Diagnostic checking for multivariate regression models

Diagnostic checking for multivariate parametric models is investigated in this article. A nonparametric Monte Carlo Test (NMCT) procedure is proposed. This Monte Carlo approximation is easy to implement and can automatically make any test procedure scale-invariant even when the test statistic is not scale-invariant. With it we do not need plug-in estimation of the asymptotic covariance matrix that is used to normalize test statistic and then the power performance can be enhanced. The consistency of NMCT approximation is proved. For comparison, we also extend the score type test to one-dimensional cases. NMCT can also be applied to diverse problems such as a classical problem for which we test whether or not certain covariables in linear model has significant impact for response. Although the Wilks lambda, a likelihood ratio test, is a proven powerful test, NMCT outperforms it especially in non-normal cases. Simulations are carried out and an application to a real data set is illustrated.     

Computation of optimal portfolios using simulation-based dimension reduction

This paper describes a simple and efficient method for determining the optimal portfolio for a risk averse investor. The portfolio selection problem is of long standing interest to finance scholars and it has obvious practical relevance. In a complete market the modern procedure for computing the optimal portfolio weights is known as the martingale approach. Recently, alternative implementations of the martingale approach based on Monte Carlo methods have been proposed. These methods use Monte Carlo simulation to compute stochastic integrals. This paper examines the efficient implementation of one of these methods due to [Cvitanic, J., Goukasian, L., Zapatero, F. 2003. Monte Carlo computation of optimal portfolios in complete markets. J. Econom. Dynam. Control 27, 971–986]. We explain why a naive application of the quasi-Monte Carlo method to this problem is often only marginally more efficient than the classical Monte Carlo method. Using the dimension reduction technique of [Imai, J., Tan, K.S., 2007. A general dimension reduction method for derivative pricing. J. Comput. Financ. 10 (2), 129–155] it is possible to significantly reduce the effective dimension of the problem. The paper shows why the proposed technique leads to a dramatic improvement in efficiency.     

Computation of optimal portfolios using simulation-based dimension reduction

This paper describes a simple and efficient method for determining the optimal portfolio for a risk averse investor. The portfolio selection problem is of long standing interest to finance scholars and it has obvious practical relevance. In a complete market the modern procedure for computing the optimal portfolio weights is known as the martingale approach. Recently, alternative implementations of the martingale approach based on Monte Carlo methods have been proposed. These methods use Monte Carlo simulation to compute stochastic integrals. This paper examines the efficient implementation of one of these methods due to [Cvitanic, J., Goukasian, L., Zapatero, F. 2003. Monte Carlo computation of optimal portfolios in complete markets. J. Econom. Dynam. Control 27, 971-986]. We explain why a naive application of the quasi-Monte Carlo method to this problem is often only marginally more efficient than the classical Monte Carlo method. Using the dimension reduction technique of [Imai, J., Tan, K.S., 2007. A general dimension reduction method for derivative pricing. J. Comput. Financ. 10 (2), 129-155] it is possible to significantly reduce the effective dimension of the problem. The paper shows why the proposed technique leads to a dramatic improvement in efficiency.     

A Nonlinear Deterministic Mode Decomposition Strategy for High-Dimensional Monte Carlo Simulations

We present a new reduced Monte Carlo simulation strategy for nonlinear high-order systems based on an extension of the proper orthogonal decomposition for transient excited structures. This novel approach enables the evaluation of the response statistics in a fractional amount of time compared to the full Monte Carlo simulation procedure. (© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Number Theoretic Methods in Applied Statistics

This paper gives some applications of number-theoretic method (or quasi Monte Carlo method) for numerical evaluation of probabilities and moments of a continuous multivariate distribution over a special domain such as cube, ball, sphere, simplex, etc., where the uniformly distributed sets of points in such domains, which are useful in experimental design, simulation, geometry probability, etc., are suggested. Some applications of number-theoretic method in optimization are discussed also.     

A simulation-based approach to two-stage stochastic programming with recourse

In this paper we consider stochastic programming problems where the objective function is given as an expected value function. We discuss Monte Carlo simulation based approaches to a numerical solution of such problems. In particular, we discuss in detail and present numerical results for two-stage stochastic programming with recourse where the random data have a continuous (multivariate normal) distribution. We think that the novelty of the numerical approach developed in this paper is twofold. First, various variance reduction techniques are applied in order to enhance the rate of convergence. Successful application of those techniques is what makes the whole approach numerically feasible. Second, a statistical inference is developed and applied to estimation of the error, validation of optimality of a calculated solution and statistically based stopping criteria for an iterative alogrithm. © 1998 The Mathematical Programming Society, Inc. Published by Elsevier Science B.V.Supported by CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico), Brasília, Brazil, through a Doctoral Fellowship under grant 200595/93-8.     

Simulation of Uncertain Inelastic Material Behaviour using the Stochastic Finite Element Method

In this work, an approach for computing three-dimensional structures with random material properties, such as the yield stress, Young's modulus and hardening parameters is proposed. The random material properties are represented as random fields which are realized with the Spectral Representation Method (SPRM). The proposed approach is coupled with Monte Carlo Simulation (MCS) to determine the response statistics of a simple mechanical structure. The numerical results are compared with those obtained from classical Latin Hypercube Sampling (LHS). (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

你也需要蒙特卡罗方法——提高应用水平的若干技巧

本文是《你也需要蒙特卡罗方法》中的第二篇。文中讨论提高应用水平的一些技巧,涉及模拟模型的选取,提高计算速度或降低抽样方差的一些方法,诸如重要抽样、相关抽样、对偶抽样和分层抽样等。还讨论了模拟中所需的抽样次数的确定和模拟结果的精度评估等实用问题。     

Adaptive sample size and importance sampling in estimation-based local search for the probabilistic traveling salesman problem

The probabilistic traveling salesman problem is a paradigmatic example of a stochastic combinatorial optimization problem. For this problem, recently an estimation-based local search algorithm using delta evaluation has been proposed. In this paper, we adopt two well-known variance reduction procedures in the estimation-based local search algorithm: the first is an adaptive sampling procedure that selects the appropriate size of the sample to be used in Monte Carlo evaluation; the second is a procedure that adopts importance sampling to reduce the variance involved in the cost estimation. We investigate several possible strategies for applying these procedures to the given problem and we identify the most effective one. Experimental results show that a particular heuristic customization of the two procedures increases significantly the effectiveness of the estimation-based local search.     

Variance reduction techniques for estimation of integrals over a set of branching trajectories

Monte Carlo variance reduction techniques within the supertrack approach are justified as applied to estimating non-Boltzmann tallies equal to the mean of a random variable defined on the set of all branching trajectories. For this purpose, a probability space is constructed on the set of all branching trajectories, and the unbiasedness of this method is proved by averaging over all trajectories. Variance reduction techniques, such as importance sampling, splitting, and Russian roulette, are discussed. A method is described for extending available codes based on the von Neumann-Ulam scheme in order to cover the supertrack approach.     

Pricing of Ratchet equity-indexed annuities under stochastic interest rates

We consider the valuation of simple and compound Ratchet equity-indexed annuities (EIAs) in the presence of stochastic interest rates. We assume that the equity index follows a geometric Brownian motion and the short rate follows the extended Vasicek model. Under a given forward measure, we obtain an explicit multivariate normal characterization for multiple log-returns on the equity index. Using such a characterization, closed-form price formulas are derived for both simple and compound Ratchet EIAs. An efficient Monte Carlo simulation scheme is also established to overcome the computational difficulties resulting from the evaluation of high-dimensional multivariate normal cumulative distribution functions (CDFs) embedded in the price formulas as well as the consideration of additional complex contract features. Finally, numerical results are provided to illustrate the computational efficiency of our simulation scheme and the effects of various model and contract parameters on pricing.     

可变抽样区间的多变量自相关过程VAR控制图

基于批量-均值法的思想,向量自回归(VAR)控制图对多变量自相关过程的较小偏移可以进行有效控制。为了提高多变量自相关过程监控效率,本文研究可变抽样区间的VAR控制图。首先,对多变量自相关过程的VAR控制图进行可变抽样区间设计;然后,用蒙特卡洛模拟方法计算其平均报警时间;最后,以平均报警时间为评价准则,对所设计的可变抽样区间VAR控制图与固定抽样区间的VAR控制图进行比较研究。研究结果表明:所设计的可变抽样区间多变量自相关过程VAR控制图较固定抽样区间的多变量自相关过程VAR控制图能更好的监控过程的变化。     

The quasi-likelihood estimation in regression

I propose a simply method to estimate the regression parameters in quasi-likelihood model My main approach utilizes the dimension reduction technique to first reduce the dimension of the regressor X to one dimension before solving the quasi-likelihood equations. In addition, the real advantage of using dimension reduction technique is that it provides a good initial estimate for one-step estimator of the regression parameters. Under certain design conditions, the estimators are asymptotically multivariate normal and consistent. Moreover, a Monte Carlo simulation is used to study the practical performance of the procedures, and I also assess the cost of CPU time for computing the estimates.This research partially supported by the National Science Council, R.O.C. (Plan No. NSC 82-0208-M-032-023-T).     

Coarse-Grained Langevin Approximations and Spatiotemporal Acceleration for Kinetic Monte Carlo Simulations of Diffusion of Interacting Particles

Kinetic Monte Carlo methods provide a powerful computational tool for the simulation of microscopic processes such as the diffusion of interacting particles on a surface, at a detailed atomistic level. However such algorithms are typically computationatly expensive and are restricted to fairly small spatiotemporal scales. One approach towards overcoming this problem was the development of coarse-grained Monte Carlo algorithms. In recent literature, these methods were shown to be capable of efficiently describing much larger length scales while still incorporating information on microscopic interactions and fluctuations. In this paper, a coarse-grained Langevin system of stochastic differential equations as approximations of diffusion of interacting particles is derived, based on these earlier coarse-grained models. The authors demonstrate the asymptotic equivalence of transient and long time behavior of the Langevin approximation and the underlying microscopic process, using asymptotics methods such as large deviations for interacting particles systems, and furthermore, present corresponding numerical simulations, comparing statistical quantities like mean paths, auto correlations and power spectra of the microscopic and the approximating Langevin processes. Finally, it is shown that the Langevin approximations presented here are much more computationally efficient than conventional Kinetic Monte Carlo methods, since in addition to the reduction in the number of spatial degrees of freedom in coarse-grained Monte Carlo methods, the Langevin system of stochastic differential equations allows for multiple particle moves in a single timestep.     

Conditionally heteroscedastic factorial HMMs for time series in finance

In this article, we develop a new approach within the framework of asset pricing models that incorporates two key features of the latent volatility: co‐movement among conditionally heteroscedastic financial returns and switching between different unobservable regimes. By combining latent factor models with hidden Markov chain models we derive a dynamical local model for segmentation and prediction of multivariate conditionally heteroscedastic financial time series. We concentrate more precisely on situations where the factor variances are modelled by univariate generalized quadratic autoregressive conditionally heteroscedastic processes. The expectation maximization algorithm that we have developed for the maximum likelihood estimation is based on a quasi‐optimal switching Kalman filter approach combined with a generalized pseudo‐Bayesian approximation, which yield inferences about the unobservable path of the common factors, their variances and the latent variable of the state process. Extensive Monte Carlo simulations and preliminary experiments obtained with daily foreign exchange rate returns of eight currencies show promising results. Copyright © 2007 John Wiley & Sons, Ltd.     

多变量非线性贝叶斯动态模型的参数估计

本文讨论多变量非线性贝叶斯动态模型参数估计 ,将 Monte Carlo最优法用于极大似然函数 ,得到未知参数和状态变量的估计     

Prioritisation of an IT budget within a local authority

This paper describes the prioritisation of an IT budget within a department of a local authority. The decision problem is cast as a simple multiattribute evaluation but from two perspectives. First, as an exercise in group decision making. Here the emphasis is on a shared process wherein the object is to obtain consensus. The use of an explicit evaluation framework and the ability to interact with the evaluation data in real time via a simple spreadsheet model were found to improve the decision making. Second, the prioritisation is made analytically. The motivation is to determine the degree to which the rankings are the result of the structural characteristics of the projects themselves rather than of the differences in importance attached to the achievement of the goals represented by the project attributes. Three methods are used: Monte Carlo simulation of ranks, cluster analysis based on attributes and an approach based on entropy maximisation. It is found that in the case studied the structure inherent in the data is high and so the results of the analyses are robust. Finally, a procedure is suggested for the appropriate use of these analyses via a facilitator to aid prioritisation decisions.     

Adaptive Monte Carlo Variance Reduction for Lévy Processes with Two-Time-Scale Stochastic Approximation

We propose an approach to a twofold optimal parameter search for a combined variance reduction technique of the control variates and the important sampling in a suitable pure-jump Lévy process framework. The parameter search procedure is based on the two-time-scale stochastic approximation algorithm with equilibrated control variates component and with quasi-static importance sampling one. We prove the almost sure convergence of the algorithm to a unique optimum. The parameter search algorithm is further embedded in adaptive Monte Carlo simulations in the case of the gamma distribution and process. Numerical examples of the CDO tranche pricing with the Gamma copula model and the intensity Gamma model are provided to illustrate the effectiveness of our method.      

Numerical Computation of Multivariate Normal and Multivariate-t Probabilities over Convex Regions

Abstract

A methodology has been developed and Fortran 90 programs have been written to evaluate multivariate normal and multivariate-t integrals over convex regions. The Cholesky transformation is used to transform the integrand into a product of standard normal or spherically symmetric t variables. For any random direction from the origin, an unbiased estimate of the value of the integral is Pr[X ² ≥ r²] (multivariate normal) or Pr[F ≥ r²/k] (multivariate-t), where r is the distance from the origin to the boundary in a randomly chosen direction, and k is the dimension of the integral. Two Fortran 90 programs have been written. MVI uses the average of many estimates. MVIB uses a binning procedure to obtain an empirical distribution of the distance from the origin to the boundary. Gauss-Legendre quadrature is then used to estimate the value of the integral. The running time for MVIB is modestly smaller than that for MVI. However, in solving certain integral equations (e.g., using an iterative procedure to find the percentage point of a statistic), using MVIB usually requires no Monte Carlo sampling after the first iteration, and is considerably more efficient. MVIB and MVI are highly efficient for the evaluation of integrals whose value is large. “Naive” Monte Carlo (MC) may be competitive with MVI or MVIB only if the value of the probability integral is small or the shape of the region is “extreme.”