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Academic literature on the topic 'Linear Prediction Formula'
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Journal articles on the topic "Linear Prediction Formula"
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Lee, Kwang-Ho, and Yong-Hwan Cho. "Simple Breaker Index Formula Using Linear Model." Journal of Marine Science and Engineering 9, no. 7 (2021): 731. http://dx.doi.org/10.3390/jmse9070731.
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Breaking waves generated by wave shoaling in coastal areas have a close relationship with various physical phenomena in coastal regions. Therefore, it is crucial to accurately predict breaker indexes such as breaking wave height and breaking depth when designing coastal structures. Many studies on wave breaking have been carried out, and many experimental data have been documented. Representative studies on wave breaking provide many empirical formulas for the prediction of breaking index, mainly through hydraulic model experiments. However, the existing empirical formulas for breaking index determine the coefficients of the assumed equation through statistical analysis of data under the assumption of a specific equation. This study presents an alternative method to estimate breaker index using representative linear-based supervised machine learning algorithms that show high predictive performance in various research fields related to regression or classification problems. Based on the used machine learning methods, a new simple linear equation for the prediction of breaker index is presented. The newly proposed breaker index formula showed similar predictive performance compared to the existing empirical formula, although it was a simple linear equation.
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Ngô, Trọng Hữu. "Dự đoán khả năng chịu uốn của tiết diện dầm bê tông cốt thép bằng công thức thực hành". Vietnam Institute for Building Science and Technology 2023, vi.vol2 (2023): 14–21. http://dx.doi.org/10.59382/j-ibst.2023.vi.vol2-2.
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This paper presents the process of developing a practical formula for predicting the ultimate bending moment of rectangular reinforced concrete (RC) beams through regression analysis. The data used for regression analysis was generated by using the fiber method to analyze a non-linear batch of commonly encountered RC beam cross-sections. The practical formula was obtained by fitting a linear regression model to the training set and then making predictions on the test set. The coefficient of determination, R2, between the bending moment values calculated from the formula and the results of the non-linear analysis was 0.9948, indicating a good predictive capability of the formula.
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Mohammadi, Mohammad, та Adel Mohammadpour. "On the Prediction of α-Stable Time Series". Fluctuation and Noise Letters 15, № 04 (2016): 1650021. http://dx.doi.org/10.1142/s0219477516500218.
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This paper addresses the point prediction of [Formula: see text]-stable time series. Our key idea is to define a new Hilbert space that contains [Formula: see text]-stable processes. Then, we apply the advantage of Hilbert space theory for finding the best linear prediction. We show how to use the presented predictor practically for [Formula: see text]-stable linear processes. The implementation of the presented method is easier than the implementation of the minimum dispersion method. We reveal the appropriateness of the presented method through an empirical study on predicting the natural logarithms of the volumes of SP500 market.
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Di, Yu, Ying Li, and Yan Luo. "Prediction of Implantable Collamer Lens Vault Based on Preoperative Biometric Factors and Lens Parameters." Journal of Refractive Surgery 39, no. 5 (2023): 332–39. http://dx.doi.org/10.3928/1081597x-20230207-03.
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Purpose: To establish and validate the accuracy of implantable collamer lens (ICL) vault size prediction formula based on preoperative biometric factors and lens parameters. Methods: This study included 300 patients (300 eyes) with Visian ICL V4c (STAAR Surgical) implantation. They were randomly divided into the formula establishment group and formula validation group. Anterior segment measurements, ICL V4c size and power, and vault 1 week postoperatively were collected from all patients. Multiple linear regression analysis was performed to establish the prediction formula. Mean absolute error (MAE), median absolute error (MedAE), root mean square error (RMSE), and Bland-Altman diagrams were used to evaluate the prediction formula. Results: Anterior chamber depth (ACD) had the greatest influence on vault 1 week after ICL V4c implantation, followed by ICL V4c size and angle-to-angle distance (ATA). The prediction formula was obtained according to the partial regression coefficient, which was vault (mm) = −1.279 + 0.291 × ACD (mm) + 0.210 × ICL V4c size (mm) – 0.144 × ATA (mm) ( R 2 = 0.661). In the formula validation group, the mean predictive vault, MAE, MedAE, and RMSE were 628.10, 135.09, 130.42, and 150.46 µm, respectively. The Bland-Altman diagram showed the predictive vault was in good agreement with the actual vault. Conclusions: A novel ICL V4c vault prediction formula was developed and shown to be an effective method for predicting the vault to reduce surgical complications. [ J Refract Surg . 2023;39(5):332–339.]
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HINTON-BAYRE, ANTON. "Reliable Change formula query." Journal of the International Neuropsychological Society 6, no. 3 (2000): 362–63. http://dx.doi.org/10.1017/s1355617700633118.
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In a recent article, Temkin et al. (1999) contrasted four models for detecting significant change in individual performance on neuropsychological tests. Two of these models relied on the calculation of the Reliable Change Index (RCI) by Jacobson and Truax (1991), with and without a correction for practice associated with repeated testing. The other two models were based on simple linear regression and multiple regression, respectively. The models were contrasted based on the width of 90% prediction intervals (PI) and normal-distribution-based prediction accuracy of classifying unusual cases. Participants were tested twice (Time 1 and Time 2), on seven common neuropsychological measures. Prediction accuracy was based on the discrepancy between obtained and predicted Time 2 scores.
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Engel, B., W. G. Buist, P. Walstra, E. Olsen, and G. Daumas. "Accuracy of prediction of percentage lean meat and authorization of carcass measurement instruments: adverse effects of incorrect sampling of carcasses in pig classification." Animal Science 76, no. 2 (2003): 199–209. http://dx.doi.org/10.1017/s1357729800053455.
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AbstractClassification of pig carcasses in the European Community is based on the lean meat percentage of the carcass. The lean meat percentage is predicted from instrumental carcass measurements, such as fat and muscle depth measurements, obtained in the slaughter-line. The prediction formula employed is derived from the data of a dissection experiment and has to meet requirements for authorization as put down in EC regulations. Requirements involve the sampling procedure and sample size for the dissected carcasses and the accuracy of prediction. Formulae are often derived by linear regression. In this paper we look at a particular type of sampling scheme. This involves selection of carcasses on the basis of carcass measurements not all of which are intended to be used as prediction variables. This sampling scheme frequently appears in requests for authorization of carcass measurement instruments and accompanying prediction formulae, despite the fact that it lacks formal statistical justification when used in conjunction with linear regression. The objective of this work was to assess the performance of the prediction formula that follows from this potentially faulty combination of sampling scheme and linear regression in relation to the requirements in the EC regulations. We show that this sampling scheme may produce poor predictions for lean meat percentage compared with proper sampling procedures with selection on prediction variables only or random sampling. We do so by computer simulation. Initially, simulated data were based on recent and historic data from The Netherlands. Prediction variables are fat and muscle depth measurements. The additional variable involved in sampling, but not included in the regression, was carcass weight. We also show that due to this faulty sampling scheme there is a serious risk that a new measurement instrument may not be authorized because performance criteria in the EC-regulations are not met.
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Xie, Liusen, and William W. Hsieh. "Predicting the Return Migration Routes of the Fraser River Sockeye Salmon (Oncorhynchus nerka)." Canadian Journal of Fisheries and Aquatic Sciences 46, no. 8 (1989): 1287–92. http://dx.doi.org/10.1139/f89-165.
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The Johnstone Strait diversion rate (i.e. the percentage of homeward migrating Fraser River sockeye salmon (Oncorhynchus nerka) travelling around Vancouver Island via the northern route of Johnstone Strait) is statistically predicted using the March values of the Kains Island sea surface temperature T and the Fraser River runoff R. The prediction formula incorporates nonlinear terms such as T2 and RT, as well as the diversion rate 2 yr ago. We tested the forecasting performance by constructing a prediction formula using only data from 1953–78, and making predictions for 1979–88. The mean absolute error of our prediction of the diversion rate was 8% which compared favourably with the prediction by a linear temperature scheme and a linear runoff scheme where the errors were respectively 13 and 29%. Unlike the latter two schemes where T and R data from April–June are needed, our scheme with the use of data no later than March allows much earlier forecasts to be made.
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Yuan, Shasha, Weidong Zhou, and Liyan Chen. "Epileptic Seizure Prediction Using Diffusion Distance and Bayesian Linear Discriminate Analysis on Intracranial EEG." International Journal of Neural Systems 28, no. 01 (2017): 1750043. http://dx.doi.org/10.1142/s0129065717500435.
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Epilepsy is a chronic neurological disorder characterized by sudden and apparently unpredictable seizures. A system capable of forecasting the occurrence of seizures is crucial and could open new therapeutic possibilities for human health. This paper addresses an algorithm for seizure prediction using a novel feature — diffusion distance (DD) in intracranial Electroencephalograph (iEEG) recordings. Wavelet decomposition is conducted on segmented electroencephalograph (EEG) epochs and subband signals at scales 3, 4 and 5 are utilized to extract the diffusion distance. The features of all channels composing a feature vector are then fed into a Bayesian Linear Discriminant Analysis (BLDA) classifier. Finally, postprocessing procedure is applied to reduce false prediction alarms. The prediction method is evaluated on the public intracranial EEG dataset, which consists of 577.67[Formula: see text]h of intracranial EEG recordings from 21 patients with 87 seizures. We achieved a sensitivity of 85.11% for a seizure occurrence period of 30[Formula: see text]min and a sensitivity of 93.62% for a seizure occurrence period of 50[Formula: see text]min, both with the seizure prediction horizon of 10[Formula: see text]s. Our false prediction rate was 0.08/h. The proposed method yields a high sensitivity as well as a low false prediction rate, which demonstrates its potential for real-time prediction of seizures.
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Castillo-Sanchez, L. E., J. R. Canul-Solís, D. Pozo-Leyva, et al. "Prediction of live weight in beef heifers using a body volume formula." Arquivo Brasileiro de Medicina Veterinária e Zootecnia 74, no. 6 (2022): 1127–33. http://dx.doi.org/10.1590/1678-4162-12886.
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ABSTRACT The objective of this study was to develop and evaluate linear, quadratic, and allometric models to predict live weight (LW) using the body volume formula (BV) in crossbred heifers raised in southeastern Mexico. The LW (426.25±117.49kg) and BV (338.05±95.38 dm3) were measured in 360 heifers aged between 3 and 30 months. Linear and non-linear regression were used to construct prediction models. The goodness-of-fit of the models was evaluated using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), coefficient of determination (R2), mean squared error (MSE), and root MSE (RMSE). In addition, the developed models were evaluated through cross-validation (k-folds). The ability of the fitted models to predict the observed values was evaluated based on the RMSEP, R2, and mean absolute error (MAE). The quadratic model had the lowest values of AIC (2688.39) and BIC (2700.05). On the other hand, the linear model showed the lowest values of MSE (7954.74) and RMSE (89.19), and the highest values of AIC (2709.70) and BIC (2717.51). Despite this, all models presented the same R2 value (0.87). The cross-validation (k-folds) evaluation of fit showed that the quadratic model had better values of MSEP (41.49), R2 (0.85), and MAE (31.95). We recommend the quadratic model to predictive of the crossbred beef heifers' live weight using the body volume as the predictor.
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KARNA, DILLIP KUMAR, ADITYA PRASAD ACHARYA, BHABESH CHANDRA DAS, GANGADHAR NAYAK, and M. R. DIBYADARSHINI. "Comparison of regression methods and Shaeffer’s formula in prediction of Live Body Weight of Ganjam Goats." Indian Journal of Animal Sciences 92, no. 6 (2022): 770–75. http://dx.doi.org/10.56093/ijans.v92i6.108921.
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Linear regression and polynomial regression of order two and three were utilized to predict the live weight ofGanjam goats across five age groups using chest girth as predictor and their accuracies were compared with theprediction of weight made by Shaeffer’s formula. Live body weight of Ganjam goat recorded by electronic weighingbalance was used as standard for calculating the error of prediction. The body weights of 1014 Ganjam goats (329males and 685 females) were estimated by each technique during 2015 to 2017. Compared with electronic weighingscale, the body weight estimates in Ganjam goat exceeded in all age groups for Shaeffer’s formula whereas predicted body weight estimates by linear regression and second order polynomial regression were close to the live body weights. The estimates of linear regression and second order polynomial regression were significantly different from the electronic weighing scale for all age groups. The study concluded that polynomial regression of order two had better predictive value for live body weight of Ganjam goat, followed by third order polynomial regression, linear regression and Shaeffer’s formula, in order