Non-standard Problem Research Articles

Testing random effects in linear mixed-effects models with serially correlated errors.

In linear mixed-effects models, random effects are used to capture the heterogeneity and variability between individuals due to unmeasured covariates or unknown biological differences. Testing for the need of random effects is a nonstandard problem because it requires testing on the boundary of parameter space where the asymptotic chi-squared distribution of the classical tests such as likelihood ratio and score tests is incorrect. In the literature several tests have been proposed to overcome this difficulty, however all of these tests rely on the restrictive assumption of i.i.d. measurement errors. The presence of correlated errors, which often happens in practice, makes testing random effects much more difficult. In this paper, we propose a permutation test for random effects in the presence of serially correlated errors. The proposed test not only avoids issues with the boundary of parameter space, but also can be used for testing multiple random effects and any subset of them. Our permutation procedure includes the permutation procedure in Drikvandi, Verbeke, Khodadadi, and Partovi Nia (2013) as a special case when errors are i.i.d., though the test statistics aredifferent. We use simulations and a real data analysis to evaluate the performance of the proposed permutation test. We have found that random slopes for linear and quadratic time effects may not be significant when measurement errors are seriallycorrelated.

VALUATION OF CONTINGENT GUARANTEES USING LEAST-SQUARES MONTE CARLO

AbstractWe consider the problem of pricing modern guarantee concepts in unit-linked life insurance, where the guaranteed amount grows contingent on the performance of an investment fund that acts simultaneously as the underlying security and the replicating portfolio. Using the Martingale Method, this nonstandard pricing problem can be transformed into a fixed-point problem, whose solution requires the evaluation of conditional expectations of highly path-dependent payoffs. By adapting the least-squares Monte Carlo method for American option pricing problems, we develop a new numerical approach to approximate the value of contingent guarantees and prove its convergence. Our valuation procedure can be applied to large-scale pricing problems, for which existing methods are infeasible, and leads to significant improvements in performance.

Dictionary filtering: a probabilistic approach to online matrix factorisation

This paper investigates a link between matrix factorisation algorithms and recursive linear filters. In particular, we describe a probabilistic model in which sequential inference naturally leads to a matrix factorisation procedure. Using this probabilistic model, we derive a matrix-variate recursive linear filter that can be run efficiently in high-dimensional settings and leads to the factorisation of the data matrix into a dictionary matrix and a coefficient matrix. The resulting algorithm, referred to as the dictionary filter, is inherently online and has easy-to-tune parameters. We provide an extension of the proposed method for the cases where the dataset of interest is time-varying and nonstationary, thereby showing the adaptability of the proposed framework to non-standard problem settings. Numerical results, which are provided for image restoration and video modelling problems, demonstrate that the proposed method is a viable alternative to existing methods.

Controlling co-generation: conservation laws, modelling and Lyapunov synthesis

ABSTRACTThe paper considers a model of co-generation with distributed parameters, described by a non-standard boundary value problem with nonlinear boundary conditions with respect to a system of conservation laws. For the aforementioned model, well posedness in the sense of Hadamard, existence of some invariant sets, equilibria and their stability will be studied. The stabilising feedback laws are synthesised using a suitably constructed Lyapunov functional.

Sensitivity analysis with respect to observations in variational data assimilation for parameter estimation

Abstract. The problem of variational data assimilation for a nonlinear evolution model is formulated as an optimal control problem to find unknown parameters of the model. The observation data, and hence the optimal solution, may contain uncertainties. A response function is considered as a functional of the optimal solution after assimilation. Based on the second-order adjoint techniques, the sensitivity of the response function to the observation data is studied. The gradient of the response function is related to the solution of a nonstandard problem involving the coupled system of direct and adjoint equations. The nonstandard problem is studied, based on the Hessian of the original cost function. An algorithm to compute the gradient of the response function with respect to observations is presented. A numerical example is given for the variational data assimilation problem related to sea surface temperature for the Baltic Sea thermodynamics model.

Teachers’ influence on the quality of pupils’ written explanations – Third-graders solving a simplified arithmagon task during a mathematics lesson

The aim of this study is to find out whether there is a connection between teacher’s request and guidance for written explanation and third-graders’ achievements in solving a non-standard problem. Pupils’ task was to solve a simplified arithmagon and to explain their solution. The lessons of seven teachers were recorded and their actions were examined and categorized during a problem-solving lesson. Also pupils’ solutions were checked and classified. The teacher’s behavior seems to have a crucial role in the quality of pupils’ written explanations. The third-graders had difficulties in writing their reasoning for solving the problem.

Modeling of three dimensional thermocapillary flows with evaporation at the interface based on the solutions of a special type of the convection equations

• New 3D exact solution of the problem of evaporative convection is constructed. • Numerical algorithm for finding basic characteristics is proposed. • Basic mechanisms defining the structure of flows are described. • Influence of thermal load and gravity is studied. • Applicability of the different types of boundary conditions is discussed. Theoretical and numerical study of the convection processes, which are accompanied by evaporation/condensation, in the framework of new non-standard problem is largely motivated by new physical experiments. One of the principal questions is to understand the character and to evaluate the degree of influence of particular factors or their combined action on the structure of the joint flows of liquid and gas-vapor mixture. The flow topology is determined by four main mechanisms: natural and thermocapillary convection, tangential stresses and mass transfer due to evaporation at the interface. The mathematical modeling of the fluid flows in an infinite channel with a rectangular cross section is carried out on the basis of the solution of a special type of the convection equations. The effects of thermodiffusion and diffusive thermal conductivity in the gas phase and evaporation at the thermocapillary interface are taken into consideration. Numerical investigations are performed for the liquid – gas (ethanol – nitrogen) system under normal and low gravity. The fluid flows are characterized as translational and progressively rotational motions and can be realized in various forms.

Non-standard adjoint problem in topology optimization using BEM in time harmonic dynamics

Non-standard adjoint problem in topology optimization using BEM in time harmonic dynamics

Optimal open-loop strategies in a debt management problem

The paper studies optimal strategies for a borrower who needs to repay his debt, in an infinite time horizon. An instantaneous bankruptcy risk is present, which increases with the size of the debt. This induces a pool of risk-neutral lenders to charge a higher interest rate, to compensate for the possible loss of part of their investment. Solutions are interpreted as Stackelberg equilibria, where the borrower announces his repayment strategy [Formula: see text] at all future times, and lenders adjust the interest rate accordingly. This yields a highly non-standard problem of optimal control, where the instantaneous dynamics depend on the entire future evolution of the system. Our analysis shows the existence of optimal open-loop controls, deriving necessary conditions for optimality and characterizing possible asymptotic limits as [Formula: see text].

Homogenization of layered materials with rigid components in single-slip finite crystal plasticity

We determine the effective behavior of a class of composites in finite-strain crystal plasticity, based on a variational model for materials made of fine parallel layers of two types. While one component is completely rigid in the sense that it admits only local rotations, the other one is softer featuring a single active slip system with linear self-hardening. As a main result, we obtain explicit homogenization formulas by means of Gamma -convergence. Due to the anisotropic nature of the problem, the findings depend critically on the orientation of the slip direction relative to the layers, leading to three qualitatively different regimes that involve macroscopic shearing and blocking effects. The technical difficulties in the proofs are rooted in the intrinsic rigidity of the model, which translates into a non-standard variational problem constraint by non-convex partial differential inclusions. The proof of the lower bound requires a careful analysis of the admissible microstructures and a new asymptotic rigidity result, whereas the construction of recovery sequences relies on nested laminates.

Bayesian regression for group testing data.

Group testing involves pooling individual specimens (e.g., blood, urine, swabs, etc.) and testing the pools for the presence of a disease. When individual covariate information is available (e.g., age, gender, number of sexual partners, etc.), a common goal is to relate an individual's true disease status to the covariates in a regression model. Estimating this relationship is a nonstandard problem in group testing because true individual statuses are not observed and all testing responses (on pools and on individuals) are subject to misclassification arising from assay error. Previous regression methods for group testing data can be inefficient because they are restricted to using only initial pool responses and/or they make potentially unrealistic assumptions regarding the assay accuracy probabilities. To overcome these limitations, we propose a general Bayesian regression framework for modeling group testing data. The novelty of our approach is that it can be easily implemented with data from any group testing protocol. Furthermore, our approach will simultaneously estimate assay accuracy probabilities (along with the covariate effects) and can even be applied in screening situations where multiple assays are used. We apply our methods to group testing data collected in Iowa as part of statewide screening efforts for chlamydia, and we make user-friendly R code available to practitioners.

Approximations to Expected Utility Optimization in Continuous Time

In this paper, we explore approximate solutions to optimal control problems that cannot be solved analytically with existing techniques. Inspired by the mean-variance analysis of the single period environment, an advanced and a simple method are developed in order to approximate optimal investment strategies in continuous time. In the advanced method, the original problem is approximated by a Taylor series expansion in the conditional mean of terminal wealth. As the point of expansion is thereby continuously changing, the approximation results in a non-standard optimal control problem that can be characterised by an extended HJB equation. In the simple method, the problem is expanded in the initial mean, leading to a problem that can be solved using the classical HJB equation in an unconventional way. The advanced approximated problem inherits more features from the original problem than the simple approximated problem. In a numerical example, we illustrate how the advanced approximate strategy gives a better approximation than the simple approximated strategy. An approximate solution is determined to a prospect theory investment problem, utilising the advanced method of approximation. The solution reflects the same behaviour as the classical life-cycle investment strategy, where the proportion of wealth invested in the risky asset is decreasing over time and independent of the level of wealth.

Sensitivity with respect to observations in variational data assimilation

Abstract The problem of variational data assimilation for a nonlinear evolution model is formulated as an optimal control problem to find the initial condition function (analysis). The observation data, and hence the optimal solution, may contain uncertainties. A response function is considered as a functional of the optimal solution after assimilation. Based on the second-order adjoint techniques, the sensitivity of the response function to the observation data is studied. The gradient of the response function is related to the solution of a non-standard problem involving the coupled system of direct and adjoint equations. The solvability of the non-standard problem is studied, based on the Hessian of the original cost function. An algorithm to compute the gradient of the response function with respect to observations is developed and justified.

An a Posteriori Multi-Objective Supplier Portfolio Selection Approach Under Risk and Sustainability Considerations

This research presents a novel, state-of-the-art methodology for solving the multi-criteria supplier selection problem under risk and sustainability considerations. The approach combines multi-objective optimization with the analytic network process to meet the requirements for supplier portfolio configuration with sustainability considerations. To integrate the aspect ‘risk’ into the supplier selection problem, this research develops a multi-objective optimization model based on the investment portfolio theory introduced by Markowitz. The proposed model is a non-standard portfolio selection problem with four objectives: to minimize the purchasing costs, to select the supplier portfolio with the highest logistics service, to minimize the supply risk, and to order as much as possible from those suppliers with outstanding sustainability performance. The optimization model, which has three linear and one quadratic objective function, is solved by an algorithm which analytically computes a set of efficient solutions and provides graphical decision support by a visualization of the complete and exactly-computed Pareto front (a posteriori approach). The possibility of computing all Pareto optimal supplier portfolios is beneficial for decision makers as they can compare all optimal solutions at once, identify the trade-offs between the criteria, and study how the different aspects of supplier portfolio configuration may be balanced to finally choose the composition that satisfies the purchasing company’s strategy at best. The approach has been applied to a real-world supplier portfolio configuration case to demonstrate its applicability and to analyze how the consideration of sustainability requirements may affect the traditional supplier selection and purchasing goals in a real-life setting.

NONCLASSICAL FOURTH ORDER DISCONTINUOUS BOUNDARY VALUE PROBLEM WITH ABSTRACT LINEAR FUNCTIONALS

In this study, we have considered a nonstandard boundary-value problem for fourth order differential equation on two disjoint intervals The boundary conditions contains not only endpoints and but also a point interaction and abstract linear functional So our problem is nota pure differential one. We investigate such some important properties as isomorphism, Fredholmness and coerciveness with respect to the spectral parameter.

A mixed-integer linear programming model for optimal vessel scheduling in offshore oil and gas operations

This paper introduces a non-standard vehicle routing problem (VRP) arising in the oil and gas industry. The problem involves multiple offshore production facilities, each of which requires regular servicing by support vessels to replenish essential commodities such as food, water, fuel, and chemicals. The support vessels are also required to assist with oil off-takes, in which oil stored at a production facility is transported via hose to a waiting tanker. The problem is to schedule a series of round trips for the support vessels so that all servicing and off-take requirements are fulfilled, and total cost is minimized. Other constraints that must be considered include vessel suitability constraints (not every vessel is suitable for every facility), depot opening constraints (base servicing can only occur during specified opening periods), and off-take equipment constraints (the equipment needed for off-take support can only be deployed after certain commodities have been offloaded). Because of these additional constraints, the scheduling problem under consideration is far more difficult than the standard VRP. We formulate a mixed-integer linear programming (MILP) model for determining the optimal vessel schedule. We then verify the model theoretically and show how to compute the vessel utilization ratios for any feasible schedule. Finally, simulation results are reported for a real case study commissioned by Woodside Energy Ltd, Australia's largest dedicated oil and gas company.

Optimal State Estimation for Non-Time Invertible Evolutionary Systems

In this paper, we formulate state estimation problem for usually non-time invertible evolutionary systems into a nonstandard finite horizon linear quadratic output tracking problem. This problem directly leads to the classical filtering problem with disturbance constraint. For a given reference trajectory, we give the explicit expressions of optimal initial state and optimal control. In addition, based on some well-known results for classical finite horizon linear quadratic optimal control problems, we obtain, in terms of measured output, the pointwise linear state feedbacks to optimal initial state and optimal control, and the representation of optimal cost is also given. As an important consequence, the differential equation satisfied by the optimal state estimate for filtering problem is developed. Some examples are given as applications.

Sensitivity of functionals with respect to observations in variational data assimilation

AbstractThe problem of variational data assimilation for a model of ocean thermodynamics is formulated as an optimal control problem to find the boundary heat flux. The sensitivity of functionals of the optimal solution with respect to observations is studied. Computing the gradient of the functionals is reduced to the solution of a non-standard problem which is a coupled system involving direct and adjoint equations with mutually dependent boundary values. Solvability of the non-standard problem is studied based on the Hessian of the original cost function. An algorithm for computing the gradient of the response function related to the mean surface temperature is developed and justified. Numerical examples are presented for the Baltic Sea thermodynamics model.

SEMIPARAMETRIC ESTIMATION WITH GENERATED COVARIATES

We study a general class of semiparametric estimators when the infinite-dimensional nuisance parameters include a conditional expectation function that has been estimated nonparametrically using generated covariates. Such estimators are used frequently to e.g., estimate nonlinear models with endogenous covariates when identification is achieved using control variable techniques. We study the asymptotic properties of estimators in this class, which is a nonstandard problem due to the presence of generated covariates. We give conditions under which estimators are root-nconsistent and asymptotically normal, derive a general formula for the asymptotic variance, and show how to establish validity of the bootstrap.

Sensitivity of functionals in problems of variational assimilation of observational data

The problem of the variational assimilation of observational data is stated for a nonlinear evolution model as a problem of optimal control in order to find the function of initial condition. The operator of the model, and consequently the optimal solution, depend on parameters that may contain uncertainties. A functional of the solution of the problem of variational data assimilation is considered. Using the method of second-order adjoint equations, the sensitivity of the functional in respect to the model parameters is studied. The gradient of the functional is expressed through solving a “nonstandard” (nonclassical) problem that involves the coupled system of direct and adjoint equations. The solvability of the nonstandard problem using the Hessian of the initial functional of observations is studied. Numerical algorithms for solving the problem and computing the gradient of the functional under consideration are developed with respect to the parameters. The results of the studies are applied in the problem of variational data assimilation for a 3D ocean thermodynamic model.