, obtained by order variation, are used with the a priori error estimates to control the adaptive mesh refinement strategy. Computational results suggest convergence of the a posteriori error estimate to the exact discretization error and verify the utility of the adaptive technique.This research was partially supported by the U.S. Air Force Office of Scientific Research, Air Force Systems Command, USAF, under Grant Number AFOSR-90-0194; the U.S. Army Research Office under Contract Number DAAL 03-91-G-0215; by the National Science Foundation under Grant Number CDA-8805910; and by a grant from the Committee on Research, Tulane University.     

Interior proximal point algorithm for linear programs

An interior proximal point algorithm for finding a solution of a linear program is presented. The distinguishing feature of this algorithm is the addition of a quadratic proximal term to the linear objective function. This perturbation has allowed us to obtain solutions with better feasibility. Implementation of this algorithm shows that the algorithms. We also establish global convergence and local linear convergence of the algorithm.This research was supported by National Science Foundation Grants DCR-85-21228 and CCR-87-23091 and by Air Force Office of Scientific Research Grants AFOSR-86-0172 and AFOSR-89-0410. It was conducted while the author was a Graduate Student at the Computer Sciences Department, University of Wisconsin, Madison, Wisconsin.     

Geometric convergence of rational approximations toe −z in infinite sectors

Summary In this paper, we study the geometric convergence of rational approximations toe ^–z in infinite sectors symmetric about the positive real axis.Research supported in part by the Air Force Office of Scientific Research under Grant AFOSR-74-2688, and by the University of South Florida Research CouncilResearch supported in part by the Air Force Office of Scientific Research under Grant AFOSR-74-2729, and by the Atomic Energy Commission under Grant AT (11-1)-2075     

On the zeros and poles of Padé approximants toe z

Summary In this paper, we study the location of the zeros and poles of general Padé approximats toe ^z. The location of these zeros and poles is useful in the analysis of stability for related numerical methods for solving systems of ordinary differential equations.Research supported in part by the Air Force Office of Scientific Research under Grant AFOSR-74-2688, and by the University of South Fla. Research Council.Research supported in part by the Air Force Office of Scientific Research under Grant AFOSR-74-2729, and by the Atomic Energy Commission under Grant AT(11-1)-2075.     

On the spectra of sums of orthogonal projections with applications to parallel computing

Many parallel iterative algorithms for solving symmetric, positive definite problems proceed by solving in each iteration, a number of independent systems on subspaces. The convergence of such methods is determined by the spectrum of the sums of orthogonal projections on those subspaces, while the convergence of a related sequential method is determined by the spectrum of the product of complementary projections. We study spectral properties of sums of orthogonal projections and in the case of two projections, characterize the spectrum of the sum completely in terms of the spectrum of the product.This work was supported in part by the Norwegian Research Council for Science and the Humanities under grant D.01.08.054 and by The Royal Norwegian Council for Scientific and Industrial Research under grant IT2.28.28484; also supported in part by the Air Force Office of Scientific Research under grant AFOSR-86-0126 and by the National Science Foundation under grant DMS-8704169.     

Sequential estimation in regression models using analogues of trimmed means

A sequential procedure is proposed for constructing a fixed-size confidence region for the parameters of a linear regression model. The procedure is based on certain regression analogues of trimmed means, as formulated by Welsh (1987,Ann. Statist.,15, 20–36), rather than least squares estimates. For error distributions with continuous, symmetric density and some moment higher than fourth finite, if the design points of the model are bounded, then the procedure is both asymptotically consistent and asymptotically efficient as the size of the region approaches zero.Research supported in part by the National Science Foundation under Grants DMS 85-03321 and 88-02556 and by the Air Force under Grant AFOSR-87-0041.     

Computational complexity of parametric linear programming

We establish that in the worst case, the computational effort required for solving a parametric linear program is not bounded above by a polynomial in the size of the problem.This research is partially supported by Air Force Office of Scientific Research, Air Force Number AFOSR-78-3646.     

On the sharpness of theorems concerning zero-free regions for certain sequences of polynomials

Summary In this paper, we establish the sharpness of a theorem concerning zero-free parabolic regions for certain sequences of polynomials satisfying a three-term recurrence relation. Similarly, we establish the sharpness of a zero-free sectorial region for certain sequences of Padé approximants toe ^z .Research supported in part by the Air Force Office of Scientific Research under Grant AFOSR-74-2688Research supported in part by the Air Force Office of Scientific Research under Grant AFOSR-74-2729, and by the Energy Research and Development Administration (ERDA) under Grant E(11-1)-2075     

Expansions for statistics involving the mean absolute deviations

Expansion for the difference of mean absolute deviations from the sample mean and the population mean is derived. This result is used to obtain strong representations for mean absolute deviations from the sample mean and the sample median. Edgeworth expansions for some scale invariant statistics involving the mean absolute deviations are studied. These expansions are shown to be valid in spite of the presence of a lattice variable.Research supported in part by NSA Grant MDA904-90-H-1001.Research supported by the Air Force Office of Scientific Research under Grant AFOSR-89-0279.     

Finite perturbation of convex programs

This paper concerns a characterization of the finite-perturbation property of a convex program. When this property holds, finite perturbation of the objective function of a convex program leads to a solution of the original problem which minimizes the perturbation function over the set of solutions of the original problem. This generalizes a finite-termination property of the proximal point algorithm for linear programs and characterizes finite Tikhonov regularization of convex programs.This material is based on research supported by National Science Foundation Grants DCR-8521228 and CCR-8723091 and Air Force Office of Scientific Research Grants AFOSR-86-0172 and AFOSR-89-0410.     

Asymptotically distribution-free aligned rank order tests for composite hypotheses for general multivariate linear models

For general multivariate linear models, a composite hypothesis does not usually induce invariance of the joint distribution under appropriate groups of transformations, so that genuinely distribution-free tests do not usually exist. For this purpose, some aligned rank order statistics are incorporated in the proposal and study of a class of asymptotically distribution-free tests. Tests for the parallelism of several multiple regression surfaces are also considered. Finally the optimal properties of these tests are discussed.Work supported by the Air Force Office of Scientific Research, AFSC, USAF, Contract Nos: AFOSR-74-2736 and AFOSR-76-2927. Reproduction in whole or part is permitted for any purpose of the U.S. Government     

A least-squares-based method for a class of nonsmooth minimization problems with applications in plasticity

This paper introduces a globally convergent algorithm for solving a class of nonsmooth optimization problems, involving square roots of quadratic forms. The class includes in particular limit analysis problems in plasticity. The algorithm combines smoothing with successive approximation. The main computational effort in each iteration is solving a linear weighted least-squares problem. The convergence of the algorithm is proved and ana priori error estimate is obtained. Numerical results are presented for two limit analysis problems.The work of the first author was partially supported by NSF Grant DDM-89-96112. Parts of the work was done during his stay at the University of Bayreuth as a guest of the DFG. The work of the second author was supported in part by the Air Force Office of Scientific Research under contract AFOSR-88-0218 and by a National Science Foundation Grant ECS-8802239 at the University of Maryland, Baltimore County Campus.     

Optimal cost and policy for a Markovian replacement problem

We consider the computation of the optimal cost and policy associated with a two-dimensional Markov replacement problem with partial observations, for two special cases of observation quality. Relying on structural results available for the optimal policy associated with these two particular models, we show that, in both cases, the infinitehorizon, optimal discounted cost function is piecewise linear, and provide formulas for computing the cost and the policy. Several examples illustrate the usefulness of the results.This research was supported by the Air Force Office of Scientific Research Grant AFOSR-86-0029, by the National Science Foundation Grant ECS-86-17860, by the Advanced Technology Program of the State of Texas, and by the Air Force Office of Scientific Research (AFSC) Contract F49620-89-C-0044.     

Feedback exact null controllability for unbounded control problems in Hilbert space

Terminal constraint optimal control problems with unbounded control operators are considered. It is shown that the optimal solutions can be represented in a feedback form via a solution of an appropriate Riccati equation. In particular, it is proved that, for systems described by partial differential equations with infinite speed of propagation, boundary exact null controllability can be realized in feedback form.This work was partially supported by the National Science Foundation, Grant No. DMS-89-02811, and by the Air Force Office of Scientific Research, Grant No. AFOSR-89-0511 DEF.