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

Author: Grafiati

Published: 1 June 2024

Last updated: 31 July 2025

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1

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 p = 0.80&sbquo;0.85&sbquo;0.90&sbquo;0.95&sbquo;0.99<em>and </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–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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Tobias, Justin L. "Adaptive Bayesian Nonparametric Regression via Stationary Smoothness Priors." Mathematics 13, no. 7 (2025): 1162. https://doi.org/10.3390/math13071162.

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A procedure for Bayesian nonparametric regression is described that automatically adjusts the degree of smoothing as the curvature of the underlying function changes. Relative to previous work adopting a similar approach that either employs a single global smoothing parameter or assumes that the smoothing process follows a random walk, the model considered here permits adaptive smoothing and imposes stationarity in the autoregressive smoothing process. An efficient Markov Chain Monte Carlo (MCMC) scheme for model estimation is fully described for this stationary case, and the performance of th

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Wang, Han, Lingwei Xu, and Xianpeng Wang. "Outage Probability Performance Prediction for Mobile Cooperative Communication Networks Based on Artificial Neural Network." Sensors 19, no. 21 (2019): 4789. http://dx.doi.org/10.3390/s19214789.

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This paper investigates outage probability (OP) performance predictions using transmit antenna selection (TAS) and derives exact closed-form OP expressions for a TAS scheme. It uses Monte-Carlo simulations to evaluate OP performance and verify the analysis. A back-propagation (BP) neural network-based OP performance prediction algorithm is proposed and compared with extreme learning machine (ELM), locally weighted linear regression (LWLR), support vector machine (SVM), and BP neural network methods. The proposed method was found to have higher OP performance prediction results than the other p

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Seo, Jung-In, Young Eun Jeon, and Suk-Bok Kang. "New Approach for a Weibull Distribution under the Progressive Type-II Censoring Scheme." Mathematics 8, no. 10 (2020): 1713. http://dx.doi.org/10.3390/math8101713.

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This paper proposes a new approach based on the regression framework employing a pivotal quantity to estimate unknown parameters of a Weibull distribution under the progressive Type-II censoring scheme, which provides a closed form solution for the shape parameter, unlike its maximum likelihood estimator counterpart. To resolve serious rounding errors for the exact mean and variance of the pivotal quantity, two different types of Taylor series expansion are applied, and the resulting performance is enhanced in terms of the mean square error and bias obtained through the Monte Carlo simulation.

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MORALES, MARÍA, CARMELO RODRÍGUEZ, and ANTONIO SALMERÓN. "SELECTIVE NAIVE BAYES FOR REGRESSION BASED ON MIXTURES OF TRUNCATED EXPONENTIALS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15, no. 06 (2007): 697–716. http://dx.doi.org/10.1142/s0218488507004959.

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Naive Bayes models have been successfully used in classification problems where the class variable is discrete. These models have also been applied to regression or prediction problems, i.e. classification problems where the class variable is continuous, but usually under the assumption that the joint distribution of the feature variables and the class is multivariate Gaussian. In this paper we are interested in regression problems where some of the feature variables are discrete while the others are continuous. We propose a Naive Bayes predictor based on the approximation of the joint distrib

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Ma, Zhi-Sai, Li Liu, Si-Da Zhou, and Lei Yu. "Output-Only Modal Parameter Recursive Estimation of Time-Varying Structures via a Kernel Ridge Regression FS-TARMA Approach." Shock and Vibration 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/8176593.

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Modal parameter estimation plays an important role in vibration-based damage detection and is worth more attention and investigation, as changes in modal parameters are usually being used as damage indicators. This paper focuses on the problem of output-only modal parameter recursive estimation of time-varying structures based upon parameterized representations of the time-dependent autoregressive moving average (TARMA). A kernel ridge regression functional series TARMA (FS-TARMA) recursive identification scheme is proposed and subsequently employed for the modal parameter estimation of a nume

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Sun, Mingjie, Yulu Dai, and Jingyi Wang. "Research on Natural Disaster Risk Based on Garch Volatility and Monte Carlo Algorithm." Highlights in Business, Economics and Management 45 (December 28, 2024): 770–77. https://doi.org/10.54097/gxckff25.

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The purpose of this paper is to study the optimal decision problem of different agents. Firstly, the expression of the profit process of the insurance company is established, and then the loss caused by the natural disaster is fitted into the insurance formula using the double cross-verified natural disaster model. The first is the prediction model of random disaster loss based on Garch volatility test. The other is ridge regression, which combines the possible relevant factors and then makes the forecast. The two results were cross-verified by Garch volatility test again, and then risk assess

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Jiang, Nan, and Yexiang Xue. "Racing Control Variable Genetic Programming for Symbolic Regression." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (2024): 12901–9. http://dx.doi.org/10.1609/aaai.v38i11.29187.

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Symbolic regression, as one of the most crucial tasks in AI for science, discovers governing equations from experimental data. Popular approaches based on genetic programming, Monte Carlo tree search, or deep reinforcement learning learn symbolic regression from a fixed dataset. These methods require massive datasets and long training time especially when learning complex equations involving many variables. Recently, Control Variable Genetic Programming (CVGP) has been introduced which accelerates the regression process by discovering equations from designed control variable experiments. Howev

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Jung, Jihyeok, Chan-Oi Song, Deok-Joo Lee, and Kiho Yoon. "Optimal Mechanism in a Dynamic Stochastic Knapsack Environment." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 9 (2024): 9807–14. http://dx.doi.org/10.1609/aaai.v38i9.28840.

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This study introduces an optimal mechanism in a dynamic stochastic knapsack environment. The model features a single seller who has a fixed quantity of a perfectly divisible item. Impatient buyers with a piece-wise linear utility function arrive randomly and they report the two-dimensional private information: marginal value and demanded quantity. We derive a revenue-maximizing dynamic mechanism in a finite discrete time framework that satisfies incentive compatibility, individual rationality, and feasibility conditions. This is achieved by characterizing buyers' utility and utilizing the Bell

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Wu, Hsiao-Chun, Shih Yu Chang, Tho Le-Ngoc, and Yiyan Wu. "Efficient Rank-Adaptive Least-Square Estimation and Multiple-Parameter Linear Regression Using Novel Dyadically Recursive Hermitian Matrix Inversion." International Journal of Antennas and Propagation 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/891932.

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Least-square estimation (LSE) and multiple-parameter linear regression (MLR) are the important estimation techniques for engineering and science, especially in the mobile communications and signal processing applications. The majority of computational complexity incurred in LSE and MLR arises from a Hermitian matrix inversion. In practice, the Yule-Walker equations are not valid, and hence the Levinson-Durbin algorithm cannot be employed for general LSE and MLR problems. Therefore, the most efficient Hermitian matrix inversion method is based on the Cholesky factorization. In this paper, we de

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Zhang, Yichi, Yangyao Ding, and Panagiotis D. Christofides. "Integrating Feedback Control and Run-to-Run Control in Multi-Wafer Thermal Atomic Layer Deposition of Thin Films." Processes 8, no. 1 (2019): 18. http://dx.doi.org/10.3390/pr8010018.

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There is currently a lack of understanding of the deposition profile in a batch atomic layer deposition (ALD) process. Also, no on-line control scheme has been proposed to resolve the prevalent disturbances. Motivated by this, we develop a computational fluid dynamics (CFD) model and an integrated online run-to-run and feedback control scheme. Specifically, we analyze a furnace reactor for a SiO2 thin-film ALD with BTBAS and ozone as precursors. Initially, a high-fidelity 2D axisymmetric multiscale CFD model is developed using ANSYS Fluent for the gas-phase characterization and the surface thi

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Wang, Tianhao, Quanyi Yu, Xianli Yu, Le Gao, and Huanyu Zhao. "Radiated Susceptibility Analysis of Multiconductor Transmission Lines Based on Polynomial Chaos." Applied Computational Electromagnetics Society 35, no. 12 (2021): 1556–66. http://dx.doi.org/10.47037/2020.aces.j.351215.

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To address the uncertainties of the radiated susceptibility of multiconductor transmission lines (MTLs), a surrogate model of the MTLs radiated susceptibility is established based on generalized polynomial chaos (gPC), and the gPC is made sparser by combining the adaptive hyperbolic truncation (AHT) scheme and the least angle regression (LAR) method. The uncertainties of the radiated susceptibility of transmission lines are calculated using the adaptive-sparse polynomial chaos (AS-PC) scheme. The parameters related to the incident field, such as elevation angle theta, azimuth angle psi, polari

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Gomes, Véronique, Ricardo Rendall, Marco Seabra Reis, Ana Mendes-Ferreira, and Pedro Melo-Pinto. "Determination of Sugar, pH, and Anthocyanin Contents in Port Wine Grape Berries through Hyperspectral Imaging: An Extensive Comparison of Linear and Non-Linear Predictive Methods." Applied Sciences 11, no. 21 (2021): 10319. http://dx.doi.org/10.3390/app112110319.

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This paper presents an extended comparison study between 16 different linear and non-linear regression methods to predict the sugar, pH, and anthocyanin contents of grapes through hyperspectral imaging (HIS). Despite the numerous studies on this subject that can be found in the literature, they often rely on the application of one or a very limited set of predictive methods. The literature on multivariate regression methods is quite extensive, so the analytical domain explored is too narrow to guarantee that the best solution has been found. Therefore, we developed an integrated linear and non

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Ibrahim, Joseph G., Sungduk Kim, Ming-Hui Chen, Arvind K. Shah, and Jianxin Lin. "Bayesian multivariate skew meta-regression models for individual patient data." Statistical Methods in Medical Research 28, no. 10-11 (2018): 3415–36. http://dx.doi.org/10.1177/0962280218801147.

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We examine a class of multivariate meta-regression models in the presence of individual patient data. The methodology is well motivated from several studies of cholesterol-lowering drugs where the goal is to jointly analyze the multivariate outcomes, low density lipoprotein cholesterol, high density lipoprotein cholesterol, and triglycerides. These three continuous outcome measures are correlated and shed much light on a subject's lipid status. One of the main goals in lipid research is the joint analysis of these three outcome measures in a meta-regression setting. Since these outcome measure

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Liu, Manhua, Yangyang Wang, Yueping Jiang, Haitao Liu, Jingjing Chen, and Shao Liu. "Quantitation of Oxcarbazepine Clinically in Plasma Using Surfaced-Enhanced Raman Spectroscopy (SERS) Coupled with Chemometrics." Applied Spectroscopy 73, no. 7 (2019): 801–9. http://dx.doi.org/10.1177/0003702819845389.

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Nondestructive, sensitive, near-real-time quantitative analysis approaches are gaining popularity and attention, especially in clinical diagnosis and detection. There is a need to propose an alternative scheme using surface-enhanced Raman spectroscopy (SERS) assisted by chemometrics to improve some defects existing using other analytical instruments to meet clinical demands. In this study, clinical drug oxcarbazepine (OXC) in human blood plasma has been quantified and detected using this method. Partial least squares regression (PLSR) modeling was employed to assess the relationship between fu

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Shahzad, Usman, Ishfaq Ahmad, Ibrahim Mufrah Almanjahie, and Amer Ibrahim Al-Omari. "Three-fold utilization of supplementary information for mean estimation under median ranked set sampling scheme." PLOS ONE 17, no. 10 (2022): e0276514. http://dx.doi.org/10.1371/journal.pone.0276514.

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Ranked set sampling (RSS) has created a broad interest among researchers and it is still a unique research topic. It has at long last begun to find its way into practical applications beyond its initial horticultural based birth in the fundamental paper by McIntyre in the nineteenth century. One of the extensions of RSS is median ranked set sampling (MRSS). MRSS is a sampling procedure normally utilized when measuring the variable of interest is troublesome or expensive, whereas it might be easy to rank the units using an inexpensive sorting criterion. Several researchers introduced ratio, reg

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Nan, Ruisi, Jingwei Wang, Hanfang Li, and Youxi Luo. "Robust Variable Selection via Bayesian LASSO-Composite Quantile Regression with Empirical Likelihood: A Hybrid Sampling Approach." Mathematics 13, no. 14 (2025): 2287. https://doi.org/10.3390/math13142287.

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Since the advent of composite quantile regression (CQR), its inherent robustness has established it as a pivotal methodology for high-dimensional data analysis. High-dimensional outlier contamination refers to data scenarios where the number of observed dimensions (p) is much greater than the sample size (n) and there are extreme outliers in the response variables or covariates (e.g., p/n > 0.1). Traditional penalized regression techniques, however, exhibit notable vulnerability to data outliers during high-dimensional variable selection, often leading to biased parameter estimates and comp

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Virtanen, Arja, Veli Kairisto, and Esa Uusipaikka. "Regression-based reference limits: determination of sufficient sample size." Clinical Chemistry 44, no. 11 (1998): 2353–58. http://dx.doi.org/10.1093/clinchem/44.11.2353.

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Abstract Regression analysis is the method of choice for the production of covariate-dependent reference limits. There are currently no recommendations on what sample size should be used when regression-based reference limits and confidence intervals are calculated. In this study we used Monte Carlo simulation to study a reference sample group of 374 age-dependent hemoglobin values. From this sample, 5000 random subsamples, with replacement, were constructed with 10–220 observations per sample. Regression analysis was used to estimate age-dependent 95% reference intervals for hemoglobin concen

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Wei, Zhichun. "Crop planting strategy based on multi-objective integer programming." Advances in Engineering Technology Research 13, no. 1 (2025): 1090. https://doi.org/10.56028/aetr.13.1.1090.2025.

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The decision-making for crop planting is key to the efficient and sustainable development of agriculture. Based on the analysis of influencing factors, a multi-objective integer programming model is established, and different planting strategies are proposed under various circumstances using a genetic algorithm for solution. For scenario one, preprocess the data, fill in missing values, and analyze trends. Based on revenue and sustainable planting factors, establish a multi-objective integer programming model, considering factors such as sales volume, costs, yield per mu, prices, and the hazar

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Liang, Xitong, Samuel Livingstone, and Jim Griffin. "Adaptive MCMC for Bayesian Variable Selection in Generalised Linear Models and Survival Models." Entropy 25, no. 9 (2023): 1310. http://dx.doi.org/10.3390/e25091310.

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Developing an efficient computational scheme for high-dimensional Bayesian variable selection in generalised linear models and survival models has always been a challenging problem due to the absence of closed-form solutions to the marginal likelihood. The Reversible Jump Markov Chain Monte Carlo (RJMCMC) approach can be employed to jointly sample models and coefficients, but the effective design of the trans-dimensional jumps of RJMCMC can be challenging, making it hard to implement. Alternatively, the marginal likelihood can be derived conditional on latent variables using a data-augmentatio

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Gao, Yuanyuan, Na Liu, Peng Liu та Chengnuo Wang. "Prediction of stamping parameters for imitation π-shaped lithium battery shells by building variable weight and threshold pelican-BP neural networks". Advances in Mechanical Engineering 14, № 9 (2022): 168781322211122. http://dx.doi.org/10.1177/16878132221112203.

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With the rapid development of artificial neural networks, more sophisticated network models and more accurate prediction results are provided for solving engineering applications. In this paper, the weights and thresholds of the feedforward neural network model were optimized using the pelican algorithm, and the optimal solution was output by simulating the pelican predation scheme and assigned as the new parameters of the neural network. A POA-BP network model was proposed, and its better prediction was demonstrated by comparing the fitting and prediction performance with 13 neural network mo

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La Rocca, Michele, and Cira Perna. "Opening the Black Box: Bootstrapping Sensitivity Measures in Neural Networks for Interpretable Machine Learning." Stats 5, no. 2 (2022): 440–57. http://dx.doi.org/10.3390/stats5020026.

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Artificial neural networks are powerful tools for data analysis, particularly in the context of highly nonlinear regression models. However, their utility is critically limited due to the lack of interpretation of the model given its black-box nature. To partially address the problem, the paper focuses on the important problem of feature selection. It proposes and discusses a statistical test procedure for selecting a set of input variables that are relevant to the model while taking into account the multiple testing nature of the problem. The approach is within the general framework of sensit

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La Rocca, Michele, and Cira Perna. "Opening the Black Box: Bootstrapping Sensitivity Measures in Neural Networks for Interpretable Machine Learning." Stats 5, no. 2 (2022): 440–57. http://dx.doi.org/10.3390/stats5020026.

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Artificial neural networks are powerful tools for data analysis, particularly in the context of highly nonlinear regression models. However, their utility is critically limited due to the lack of interpretation of the model given its black-box nature. To partially address the problem, the paper focuses on the important problem of feature selection. It proposes and discusses a statistical test procedure for selecting a set of input variables that are relevant to the model while taking into account the multiple testing nature of the problem. The approach is within the general framework of sensit

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Faristasari, Selvi, and Adhitya Ronnie Effendie. "Application of Simulated Annealing Method on Tabarru-Fund Valuation using Inflator by Vasicek Model Approach Based on Profit and Loss Sharing Scheme." Indonesian Journal of Mathematics and Applications 1, no. 1 (2023): 24–36. http://dx.doi.org/10.21776/ub.ijma.2023.001.01.4.

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Currently, the financial services industry is dominated by conventional banks and individuals that apply the system of interest or an excess of loans. In Islam, this excess is referred to as usury, which is prohibited by Islamic law because, in practice, usury makes borrowers poorer as they cannot pay such high-interest installments. Not to mention, late payments are subject to penalties that will continue to accumulate if the borrower is unable to pay the next installment. From these facts, this system is prohibited by Islamic Law because there are harmed parties. Therefore, this research dis

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Habeck, Christian, Qolamreza Razlighi, and Yaakov Stern. "Predictive utility of task-related functional connectivity vs. voxel activation." PLOS ONE 16, no. 4 (2021): e0249947. http://dx.doi.org/10.1371/journal.pone.0249947.

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Functional connectivity, both in resting state and task performance, has steadily increased its share of neuroimaging research effort in the last 1.5 decades. In the current study, we investigated the predictive utility regarding behavioral performance and task information for 240 participants, aged 20–77, for both voxel activation and functional connectivity in 12 cognitive tasks, belonging to 4 cognitive reference domains (Episodic Memory, Fluid Reasoning, Perceptual Speed, and Vocabulary). We also added a model only comprising brain-structure information not specifically acquired during per

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Meneylyuk, O. I., and O. L. Nikiforov. "METHODOLOGY DEVELOPMENT FOR EFFICIENCY JUSTIFICATION OF CONSTRUCTION MANAGEMENT TEMPLATES." Ukrainian Journal of Civil Engineering and Architecture, no. 6 (024) (December 27, 2024): 98–108. https://doi.org/10.30838/ujcea.2312.271224.98.1117.

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The relevance of the work. In the context of ongoing military aggression, the necessary reconstruction can be implemented in a diverse organizational environment, usually formed without the use of management methods standards. Accordingly, there is a need to substantiate the effectiveness and introduce an information-communication concept that explains how to perform construction management, in the presence of modern information-communication technologies. Justification of the effectiveness of these tools utilization in the conditions of Ukraine construction industry is an urgent task, because

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Gianola, Daniel, and Rohan L. Fernando. "A Multiple-Trait Bayesian Lasso for Genome-Enabled Analysis and Prediction of Complex Traits." Genetics 214, no. 2 (2019): 305–31. http://dx.doi.org/10.1534/genetics.119.302934.

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A multiple-trait Bayesian LASSO (MBL) for genome-based analysis and prediction of quantitative traits is presented and applied to two real data sets. The data-generating model is a multivariate linear Bayesian regression on possibly a huge number of molecular markers, and with a Gaussian residual distribution posed. Each (one per marker) of the T×1 vectors of regression coefficients (T: number of traits) is assigned the same T−variate Laplace prior distribution, with a null mean vector and unknown scale matrix Σ. The multivariate prior reduces to that of the standard univariate Bayesian LASSO

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Prasanna, K., Mudassir Khan, Saeed M. Alshahrani, et al. "Continual Learning Approach for Continuous Data Stream Analysis in Dynamic Environments." Applied Sciences 13, no. 14 (2023): 8004. http://dx.doi.org/10.3390/app13148004.

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Continuous data stream analysis primarily focuses on the unanticipated changes in the transmission of data distribution over time. Conceptual change is defined as the signal distribution changes over the transmission of continuous data streams. A drift detection scenario is set forth to develop methods and strategies for detecting, interpreting, and adapting to conceptual changes over data streams. Machine learning approaches can produce poor learning outcomes in the conceptual change environment if the sudden change is not addressed. Furthermore, due to developments in concept drift, learning

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Kalinina, Irina A., and Aleksandr P. Gozhyj. "Modeling and forecasting of nonlinear nonstationary processes based on the Bayesian structural time series." Applied Aspects of Information Technology 5, no. 3 (2022): 240–55. http://dx.doi.org/10.15276/aait.05.2022.17.

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The article describes an approach to modelling and forecasting non-linear non-stationary time series for various purposes using Bayesian structural time series. The concepts of non-linearity and non-stationarity, as well as methods for processing non-linearity’sand non-stationarity in the construction of forecasting models are considered. The features of the Bayesian approach in the processing of nonlinearities and nonstationaryare presented. An approach to the construction of probabilistic-statistical models based on Bayesian structural models of time series has been studied. Parametric and n

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Barrera, David, Stéphane Crépey, Babacar Diallo, Gersende Fort, Emmanuel Gobet, and Uladzislau Stazhynski. "Stochastic approximation schemes for economic capital and risk margin computations." ESAIM: Proceedings and Surveys 65 (2019): 182–218. http://dx.doi.org/10.1051/proc/201965182.

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We consider the problem of the numerical computation of its economic capital by an insurance or a bank, in the form of a value-at-risk or expected shortfall of its loss over a given time horizon. This loss includes the appreciation of the mark-to-model of the liabilities of the firm, which we account for by nested Monte Carlo à la Gordy and Juneja [17] or by regression à la Broadie, Du, and Moallemi [10]. Using a stochastic approximation point of view on value-at-risk and expected shortfall, we establish the convergence of the resulting economic capital simulation schemes, under mild assumptio

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YEASIN, M., K. N. SINGH, A. LAMA, and B. GURUNG. "Improved weather indices based Bayesian regression model for forecasting crop yield." MAUSAM 72, no. 4 (2021): 879–86. http://dx.doi.org/10.54302/mausam.v72i4.3542.

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As agriculture is the backbone of the Indian economy, Government needs a reliable forecast of crop yield for planning new schemes. The most extensively used technique for forecasting crop yield is regression analysis. The significance of parameters is one of the major problems of regression analysis. Non-significant parameters lead to absurd forecast values and these forecast values are not reliable. In such cases, models need to be improved. To improve the models, we have incorporated prior knowledge through the Bayesian technique and investigate the superiority of these models under the Baye

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YEASIN, M., K. N. SINGH, A. LAMA, and B. GURUNG. "Improved weather indices based Bayesian regression model for forecasting crop yield." MAUSAM 72, no. 4 (2021): 879–86. http://dx.doi.org/10.54302/mausam.v72i4.670.

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As agriculture is the backbone of the Indian economy, Government needs a reliable forecast of crop yield for planning new schemes. The most extensively used technique for forecasting crop yield is regression analysis. The significance of parameters is one of the major problems of regression analysis. Non-significant parameters lead to absurd forecast values and these forecast values are not reliable. In such cases, models need to be improved. To improve the models, we have incorporated prior knowledge through the Bayesian technique and investigate the superiority of these models under the Baye

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Liu, Qian, Xufang Zhang, and Xianzhen Huang. "A sparse surrogate model for structural reliability analysis based on the generalized polynomial chaos expansion." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 233, no. 3 (2018): 487–502. http://dx.doi.org/10.1177/1748006x18804047.

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The reliability analysis of a structural system is typically evaluated based on a multivariate model that describes the underlying mechanistic relationship between the system’s input and output random variables. This is the need to develop an effective surrogate model to mimic the input–output relationship as the Monte Carlo simulation–based on the mechanistic model might be computationally intensive. In this regard, the article presents a sparse regression method for structural reliability analysis based on the generalized polynomial chaos expansion. However, results from the global sensitivi

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Ramnath, Vishal. "Comparison of straight line curve fit approaches for determining parameter variances and covariances." International Journal of Metrology and Quality Engineering 11 (2020): 14. http://dx.doi.org/10.1051/ijmqe/2020011.

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Pressure balances are known to have a linear straight line equation of the form y = ax + b that relates the applied pressure x to the effective area y, and recent work has investigated the use of Ordinary Least Squares (OLS), Weighted Least Squares (WLS), and Generalized Least Squares (GLS) regression schemes in order to quantify the expected values of the zero-pressure area A0 = b and distortion coefficient λ = a/b in pressure balance models of the form y = A0(1 + λx). The limitations with conventional OLS, WLS and GLS approaches is that whilst they may be used to quantify the uncertainties u

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Zhang, Qi, Yihui Zhang, and Yemao Xia. "Bayesian Feature Extraction for Two-Part Latent Variable Model with Polytomous Manifestations." Mathematics 12, no. 5 (2024): 783. http://dx.doi.org/10.3390/math12050783.

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Semi-continuous data are very common in social sciences and economics. In this paper, a Bayesian variable selection procedure is developed to assess the influence of observed and/or unobserved exogenous factors on semi-continuous data. Our formulation is based on a two-part latent variable model with polytomous responses. We consider two schemes for the penalties of regression coefficients and factor loadings: a Bayesian spike and slab bimodal prior and a Bayesian lasso prior. Within the Bayesian framework, we implement a Markov chain Monte Carlo sampling method to conduct posterior inference.

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Mandallaz, Daniel, Jochen Breschan, and Andreas Hill. "New regression estimators in forest inventories with two-phase sampling and partially exhaustive information: a design-based Monte Carlo approach with applications to small-area estimation." Canadian Journal of Forest Research 43, no. 11 (2013): 1023–31. http://dx.doi.org/10.1139/cjfr-2013-0181.

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We consider two-phase sampling schemes where one component of the auxiliary information is known in every point (“wall-to-wall”) and a second component is available only in the large sample of the first phase, whereas the second phase yields a subsample with the terrestrial inventory. This setup is of growing interest in forest inventory thanks to the recent advances in remote sensing, in particular, the availability of LiDAR data. We propose a new two-phase regression estimator for global and local estimation and derive its asymptotic design-based variance. The new estimator performs better t

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Altukhaes, Waleed B., Mahdi Roozbeh, and Nur A. Mohamed. "Robust Liu Estimator Used to Combat Some Challenges in Partially Linear Regression Model by Improving LTS Algorithm Using Semidefinite Programming." Mathematics 12, no. 17 (2024): 2787. http://dx.doi.org/10.3390/math12172787.

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Outliers are a common problem in applied statistics, together with multicollinearity. In this paper, robust Liu estimators are introduced into a partially linear model to combat the presence of multicollinearity and outlier challenges when the error terms are not independent and some linear constraints are assumed to hold in the parameter space. The Liu estimator is used to address the multicollinearity, while robust methods are used to handle the outlier problem. In the literature on the Liu methodology, obtaining the best value for the biased parameter plays an important role in model predic

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Nabeel, Moezza, Sajid Ali, Ismail Shah, Mohammed M. A. Almazah, and Fuad S. Al-Duais. "Robust Surveillance Schemes Based on Proportional Hazard Model for Monitoring Reliability Data." Mathematics 11, no. 11 (2023): 2480. http://dx.doi.org/10.3390/math11112480.

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Product reliability is a crucial component of the industrial production process. Several statistical process control techniques have been successfully employed in industrial manufacturing processes to observe changes in reliability-related quality variables. These methods, however, are only applicable to single-stage processes. In reality, manufacturing processes consist of several stages, and the quality variable of the previous stages influences the quality of the present stage. This interdependence between the stages of a multistage process is an important characteristic that must be taken

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Pooley, C. M., and G. Marion. "Bayesian model evidence as a practical alternative to deviance information criterion." Royal Society Open Science 5, no. 3 (2018): 171519. http://dx.doi.org/10.1098/rsos.171519.

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While model evidence is considered by Bayesian statisticians as a gold standard for model selection (the ratio in model evidence between two models giving the Bayes factor), its calculation is often viewed as too computationally demanding for many applications. By contrast, the widely used deviance information criterion (DIC), a different measure that balances model accuracy against complexity, is commonly considered a much faster alternative. However, recent advances in computational tools for efficient multi-temperature Markov chain Monte Carlo algorithms, such as steppingstone sampling (SS)

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Butyrkin, A. Ya, V. A. Gelis, and E. B. Kulikova. "Features of application of progressive methods of predictive modeling for solving problems on transport." Herald of the Ural State University of Railway Transport, no. 4 (2021): 68–78. http://dx.doi.org/10.20291/2079-0392-2021-4-68-78.

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The paper analyzes the potential of forecasting approaches based on progressive methods of predictive modeling in relation to the transport sector. In the context of peculiarities of Russian and foreign business practices, the possibilities of modern technologies for working with data are considered, schemes and conceptual models formalizing their specifics are formed. Characteristic features of common algorithms and methods are described, the risks and disadvantages of unjustified use of traditional predictive models on the example of simple and multiple regressions are shown. Special attenti

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Silalahi, Divo Dharma, Habshah Midi, Jayanthi Arasan, Mohd Shafie Mustafa, and Jean-Pierre Caliman. "Automated Fitting Process Using Robust Reliable Weighted Average on Near Infrared Spectral Data Analysis." Symmetry 12, no. 12 (2020): 2099. http://dx.doi.org/10.3390/sym12122099.

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With the complexity of Near Infrared (NIR) spectral data, the selection of the optimal number of Partial Least Squares (PLS) components in the fitted Partial Least Squares Regression (PLSR) model is very important. Selecting a small number of PLS components leads to under fitting, whereas selecting a large number of PLS components results in over fitting. Several methods exist in the selection procedure, and each yields a different result. However, so far no one has been able to determine the more superior method. In addition, the current methods are susceptible to the presence of outliers and

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