Applications of Dirac's delta function in statistics

The Dirac delta function has been used successfully in mathematical physics for many years. The purpose of this article is to bring attention to several useful applications of this function in mathematical statistics. Some of these applications include a unified representation of the distribution of a function (or functions) of one or several random variables, which may be discrete or continuous, a proof of a well-known inequality, and a representation of a density function in terms of its noncentral moments.     

Value iteration and optimization of multiclass queueing networks

This paper considers in parallel the scheduling problem for multiclass queueing networks, and optimization of Markov decision processes. It is shown that the value iteration algorithm may perform poorly when the algorithm is not initialized properly. The most typical case where the initial value function is taken to be zero may be a particularly bad choice. In contrast, if the value iteration algorithm is initialized with a stochastic Lyapunov function, then the following hold: (i) a stochastic Lyapunov function exists for each intermediate policy, and hence each policy is regular (a strong stability condition), (ii) intermediate costs converge to the optimal cost, and (iii) any limiting policy is average cost optimal. It is argued that a natural choice for the initial value function is the value function for the associated deterministic control problem based upon a fluid model, or the approximate solution to Poisson’s equation obtained from the LP of Kumar and Meyn. Numerical studies show that either choice may lead to fast convergence to an optimal policy. This revised version was published online in June 2006 with corrections to the Cover Date.     

A unified formalism in mechanics

Summary A method is described by which the paths of mechanical systems, whether relativistic or non-relativistic, holonomic or non-holonomic, may be obtained from a single principle, the Principle of Stationary Curvature. The systems may include electric charges and fields.The formalism is invariant, irrespective of the type of system, for a form of contact transformation differing from that commonly used in mechanics.The generating function (metric) is initially a quadratic function of the coordinates. In the last two paragraphs the metric is generalized.     

Abstract algebra students’ evoked concept images for functions and homomorphisms

Homomorphism is a critical variety of function in undergraduate Abstract Algebra (AA) courses and function is one of the unifying concepts across many mathematical subject areas. However, despite homomorphism’s important place in the curriculum and its existence as a particular type of function, little is known of a student’s concept image of functions at advanced levels and the role this concept image may play in a students’ homomorphism-related activities. In this paper, we share cases that explore students’ concept image and treatment of functions at the undergraduate AA level. In particular, we focus on coherence of prior function understanding and functions in AA (homomorphisms), and how this coherence may account for student activity in tasks related to homomorphism. Our results reflect that even at the AA level, students may have limited concept images of functions and their understanding of function (and coherence with homomorphism) can serve as a support or obstacle to task performance in AA. We suggest that both instructors and researchers explicitly attend to the role of function and function understanding in student activity at advanced levels.     

Measuring the fuzziness of human thoughts: An application of fuzzy sets to sociological research

Conventionally, sociologists measure the membership of an individual to a group by a “0 or 1” characteristic function. But when the definition of that group is fuzzy and an individual is neither a full member nor a nonmember, this dichotomous characteristic function may distort the reality. Instead of the “0 or 1” characteristic function by classical set theory, fuzzy set theory introduces a membership function which is a gradation from 0 to 1 to measure the degree to which an object (an individual) belongs to a concept (a group). Based on the rationale of fuzzy set theory, we suggest some new methods of data collection and analysis. Among several noteworthy findings, two points are emphasized: 1) the fuzzy set is an appropriate way of measuring the fuzziness of human thought; and 2) it allows one to relax the conventional assumption that all individuals have identical distributions and deviations around their means.     

Logspline Density Estimation for Censored Data

Abstract

Logspline density estimation is developed for data that may be right censored, left censored, or interval censored. A fully automatic method, which involves the maximum likelihood method and may involve stepwise knot deletion and either the Akaike information criterion (AIC) or Bayesian information criterion (BIC), is used to determine the estimate. In solving the maximum likelihood equations, the Newton–Raphson method is augmented by occasional searches in the direction of steepest ascent. Also, a user interface based on S is described for obtaining estimates of the density function, distribution function, and quantile function and for generating a random sample from the fitted distribution.     

Harmonic analysis with a real frequency function—I. aperiodic case

An integral transform which converts a real spatial (or temporal) function into a real frequency function is introduced. The properties of this transform are investigated. It is concluded that this transform is parallel to the Fourier transform and may be applied to all fields in which the Fourier transform has been successfully applied.     

An algorithm for numerical differentiation of a function of one real variable

An Algol 60 procedure is described which will estimate the first, second or third order derivative of a function f(x) at a point x₀, using polynomial interpolation to values of f(x) at points on the real axis. This procedure does not require the user to provide information about the accuracy of the function values. The procedure itself monitors the noise level in these values, and allows for the effect of noise on the estimated derivative. The evaluation points may be restricted to one side of x₀ if f(x) makes this necessary, and are chosen either to minimise the error in the result, or to achieve a specified tolerance with as few function evaluations as possible.     

Detection of the singularities of a complex function by numerical approximations of its Laurent coefficients

For several applications, it is important to know the location of the singularities of a complex function: just for example, the rightmost singularity of a Laplace Transform is related to the exponential order of its inverse function. We discuss a numerical method to approximate, within an input accuracy tolerance, a finite sequence of Laurent coefficients of a function by means of the Discrete Fourier Transform (DFT) of its samples along an input circle. The circle may also enclose some singularities, since the method works with the Laurent expansion. The DFT is computed by the FFT algorithm so that, from a computational point of view, the efficiency is guaranteed. The function samples may be obtained by solving a numerical problem such as, for example, a differential problem. We derive, as consequences of the method, some new outcomes able to detect those singularities which are close to the circle and to discover if the singularities are all external or internal to the circle so that the Laurent expansion reduces to its regular or singular part, respectively. Other singularities may be located by means of a repeated application of the method, as well as an analytic continuation. Some examples and results, obtained by a first implementation, are reported.     

A method for determining risk aversion functions from uncertain market prices of risk

In Gzyl and Mayoral (2008) we developed a technique to solve the following type of problems: How to determine a risk aversion function equivalent to pricing a risk with a load, or equivalent to pricing different risks by means of the same risk distortion function. The information on which the procedure is based consists of the market prices of the risk. Here we extend that method to cover the case in which there may be uncertainties in the market prices of the risks.     

The if-problem in automatic differentiation

We deal with a severe problem that arises in the automatic differentiation of functions. Many computer programs defining a function employ statements of the form if B(x) then S1 else S2, where B(x) is a Boolean expression and S1 and S2 denote subprograms. This often leads to a piecewise definition of the function under consideration. Automatic differentiation of the pieces may be hazardous, for instance in cases where the underlying function is differentiable but one or the other piece is not. In such cases available software often fails to produce correct results. To resolve this perplexity, we distinguish between a function and its representations. In particular, we introduce the notion derivative-consistent. Automatic differentiation applied to a derivative-consistent representation of a function yields correct results.     

Parameter estimation by Hellinger type distance for multivariate distributions based upon probability generating functions

Maximum likelihood (ML) estimation is a popular method for parameter estimation when modeling discrete or count observations but unfortunately it may be sensitive to outliers. Alternative robust methods like minimum Hellinger distance (MHD) have been proposed for estimation. However, in the multivariate case, the MHD method leads to computer intensive estimation especially when the joint probability density function is complicated. In this paper, a Hellinger type distance measure based on the probability generating function is proposed as a tool for quick and robust parameter estimation. The proposed method yields consistent estimators, performs well for simulated and real data, and can be computationally much faster than ML or MHD estimation.     

Preliminary test estimation in system regression models in view of asymmetry

In this paper, we consider the system regression model introduced by Arashi and Roozbeh (Comput Stat 30:359–376, 2015) and study the performance of the feasible preliminary test estimator (FPTE) both analytically and computationally, under the assumption that constraints may hold on the vector parameter space. The performance of the FPTE is analysed through a Monte Carlo simulation study under bounded and or asymmetric loss functions. An application of the so-called Cobb–Douglas production function in economic modelling together with the results from the simulation study shows that the bounded linear exponential (BLINEX) loss function outperforms the linear exponential loss function (LINEX) by comparing risk values.     

梯形板弯曲问题的康托洛维奇解

在康托洛维奇对矩形板弯曲问题的有效近似解的基础上,本文进一步探讨了在不同边界条件下的梯形板弯曲问题的康氏解法.将板的位移用一级近似位移函数ω(x,y)=u(x,y)v(y)表示,式中, 在x方向的位移采用广义梁函数,用最小势能原理建立起对应于不同边界条件下的关于y方向位移函数v(y)的变系数常微分方程,求解微分方程,并利用边界条件,求出v(y)的精确解,从而可得到近似程度较高的梯形板弯曲问题的解.     

Estimation of the long-term strength of polymer materials under arbitrary loading histories

A nonlinear version of the phenomenological theory of long-term strength of polymer materials (viscoelastic bodies) is proposed. It is based on the introduction of a function accounting for the damage accumulation connected with changes in the load intensity. The form of this function may be determined from the results of testing the material with a load changing with time in a certain way, for instance, periodically. As a parameter, the function contains the rate of the changing load or the frequency for periodic loads. For a quasi-isotropic material, the basic relationships of the theory proposed are generalized to the case of combined stresses. The durability (failure time) calculations of the material based on this theory are compared with experimental data for a number of polymer and composite materials in a wide range of loading modes. Translated from Mekhanika Kompozitnykh Materialov, Vol. 35, No. 5, pp. 585–594, September–October, 1999.     

Scenario-based portfolio selection of investment projects with incomplete probability and utility information

In the selection of investment projects, it is important to account for exogenous uncertainties (such as macroeconomic developments) which may impact the performance of projects. These uncertainties can be addressed by examining how the projects perform across several scenarios; but it may be difficult to assign well-founded probabilities to such scenarios, or to characterize the decision makers’ risk preferences through a uniquely defined utility function. Motivated by these considerations, we develop a portfolio selection framework which (i) uses set inclusion to capture incomplete information about scenario probabilities and utility functions, (ii) identifies all the non-dominated project portfolios in view of this information, and (iii) offers decision support for rejection and selection of projects. The proposed framework enables interactive decision support processes where the implications of additional probability and utility information or further risk constraints are shown in terms of corresponding decision recommendations.     

On Extensions of the Frank-Wolfe Theorems

In this paper we consider optimization problems defined by a quadratic objective function and a finite number of quadratic inequality constraints. Given that the objective function is bounded over the feasible set, we present a comprehensive study of the conditions under which the optimal solution set is nonempty, thus extending the so-called Frank-Wolfe theorem. In particular, we first prove a general continuity result for the solution set defined by a system of convex quadratic inequalities. This result implies immediately that the optimal solution set of the aforementioned problem is nonempty when all the quadratic functions involved are convex. In the absence of the convexity of the objective function, we give examples showing that the optimal solution set may be empty either when there are two or more convex quadratic constraints, or when the Hessian of the objective function has two or more negative eigenvalues. In the case when there exists only one convex quadratic inequality constraint (together with other linear constraints), or when the constraint functions are all convex quadratic and the objective function is quasi-convex (thus allowing one negative eigenvalue in its Hessian matrix), we prove that the optimal solution set is nonempty.     

The Generalized Matrix Sector Function and the Separation of Matrix Eigenvalues

The matrix sector function of A is introduced and generalizedto the matrix sector function of g(A), where the complex matrixA may have a real or complex characteristic polynomial and g(A)is a matrix function of a conformal mapping. The generalizedmatrix sector function of A is employed to separate the matrixeigenvalues relative to a sector, a circle, and a sector ofa circle in the complex plane without actually seeking the characteristicpolynomial and the matrix eigenvalues relative to a sector,a circle, and a sector of a circle in the complex plane withoutactually seeking the characteristic polynomial and the matrixeigenvalues themselves. Also, the generalized matrix sectorfunction of A is utilized to carry out the block-diagonalizationand block-triangularization of a system matrix, which are usefulin developing applications to mathematical science and control-systemproblems.