Generalizing the Pareto to the log-Pareto model and statistical inference

In this article we introduce a full-fledged statistical model of log-Pareto distribution functions (dfs) parametrized by two shape parameters and a scale parameter. Pareto dfs can be regained in the limit by varying parameters of log-Pareto dfs, whence the log-Pareto model can be regarded as an extension of the Pareto model. Log-Pareto dfs are first of all obtained by means of exponential transformations of Pareto dfs. We also indicate an iterated application of such a procedure. A class of generalized log-Pareto dfs is considered as well. In addition, power-pot (p-pot) stable dfs – related to p-max stable dfs – are introduced and log-Pareto dfs are identified as special cases. A modification of a quick (systematic) estimator is proposed as an initial estimator for the numerical computation of the maximum likelihood estimator (MLE) in the 3-parameter model.      

On the Development of a Mixed-Integer Linear Programming Model for the Flowshop Sequencing Problem

This paper describes the development of a mixed-integer linear programming (MILP) model for the standard N-job, M-machine flowshop sequencing problem. Based on an earlier all-integer model developed by Wagner, this MILP model has been used to solve optimally problems with as many as 25 jobs and as many as 10 machines. Variants of the standard flowshop model, including a variety of performance measures, are also presented. Computational experience involving the successful solution of over 175 flowshop problems is discussed, and suggestions for future research projects are offered.     

A Parametric n-Dimensional Markov-Functional Model in the Terminal Measure

Abstract

This article develops and tests an n-dimensional Markov-functional interest rate model in the terminal measure based on parametric functional forms of exponential type. The parametric functional forms enable analytical expressions for forward discount bonds and forward LIBORs at all times and allows for calibration of the model to caplet prices given by a displaced diffusion Black model. The analytical expressions of the model provide a theoretical tool for understanding the structure of standard Markov-functional models (MFMs) as well as comparisons with the LIBOR market model (LMM). In particular, it is shown that for ‘typical’ market data the model is close enough to the LMM to be able to calibrate using the LMM calibration set-up and machinery. This provides further information about the similarities (as well as some of the differences) between MFM and LMM. The parametric n-dimensional MFM may be used for products that require high-dimensional models for appropriate pricing and risk management. When compared with an n-factor LMM, it has the virtue of being (much) faster for certain types of products.     

On the flatness of local models for the symplectic group

We investigate the bad reduction of certain Shimura varieties (associated to the symplectic group). More precisely, we look at a model of the Shimura variety at a prime p, with parahoric level structure at p. We show that this model is flat, as conjectured by Rapoport and Zink (Ann. of Math. Stud. 141 (1996)), and that its special fibre is reduced.A crucial ingredient is Faltings’ theorem on the normality of Schubert varieties in the affine flag variety.     

Stability of the LCD model

The growing network model with loops and multiple edges proposed by Bollobás et al. (Random Structures and Algorithms 18(2001)) is restudied from another perspective. Based on the first-passage probability of Markov chains, we prove that the degree distribution of the LCD model is power-law with degree exponent 3 as the network size grows to infinity.     

Fluctuations of the free energy in the high temperature Hopfield model

We consider the Hopfield model of size N and with p∼tN patterns, in the whole high temperature (paramagnetic) region. Our result is that the partition function has log-normal fluctuations. It is obtained by extending to the present model the method of the interpolating Brownian motions used by Comets (Comm. Math. Phys. 166 (1995) 549–564) for the Sherrington–Kirkpatrick model. We view the load t of the memory as a dynamical parameter, making the partition function a nice stochastic process. Then we write some semi-martingale decomposition for the logarithm of the partition function, and we prove that all the terms in this decomposition converge. In particular, the martingale term converges to a Gaussian martingale.     

Characterization of the multivariate Gauss-Markoff model with singular covariance matrix and missing values

The aim of this paper is to characterize the Multivariate Gauss-Markoff model (MGM) as in (2.1) with singular covariance matrix and missing values. MGMDP2 model and completed MGMDP2Q model are obtained by three transformations D, P and Q (cf. (3.21)) of MGM. The unified theory of estimation (Rao, 1973) which is of interest with respect to MGM has been used.The characterization is reached by estimation of parameters: scalar ² and linear combination as in (4.8), (4.6), (4.7) as well as by the model of the form (5.1) (cf. Th. 5.1). Moreover, testing linear hypothesis in the available model MGMDP2 by test function F as in (6.3) and (6.4) is considered.It is known (Oktaba 1992) that ten quantities in models MGMDP2, and MGMDP2Q are identical (invariant). They permit to say that formulas for estimation and testing in both models are identical (Oktaba et al., 1988, Baksalary and Kala, 1981, Drygas, 1983).An algorithm and the UMGMBO program for calculations concerning estimation and testing in MGM have been presented by Oktaba and Osypiuk (1993).     

Modeling the effects of variable external influences and demographic processes on innovation diffusion

In this paper, a nonlinear mathematical model for innovation diffusion is proposed and analyzed by considering the effects of variable external influences (cumulative marketing efforts) and human population (variable marketing potential) in a society. The change in the population density is caused by various demographic processes such as immigration, emigration, intrinsic growth rate, death rate, etc.Thus, the problem of innovation diffusion is governed by three dynamic variables, namely, non adopters’ density, adopters’ density and the cumulative density of external influences. The model is analyzed by using the stability theory of differential equations and computer simulation.The model analysis shows that the main effect of the increase in cumulative density of external influences is to make the adopter population density reach its equilibrium at a much faster rate. It further shows that the density of adopters’ population increases as the parameters related to increase in non adopters’ population density increase. The effects of various parameters in the model on the nature of existing single equilibrium have also been discussed by using numerical simulation. It is shown that parameters related to the growth of non adopters’ population density have stabilizing effects on the system.     

On the structure of kripke models of heyting arithmetic

Since in Heyting Arithmetic (HA) all atomic formulas are decidable, a Kripke model for HA may be regarded classically as a collection of classical structures for the language of arithmetic, partially ordered by the submodel relation. The obvious question is then: are these classical structures models of Peano Arithmetic (PA)? And dually: if a collection of models of PA, partially ordered by the submodel relation, is regarded as a Kripke model, is it a model of HA? Some partial answers to these questions were obtained in [6], [3], [1] and [2]. Here we present some results in the same direction, announced in [7]. In particular, it is proved that the classical structures at the nodes of a Kripke model of HA must be models of IΔ₁ (PA^- with induction for provably Δ₁ formulas) and that the relation between these classical structures must be that of a Δ₁-elementary submodel. MSC: 03F30, 03F55.     

Comparison and correction of the drop breakup models for stochastic dilute spray flow

Spray–gas interaction is common in many industrial applications that use a liquid jet injection system. Numerous liquid drops interact with the surrounding gas as they travel through the air. During such a travel, aerodynamic interaction between a drop and the surrounding gas flattens the drop and ultimately, breaks up the drop. The TAB (Taylor Analogy Breakup) model was proposed by O’Rourke and Amsden (1987) [6] for the KIVA spray code, but the use of this model has been controversial because the original paper that proposed this model has typographical errors. Another well-known drop breakup model, such as the DDB (Drop Deformation Breakup) model of Ibrahim et al. (1993) [8], has been widely used. However, although numerical solutions of the DDB model ostensibly make it appear superior to those of other previous breakup models, they contain errors that need to be amended. This paper aims to clarify the error controversies of both models; the typographical errors and the erroneous numerical solutions. The complete mathematical derivation of the TAB model is presented, and its correct numerical solutions are compared against the experimental data. We found that the TAB model was superior to other breakup models, such as Clark (1988) [7] and DDB.     

A dynamic subgrid-scale model for the large eddy simulation of stratified flow

A new dynamic subgrid-scale (SGS) model, including subgrid turbulent stress and heat flux models for stratified shear flow is proposed by using Yoshizawa’s eddy viscosity model as a base model. Based on our calculated results, the dynamic subgrid-scale model developed here is effective for the large eddy simulation (LES) of stratified turbulent channel flows. The new SGS model is then applied to the large eddy simulation of stratified turbulent channel flow under gravity to investigate the coupled shear and buoyancy effects on the near-wall turbulent statistics and the turbulent heat transfer at different Richardson numbers. The critical Richardson number predicted by the present calculation is in good agreement with the value of theoretical analysis     

Approximating the Quasi-stationary Distribution of the SIS Model for Endemic Infection

Probably the simplest model for endemic infection is the susceptible-infected-susceptible (SIS) logistic model. Long-term behaviour of this model prior to disease extinction is described by the quasi-stationary distribution. This quasi-stationary distribution has been the subject of much previous work, including derivation of a variety of approximations, using both standard distributional forms and specialized approximating formulae. The aim of this paper is to carry out a systematic comparison between approximations. As well as comparing previously available approximations, we derive several new variants. Taking into account both accuracy (measured using total variation distance) and simplicity, and denoting by R ₀ the basic reproduction number, our main findings are: (a) in the subcritical region R ₀ < 1 a geometric distribution approximation is preferred; (b) in the supercritical region R ₀ ≫ 1 a beta-binomial distribution is preferred. Both of these preferred approximations are new.     

广义Cox模型下的逐步扩大域流相关问题研究

在本文中, 我们主要讨论了广义Cox模型的信息流扩大问题. 假设在市场中有两类投资者, 第一类投资者拥有市场信息, 这里由一个维的布朗运动和一个可积随机 测度驱动; 而第二类投资者具有扩大的信息流, 这里假设是由信息流和广义Cox的模型刻画的违约信息流生成. 我们建立和刻画了广义Cox模型并且求给出它的主要性质包括生存过程和违约条件密度. 与Cox模型显著区别的是, 如果违约由广义Cox模型模型刻画, 与Cox模型平凡的结果不同的是, 鞅的分解更复杂和具有一般性.     

Symmetric Fock space and orthogonal symmetric polynomials associated with the Calogero model

Using a similarity transformation that maps the Calogero model into N decoupled quantum harmonic oscillators, we construct a set of mutually commuting conserved operators of the model and their simultaneous eigenfunctions.The simultaneous eigenfunction is a deformation of the symmetrized number state (bosonic state) and forms an orthogonal basis of the Hilbert (Fock) space of the model. This orthogonal basis is different from the known one that is a variant of the Jack polynomial, i.e., the Hi-Jack polynomial. This fact shows that the conserved operators derived by the similarity transformation and those derived by the Dunkl operator formulation do not commute. Thus we conclude that the Calogero model has two, algebraically inequivalent sets of mutually commuting conserved operators, as is the case with the hydrogen atom. We also confirm the same story for the B_N-Calogero model.     

Extending the stochastic programming framework for the modeling of several decision makers: pricing and competition in the telecommunication sector

In a previous paper, Gouveia and Magnanti (2003) found diameter-constrained minimal spanning and Steiner tree problems to be more difficult to solve when the tree diameter D is odd. In this paper, we provide an alternate modeling approach that views problems with odd diameters as the superposition of two problems with even diameters. We show how to tighten the resulting formulation to develop a model with a stronger linear programming relaxation. The linear programming gaps for the tightened model are very small, typically less than 0.5–, and are usually one third to one tenth of the gaps of the best previous model described in Gouveia and Magnanti (2003). Moreover, the new model permits us to solve large Euclidean problem instances that are not solvable by prior approaches. Research funded in part by the Research Projects POCTI-ISFL-1-152,POSI/CPS/41459/2001 and POCTI/MAT/139/94 Research funded in part by the Singapore-MITAlliance(SMA)     

Discussion of Different Model Approaches for the Flow Behavior of Ice

Ice of Antarctic ice shelves is assumed to behave on long-term as an incompressible viscous fluid, which is dominated on short time scales by the elastic response. Hence, a viscoelastic material model is required. The thermodynamic pressure is treated differently in elastic and viscous models. For small deformations, the elastic isometric stress for ν → 0.5 gives similar results to those solving for pressure in an incompressible laminar flow model. A viscous model, in which the thermodynamic pressure is approximated by an elastic isometric stress, can be easily extended to viscoelasticity. (© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

MIP approaches for the integrated berth allocation and quay crane assignment and scheduling problem

In this paper we consider an integrated berth allocation and quay crane assignment and scheduling problem motivated by a real case where a heterogeneous set of cranes is considered. A first mathematical model based on the relative position formulation (RPF) for the berth allocation aspects is presented. Then, a new model is introduced to avoid the big-M constraints included in the RPF. This model results from a discretization of the time and space variables. For the new discretized model several enhancements, such as valid inequalities, are introduced. In order to derive good feasible solutions, a rolling horizon heuristic (RHH) is presented. A branch and cut approach that uses the enhanced discretized model and incorporates the upper bounds provided by the RHH solution is proposed. Computational tests are reported to show (i) the quality of the linear relaxation of the enhanced models; (ii) the effectiveness of the exact approach to solve to optimality a set of real instances; and (iii) the scalability of the RHH based on the enhanced mathematical model which is able to provide good feasible solutions for large size instances.     

Frequency domain characteristics of linear operator to decompose a time series into the multi-components

Frequency domain properties of the operators to decompose a time series into the multi-components along the Akaike's Bayesian model (Akaike (1980, Bayesian Statistics, 143–165, University Press, Valencia, Spain)) are shown. In that analysis a normal disturbance-linear-stochastic regression prior model is applied to the time series. A prior distribution, characterized by a small number of hyperparameters, is specified for model parameters. The posterior distribution is a linear function (filter) of observations. Here we use frequency domain analysis or filter characteristics of several prior models parametrically as a function of the hyperparameters.