Monte Carlo Methods and Applications最新文献_第8页

Monte Carlo Methods: A Hands-On Computational Introduction Utilizing Excel 蒙特卡罗方法:利用Excel的动手计算入门

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Monte Carlo Methods and Applications Pub Date : 2021-02-22 DOI: 10.2200/S01073ED1V01Y202101MAS037

S. Chowdhury

引用次数: 0

High order weak approximation for irregular functionals of time-inhomogeneous SDEs 时间非齐次SDE不规则泛函的高阶弱逼近

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Monte Carlo Methods and Applications Pub Date : 2021-02-20 DOI: 10.1515/MCMA-2021-2085

T. Yamada

引用次数: 3

On the absorption probabilities and mean time for absorption for discrete Markov chains 离散Markov链的吸收概率和平均吸收时间

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Monte Carlo Methods and Applications Pub Date : 2021-02-02 DOI: 10.1515/MCMA-2021-2084

N. Halidias

引用次数: 0

Sensitivity analysis of stochastic frontier analysis models 随机前沿分析模型的敏感性分析

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Monte Carlo Methods and Applications Pub Date : 2021-02-02 DOI: 10.1515/mcma-2021-2083

Kekoura Sakouvogui, Saleem Shaik, C. Doetkott, R. Magel

{"title":"Sensitivity analysis of stochastic frontier analysis models","authors":"Kekoura Sakouvogui, Saleem Shaik, C. Doetkott, R. Magel","doi":"10.1515/mcma-2021-2083","DOIUrl":"https://doi.org/10.1515/mcma-2021-2083","url":null,"abstract":"Abstract The efficiency measures of the Stochastic Frontier Analysis (SFA) models are dependent on distributional assumptions of the one-sided error or inefficiency term. Given the intent of earlier researchers in the evaluation of a single inefficiency distribution using Monte Carlo (MC) simulation, much attention has not been paid to the comparative analysis of SFA models. Our paper aims to evaluate the effects of the assumption of the inefficiency distribution and thus compares different SFA model assumptions by conducting a MC simulation. In this paper, we derive the population statistical parameters of truncated normal, half-normal, and exponential inefficiency distributions of SFA models with the objective of having comparable sample mean and sample standard deviation during MC simulation. Thus, MC simulation is conducted to evaluate the statistical properties and robustness of the inefficiency distributions of SFA models and across three different misspecification scenarios, sample sizes, production functions, and input distributions. MC simulation results show that the misspecified truncated normal SFA model provides the smallest mean absolute deviation and mean square error when the true data generating process is a half-normal inefficiency distribution.","PeriodicalId":46576,"journal":{"name":"Monte Carlo Methods and Applications","volume":"27 1","pages":"71 - 90"},"PeriodicalIF":0.9,"publicationDate":"2021-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/mcma-2021-2083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46281378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Body tail adaptive kernel density estimation for nonnegative heavy-tailed data 非负重尾数据的体尾自适应核密度估计

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Monte Carlo Methods and Applications Pub Date : 2021-02-02 DOI: 10.1515/mcma-2021-2082

Y. Ziane, N. Zougab, S. Adjabi

引用次数: 2

A note on transformed Fourier systems for the approximation of non-periodic signals 关于非周期信号近似的变换傅立叶系统的注释

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Monte Carlo Methods and Applications Pub Date : 2021-02-01 DOI: 10.1007/978-3-030-98319-2_13

Robert Nasdala, D. Potts

引用次数: 2

Automatic control variates for option pricing using neural networks 基于神经网络的期权定价自动控制变量

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Monte Carlo Methods and Applications Pub Date : 2021-01-13 DOI: 10.1515/MCMA-2020-2081

Zineb El Filali Ech-Chafiq, J. Lelong, A. Reghai

{"title":"Automatic control variates for option pricing using neural networks","authors":"Zineb El Filali Ech-Chafiq, J. Lelong, A. Reghai","doi":"10.1515/MCMA-2020-2081","DOIUrl":"https://doi.org/10.1515/MCMA-2020-2081","url":null,"abstract":"Abstract Many pricing problems boil down to the computation of a high-dimensional integral, which is usually estimated using Monte Carlo. In fact, the accuracy of a Monte Carlo estimator with M simulations is given by σM{frac{sigma}{sqrt{M}}}. Meaning that its convergence is immune to the dimension of the problem. However, this convergence can be relatively slow depending on the variance σ of the function to be integrated. To resolve such a problem, one would perform some variance reduction techniques such as importance sampling, stratification, or control variates. In this paper, we will study two approaches for improving the convergence of Monte Carlo using Neural Networks. The first approach relies on the fact that many high-dimensional financial problems are of low effective dimensions. We expose a method to reduce the dimension of such problems in order to keep only the necessary variables. The integration can then be done using fast numerical integration techniques such as Gaussian quadrature. The second approach consists in building an automatic control variate using neural networks. We learn the function to be integrated (which incorporates the diffusion model plus the payoff function) in order to build a network that is highly correlated to it. As the network that we use can be integrated exactly, we can use it as a control variate.","PeriodicalId":46576,"journal":{"name":"Monte Carlo Methods and Applications","volume":"27 1","pages":"91 - 104"},"PeriodicalIF":0.9,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/MCMA-2020-2081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43144945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Bayesian estimation of a competing risk model based on Weibull and exponential distributions under right censored data 基于威布尔分布和指数分布的竞争风险模型的贝叶斯估计

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Monte Carlo Methods and Applications Pub Date : 2021-01-10 DOI: 10.1515/mcma-2022-2112

H. Talhi, H. Aiachi, N. Rahmania

引用次数: 1

Controlled accuracy Gibbs sampling of order-constrained non-iid ordered random variates 阶约束非iid有序随机变量的控制精度Gibbs抽样

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Monte Carlo Methods and Applications Pub Date : 2020-12-31 DOI: 10.1515/mcma-2022-2121

J. Corcoran, Caleb Miller

{"title":"Controlled accuracy Gibbs sampling of order-constrained non-iid ordered random variates","authors":"J. Corcoran, Caleb Miller","doi":"10.1515/mcma-2022-2121","DOIUrl":"https://doi.org/10.1515/mcma-2022-2121","url":null,"abstract":"Abstract Order statistics arising from 𝑚 independent but not identically distributed random variables are typically constructed by arranging some X 1 , X 2 , … , X m X_{1},X_{2},ldots,X_{m} , with X i X_{i} having distribution function F i ⁢ ( x ) F_{i}(x) , in increasing order denoted as X ( 1 ) ≤ X ( 2 ) ≤ ⋯ ≤ X ( m ) X_{(1)}leq X_{(2)}leqcdotsleq X_{(m)} . In this case, X ( i ) X_{(i)} is not necessarily associated with F i ⁢ ( x ) F_{i}(x) . Assuming one can simulate values from each distribution, one can generate such “non-iid” order statistics by simulating X i X_{i} from F i F_{i} , for i = 1 , 2 , … , m i=1,2,ldots,m , and arranging them in order. In this paper, we consider the problem of simulating ordered values X ( 1 ) , X ( 2 ) , … , X ( m ) X_{(1)},X_{(2)},ldots,X_{(m)} such that the marginal distribution of X ( i ) X_{(i)} is F i ⁢ ( x ) F_{i}(x) . This problem arises in Bayesian principal components analysis (BPCA) where the X i X_{i} are ordered eigenvalues that are a posteriori independent but not identically distributed. We propose a novel coupling-from-the-past algorithm to “perfectly” (up to computable order of accuracy) simulate such order-constrained non-iid order statistics. We demonstrate the effectiveness of our approach for several examples, including the BPCA problem.","PeriodicalId":46576,"journal":{"name":"Monte Carlo Methods and Applications","volume":"28 1","pages":"279 - 292"},"PeriodicalIF":0.9,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45372736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the Distribution of Scrambled (0, m, s)-Nets Over Unanchored Boxes 乱码(0,m, s)网在无锚箱上的分布

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Monte Carlo Methods and Applications Pub Date : 2020-12-18 DOI: 10.1007/978-3-030-98319-2_5

C. Lemieux, J. Wiart

引用次数: 2