Relevant bibliographies by topics / Regression Monte-Carlo scheme

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Regression Monte-Carlo scheme'

Author: Grafiati
Published: 1 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Regression Monte-Carlo scheme"

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Izydorczyk, Lucas, Nadia Oudjane, and Francesco Russo. "A fully backward representation of semilinear PDEs applied to the control of thermostatic loads in power systems." Monte Carlo Methods and Applications 27, no. 4 (2021): 347–71. http://dx.doi.org/10.1515/mcma-2021-2095.

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Abstract We propose a fully backward representation of semilinear PDEs with application to stochastic control. Based on this, we develop a fully backward Monte-Carlo scheme allowing to generate the regression grid, backwardly in time, as the value function is computed. This offers two key advantages in terms of computational efficiency and memory. First, the grid is generated adaptively in the areas of interest, and second, there is no need to store the entire grid. The performances of this technique are compared in simulations to the traditional Monte-Carlo forward-backward approach on a cont
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Wang, Qisen, Junhan Hou, and Zihe Zhong. "Optimization of Crop Planting Strategies Based on Monte Carlo Simulation and Greedy Algorithm." Frontiers in Computing and Intelligent Systems 11, no. 1 (2025): 73–79. https://doi.org/10.54097/5as5pd83.

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Based on Monte Carlo simulation and greedy algorithm, this paper explores the optimization scheme of crop planting under different conditions. First, for the planting scheme under stable market conditions, this paper establishes an objective planning model to optimize the planting plan from 2024 to 2030 by maximizing the annual planting revenue and combining the constraints of various economic indicators. Further, considering that part of the crop may be stagnant or sold at a discounted price, this paper explores the impacts of different sales scenarios on the planting strategy. Then, for the
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Folashade Adeola Bolarinwa, Olusola Samuel Makinde, and Olusoga Akin Fasoranbaku. "A new Bayesian ridge estimator for logistic regression in the presence of multicollinearity." World Journal of Advanced Research and Reviews 20, no. 3 (2023): 458–65. http://dx.doi.org/10.30574/wjarr.2023.20.3.2415.

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This research introduces the Bayesian schemes for estimating logistic regression parameters in the presence of multicollinearity. The Bayesian schemes involve the introduction of a prior together with the likelihood which resulted in the posterior distribution that is not tractable, hence the use of a numerical method i.e Gibbs sampler. Different levels of multicollinearity were chosen to be p = 0.80‚0.85‚0.90‚0.95‚0.99and 0.999to accommodate severe, very severe and nearly perfect state of multicollinearity with sample sizes taken as 10,20,30,50,100,200,300 and 500.Different ridge parameters k
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Folashade, Adeola Bolarinwa, Samuel Makinde Olusola, and Akin Fasoranbaku Olusoga. "A new Bayesian ridge estimator for logistic regression in the presence of multicollinearity." World Journal of Advanced Research and Reviews 20, no. 3 (2023): 458–65. https://doi.org/10.5281/zenodo.12739995.

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This research introduces the Bayesian schemes for estimating logistic regression parameters in the presence of multicollinearity. The Bayesian schemes involve the introduction of a prior together with the likelihood which resulted in the posterior distribution that is not tractable, hence the use of a numerical method i.e Gibbs sampler. Different levels of multicollinearity were chosen to be&nbsp;p&nbsp;=&nbsp;0.80&sbquo;0.85&sbquo;0.90&sbquo;0.95&sbquo;0.99<em>and&nbsp;</em><em>0</em><em>.</em><em>9</em><em>9</em><em>9</em>to accommodate severe, very severe and nearly perfect state of multico
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López-Salas, José Germán, Emmanuel Gobet, Plamen Turkedjiev, and Carlos Vázquez. "Stratified Regression Monte-Carlo Scheme for Semilinear PDEs and BSDEs with Large Scale Parallelization on GPUs." SIAM Journal on Scientific Computing 38, no. 6 (2016): C652—C677. https://doi.org/10.1137/16M106371X.

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In this paper, we design a novel algorithm based on least-squares Monte-Carlo (LSMC) in order to approximate the solution of discrete time backward stochastic differential equations (BSDEs). Our algorithm allows massive parallelization of the computations on many core processors such as graphics processing units (GPUs). Our approach consists of a novel method of stratification which appears to be crucial for large scale parallelization. In this way, we minimize the exposure to the memory requirements due to the storage of simulations. Indeed, we note the lower memory overhead of the method com
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Gobet, E., J. G. López-Salas, P. Turkedjiev, and C. Vázquez. "Stratified Regression Monte-Carlo Scheme for Semilinear PDEs and BSDEs with Large Scale Parallelization on GPUs." SIAM Journal on Scientific Computing 38, no. 6 (2016): C652—C677. http://dx.doi.org/10.1137/16m106371x.

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Trinchero, Riccardo, and Flavio Canavero. "Use of an Active Learning Strategy Based on Gaussian Process Regression for the Uncertainty Quantification of Electronic Devices." Engineering Proceedings 3, no. 1 (2020): 3. http://dx.doi.org/10.3390/iec2020-06967.

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This paper presents a preliminary version of an active learning (AL) scheme for the sample selection aimed at the development of a surrogate model for the uncertainty quantification based on the Gaussian process regression. The proposed AL strategy iteratively searches for new candidate points to be included within the training set by trying to minimize the relative posterior standard deviation provided by the Gaussian process regression surrogate. The above scheme has been applied for the construction of a surrogate model for the statistical analysis of the efficiency of a switching buck conv
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Gobet, Emmanuel, José Germán López-Salas, and Carlos Vázquez. "Quasi-Regression Monte-Carlo Scheme for Semi-Linear PDEs and BSDEs with Large Scale Parallelization on GPUs." Archives of Computational Methods in Engineering 27 (April 4, 2019): 889–921. https://doi.org/10.1007/s11831-019-09335-x.

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In this article we design a novel quasi-regression Monte Carlo algorithm in order to approximate the solution of discrete time backward stochastic differential equations, and we analyze the convergence of the proposed method. The algorithm also approximates the solution to the related semi-linear parabolic partial differential equation obtained through the well known Feynman&ndash;Kac representation. For the sake of enriching the algorithm with high order convergence a weighted approximation of the solution is computed and appropriate conditions on the parameters of the method are inferred. Wi
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Gobet, Emmanuel, José Germán López-Salas, and Carlos Vázquez. "Quasi-Regression Monte-Carlo Scheme for Semi-Linear PDEs and BSDEs with Large Scale Parallelization on GPUs." Archives of Computational Methods in Engineering 27, no. 3 (2019): 889–921. http://dx.doi.org/10.1007/s11831-019-09335-x.

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Khan, Sajid Ali, Sayyad Khurshid, Shabnam Arshad, and Owais Mushtaq. "Bias Estimation of Linear Regression Model with Autoregressive Scheme using Simulation Study." Journal of Mathematical Analysis and Modeling 2, no. 1 (2021): 26–39. http://dx.doi.org/10.48185/jmam.v2i1.131.

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In regression modeling, first-order auto correlated errors are often a problem, when the data also suffers from independent variables. Generalized Least Squares (GLS) estimation is no longer the best alternative to Ordinary Least Squares (OLS). The Monte Carlo simulation illustrates that regression estimation using data transformed according to the GLS method provides estimates of the regression coefficients which are superior to OLS estimates. In GLS, we observe that in sample size $200$ and $\sigma$=3 with correlation level $0.90$ the bias of GLS $\beta_0$ is $-0.1737$, which is less than al
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Dissertations / Theses on the topic "Regression Monte-Carlo scheme"

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Min, Ming. "Numerical Methods for European Option Pricing with BSDEs." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1169.

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This paper aims to calculate the all-inclusive European option price based on XVA model numerically. For European type options, the XVA can be calculated as so- lution of a BSDE with a specific driver function. We use the FT scheme to find a linear approximation of the nonlinear BSDE and then use linear regression Monte Carlo method to calculate the option price.
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Izydorczyk, Lucas. "Probabilistic backward McKean numerical methods for PDEs and one application to energy management." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAE008.

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Cette thèse s'intéresse aux équations différentielles stochastiques de type McKean(EDS) et à leur utilisation pour représenter des équations aux dérivées partielles (EDP) non linéaires. Ces équations ne dépendent pas seulement du temps et de la position d'une certaine particule mais également de sa loi. En particulier nous traitons le cas inhabituel de la représentation d'EDP de type Fokker-Planck avec condition terminale fixée. Nous discutons existence et unicité pour ces EDP et de leur représentation sous la forme d'une EDS de type McKean, dont l'unique solutioncorrespond à la dynamique du r
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Books on the topic "Regression Monte-Carlo scheme"

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Sobczyk, Eugeniusz Jacek. Uciążliwość eksploatacji złóż węgla kamiennego wynikająca z warunków geologicznych i górniczych. Instytut Gospodarki Surowcami Mineralnymi i Energią PAN, 2022. http://dx.doi.org/10.33223/onermin/0222.

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Hard coal mining is characterised by features that pose numerous challenges to its current operations and cause strategic and operational problems in planning its development. The most important of these include the high capital intensity of mining investment projects and the dynamically changing environment in which the sector operates, while the long-term role of the sector is dependent on factors originating at both national and international level. At the same time, the conditions for coal mining are deteriorating, the resources more readily available in active mines are being exhausted, m
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Book chapters on the topic "Regression Monte-Carlo scheme"

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Habyarimana, Ephrem, and Sofia Michailidou. "Genomic Prediction and Selection in Support of Sorghum Value Chains." In Big Data in Bioeconomy. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_16.

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AbstractGenomic prediction and selection models (GS) were deployed as part of DataBio project infrastructure and solutions. The work addressed end-user requirements, i.e., the need for cost-effectiveness of the implemented technologies, simplified breeding schemes, and shortening the time to cultivar development by selecting for genetic merit. Our solutions applied genomic modelling in order to sustainably improve productivity and profits. GS models were implemented in sorghum crop for several breeding scenarios. We fitted the best linear unbiased predictions data using Bayesian ridge regressi
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Yoshida, Ruriko, Hisayuki Hara, and Patrick M. Saluke. "Sequential Importance Sampling for Logistic Regression Model." In Computational Models for Biomedical Reasoning and Problem Solving. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7467-5.ch009.

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Logistic regression is one of the most popular models to classify in data science, and in general, it is easy to use. However, in order to conduct a goodness-of-fit test, we cannot apply asymptotic methods if we have sparse datasets. In the case, we have to conduct an exact conditional inference via a sampler, such as Markov Chain Monte Carlo (MCMC) or Sequential Importance Sampling (SIS). In this chapter, the authors investigate the rejection rate of the SIS procedure on a multiple logistic regression models with categorical covariates. Using tools from algebra, they show that in general SIS
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Conference papers on the topic "Regression Monte-Carlo scheme"

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Pidaparthi, Bharath, and Samy Missoum. "A Multi-Fidelity Approach for Reliability Assessment Based on the Probability of Model Inconsistency." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-90115.

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Abstract Most multi-fidelity schemes rely on regression surrogates, such as Gaussian Processes, to combine low- and high-fidelity data. Contrary to these approaches, we propose a classification-based multi-fidelity scheme for reliability assessment. This multi-fidelity technique leverages low- and high-fidelity model evaluations to locally construct the failure boundaries using support vector machine (SVM) classifiers. These SVMs can subsequently be used to estimate the probability of failure using Monte Carlo Simulations. At the core of this multi-fidelity scheme is an adaptive sampling routi
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Chao, Manuel Arias, Darrel S. Lilley, Peter Mathé, and Volker Schloßhauer. "Calibration and Uncertainty Quantification of Gas Turbine Performance Models." In ASME Turbo Expo 2015: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/gt2015-42392.

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Calibration and uncertainty quantification for gas turbine (GT) performance models is a key activity for GT manufacturers. The adjustment between the numerical model and measured GT data is obtained with a calibration technique. Since both, the calibration parameters and the measurement data are uncertain the calibration process is intrinsically stochastic. Traditional approaches for calibration of a numerical GT model are deterministic. Therefore, quantification of the remaining uncertainty of the calibrated GT model is not clearly derived. However, there is the business need to provide the p
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