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27 results for “Error Estimates”
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Research ArticleOpen accessIn the keywordsA Computational Method Based on Bernstein Polynomials for Solving FredholmIntegro-Differential Equations under Mixed ConditionsMiloud Moussai, Lakhdar ChiterIn this study, a computational method for solving linear FredholmIntegro-Differential Equation (FIDE) of the first order under the mixed conditions using the Bernstein polynomials. First, we present some properties of these polynomials and the method is explained. These properties are then used to convert the integro-differential equation to a system of linear algebraic equations with unknown Bernstein coefficients. Using Galerkin … …Journal of Mathematics and Statistics, Vol. 13, Issue 1 · 27 Feb 2017 · 7,142 accessesView PDFCite
Research ArticleOpen accessIn the abstractAdaptive Cross-Validation Under Concept Drift for Time Series ForecastingKunjira Kingphai, Prapakorn Kanjina, Kamol Sanittham, Wacharong WongsanurakTime-series forecasting often involves non-stationary data, making i.i.d. validation unreliable and fixed-window protocols vulnerable to leakage and biased error estimates. We propose Adaptive Time-Series Cross-Validation (ATSCV), a drift-aware evaluation framework that uses statistical change-point detection to partition each series into contiguous, approximately stationary regimes, followed by forward-chaining folds that respect those boundaries. By aligning train–validation splits with distributional changes (with … …Journal of Computer Science, Vol. 22, Issue 7 · 7 Jul 2026 · 1,488 accessesView PDFCite
Research ArticleOpen accessIn the abstractAI-Integrated Probabilistic Optimization for Inventory Control Under Stochastic DemandJanardan BeheraThe identification and operationalization of increasingly nonstationary and volatile demand patterns challenge the conduct of inventory management in contemporary supply chains. The classical models, especially EOQ and static policies, rest on the stability of demand assumptions, either deterministically or distributionally, and are hence less apt under real-world uncertainty. In view of these limitations, the present study puts forward an integrated … …Journal of Mathematics and Statistics, Vol. 22, Issue 1 · 6 May 2026 · 2,217 accessesView PDFCite
Research ArticleOpen accessIn the abstractDeep Learning Models for Predicting Stock Closing Prices in the Saudi Stock MarketAreej Alhumaidi, Hoda A. AbdelhafezFinancial time series data are inherently volatile and nonlinear, presenting considerable challenges in accurately forecasting stock market trends. This study aims to predict the closing prices of companies listed on the Saudi stock market by analyzing historical data spanning from 2013 to 2023. To this end, four deep learning models were employed-Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent … …Journal of Computer Science, Vol. 22, Issue 4 · 22 Apr 2026 · 1,355 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Novel Virtual Modeling Approach for Mango Tree Biomass Estimation Using Allometric Equations and Functional-Structural Plant Modeling (FSPM) with GroIMPOki Marzuqi, Ditdit Nugeraha Utama, Azhar Indra RusmanaBiomass represents a critical ecological contribution of trees, including mango (Mangifera indica L.), necessitating rigorous academic approaches for quantification and modeling. This study develops a computational model to enhance understanding of mango tree morphology and physiology using Functional-Structural Plant Modeling (FSPM) implemented through GroIMP software. Comparative analysis reveals that the FSPM-based biomass model generates higher estimates than traditional allometric equations, … …Journal of Computer Science, Vol. 21, Issue 11 · 12 Feb 2026 · 1,282 accessesView PDFCite
Research ArticleOpen accessIn the abstractOn Bessel’s Correction: Unbiased Sample Variance, the Bariance, and a Novel Runtime-Optimized EstimatorFelix ReichelBessel’s correction adjusts the denominator in the sample variance formula from n to n – 1 to ensure an unbiased estimator of the population variance. This paper provides rigorous algebraic derivations to reinforce the necessity of this correction. It further introduces the concept of Bariance, an alternative dispersion measure based on average pairwise squared differences that avoids reliance on the … …Journal of Mathematics and Statistics, Vol. 21, Issue 1 · 29 Jan 2026 · 2,994 accessesView PDFCite
Research ArticleOpen accessIn the abstractBayesian Estimation for Lomax Distribution: A Comparison of Loss Functions Using Jeffreys PriorsHuda Mohammed AlomariThis study investigates the estimation of the shape parameter for the Lomax distribution using complete data. We compare the performance of the classical Maximum Likelihood Estimation (MLE) method against the Bayesian framework. Within the Bayesian approach, three distinct loss functions were utilized: the linear exponential (LINEX), general entropy, and weighted general entropy loss functions. The precision of these estimators was … …Journal of Mathematics and Statistics, Vol. 21, Issue 1 · 24 Dec 2025 · 2,635 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnhancing Fleet Management with GPS Data in Port Terminals: A Deep Learning Architecture for Truck Position PredictionMohammed El Karkraoui, Hicham AttariuasEffective truck fleet management in port terminals is crucial for improving operational efficiency and reducing delays. However, GPS-based positioning systems often face limitations in port environments due to signal obstructions and network issues. This study explores the utilization of advanced methods for deep learning, particularly Recurrent Neural Networks (RNNs) for predicting truck positions when GPS signals are unavailable or unreliable. … …Journal of Computer Science, Vol. 21, Issue 10 · 16 Dec 2025 · 2,271 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Empirical Study of Robust Modified Recursive Fits of Moving Average ModelsMohamed Ali Ismail, Hend A. Auda, Joe W. McKean, Mahmoud Mohamed SadekThe time-series Moving Average (MA) model is a nonlinear model; see, for example. For traditional Least Squares (LS) fits, there are several algorithms to use for computing its fit. Since the model is nonlinear, Fuller discusses a Newton-type step algorithm. proposed a recursive algorithm based on a sequence of three linear LS regressions. In this study, we robustify Koreisha and … …Journal of Mathematics and Statistics, Vol. 18, Issue 1 · 28 Jul 2022 · 5,859 accessesView PDFCite
Research ArticleOpen accessIn the abstractComparison of Probit and Logistic Regression Models in the Analysis of Dichotomous OutcomesAmrutha Jose, Mariyamma Philip, Lavanya Tumkur Prasanna, M. ManjulaProbit and logistic regression models are members of the family of generalized linear models, used for estimating the functional relationship between the dichotomous dependent and independent variables. The current study is designed to find the performance of logistic and probit regression models in different conditions under multivariate normality. The objective of the study is to compare the performance of probit … …Current Research in Biostatistics, Vol. 10, Issue 1 · 26 Mar 2020 · 17,036 accessesView PDFCite