Artykuły w czasopismach: „Multiple Regression Estimate”

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ALVES, M. E., i A. LAVORENTI. "Remaining Phosphorus Estimate Through Multiple Regression Analysis". Pedosphere 16, nr 5 (październik 2006): 566–71. http://dx.doi.org/10.1016/s1002-0160(06)60089-1.

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Li, Zhong-xiao. "Adaptive multiple subtraction based on support vector regression". GEOPHYSICS 85, nr 1 (22.11.2019): V57—V69. http://dx.doi.org/10.1190/geo2018-0245.1.

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ABSTRACT Multiple removal is essential for seismic imaging in marine seismic processing. After prediction of multiple models, adaptive multiple subtraction is an important procedure for multiple removal. Generally, adaptive multiple subtraction can be conducted by the iterative reweighted least-squares (IRLS) algorithm with an [Formula: see text]-norm minimization constraint of primaries. We have developed a machine-learning algorithm into adaptive multiple subtraction, which is implemented based on support vector regression (SVR). Our SVR-based method contains training and prediction stages. During the training stage, an SVR function is estimated by solving a dual optimization problem with the feature vectors of the predicted multiples and the target values of the original data. The SVR function can transform predicted multiples nonlinearly for a better match between the predicted multiples and the true multiples. Furthermore, we use the SVR function to estimate multiples in the prediction stage by inputting the feature vectors of predicted multiples. Then, multiple-removal results are obtained by subtracting the estimated multiples directly from the original data. Synthetic and field data examples demonstrate that our SVR-based method can better balance multiple removal and primary preservation than the IRLS-based method.

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Gorgees, HazimMansoor, i FatimahAssim Mahdi. "Using the Ridge Regression Procedures to Estimate the Multiple Linear Regression Coefficients". Journal of Physics: Conference Series 1003 (maj 2018): 012049. http://dx.doi.org/10.1088/1742-6596/1003/1/012049.

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Rahayu, Elvri, Iin Parlina i Zulia Almaida Siregar. "Application of Multiple Linear Regression Algorithm for Motorcycle Sales Estimation". JOMLAI: Journal of Machine Learning and Artificial Intelligence 1, nr 1 (18.03.2022): 1–10. http://dx.doi.org/10.55123/jomlai.v1i1.142.

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CV. Kerinci Motor is a company engaged in the transportation and automotive sector, especially in the sale of motorcycles. The uncertainty in the number of motorcycle sales at this company has hampered the company's development, because the company cannot take definite policies regarding the sales that occur. Therefore, it is necessary to estimate the sales of motorcycles at this company in the future, so that the management can estimate consumer demand in the future. So that the company is able to serve and provide consumer demand. The estimation algorithm that will be used in this research is Multiple Linear Regression which is one of the data mining methods. This method was chosen because it is able to make an estimate by utilizing data regarding sales. The results of the estimated (estimated) sales of manual motorcycles in 2021 by January are 56,941 or 57 motorcycles in the manual category. This means that there is an increase in the number of manual motorbikes by 5 motorbikes, while the results until May 2021 amounted to 65,710 motorbikes. So it can be concluded that sales of motorcycles at CV. Kerinci Motor have increased sales in the next 5 months.

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Suparta, W., i W. S. Putro. "Using multiple linear regression model to estimate thunderstorm activity". IOP Conference Series: Materials Science and Engineering 185 (marzec 2017): 012023. http://dx.doi.org/10.1088/1757-899x/185/1/012023.

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Long, Michael A., Kenneth J. Berry i Paul W. Mielke. "A Note on Permutation Tests of Significance for Multiple Regression Coefficients". Psychological Reports 100, nr 2 (kwiecień 2007): 339–45. http://dx.doi.org/10.2466/pr0.100.2.339-345.

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In the vast majority of psychological research utilizing multiple regression analysis, asymptotic probability values are reported. This paper demonstrates that asymptotic estimates of standard errors provided by multiple regression are not always accurate. A resampling permutation procedure is used to estimate the standard errors. In some cases the results differ substantially from the traditional least squares regression estimates.

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Ellington, E. Hance, Guillaume Bastille‐Rousseau, Cayla Austin, Kristen N. Landolt, Bruce A. Pond, Erin E. Rees, Nicholas Robar i Dennis L. Murray. "Using multiple imputation to estimate missing data in meta‐regression". Methods in Ecology and Evolution 6, nr 2 (17.12.2014): 153–63. http://dx.doi.org/10.1111/2041-210x.12322.

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White, Edward D., Vincent P. Sipple i Michael A. Greiner. "Using Logistic and Multiple Regression to Estimate Engineering Cost Risk". Journal of Cost Analysis & Management 6, nr 1 (lipiec 2004): 67–79. http://dx.doi.org/10.1080/15411656.2004.10462248.

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Shen, Gang. "Asymptotics of a Theil-type estimate in multiple linear regression". Statistics & Probability Letters 79, nr 8 (kwiecień 2009): 1053–64. http://dx.doi.org/10.1016/j.spl.2008.12.017.

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Chen, Xiru, i Hongzhi An. "Abnormal behavior of the least squares estimate of multiple regression". Science in China Series A: Mathematics 40, nr 3 (marzec 1997): 234–42. http://dx.doi.org/10.1007/bf02874515.

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Gideon, Rudy A. "Using correlation coefficients to estimate slopes in multiple linear regression". Sankhya B 72, nr 1 (maj 2010): 96–106. http://dx.doi.org/10.1007/s13571-010-0006-4.

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عبد السلام, ايهاب. "Detecting Outliers In Multiple Linear Regression". Journal of Economics and Administrative Sciences 17, nr 64 (1.12.2011): 9. http://dx.doi.org/10.33095/jeas.v17i64.900.

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It is well-known that the existence of outliers in the data will adversely affect the efficiency of estimation and results of the current study. In this paper four methods will be studied to detect outliers for the multiple linear regression model in two cases : first, in real data; and secondly, after adding the outliers to data and the attempt to detect it. The study is conducted for samples with different sizes, and uses three measures for comparing between these methods . These three measures are : the mask, dumping and standard error of the estimate.

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Pati, Kafi Dano. "Using Robust Ridge Regression Diagnostic Method to Handle Multicollinearity Caused High Leverage Points". Academic Journal of Nawroz University 10, nr 1 (11.04.2021): 326. http://dx.doi.org/10.25007/ajnu.v10n1a578.

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Statistics practitioners have been depending on the ordinary least squares (OLS) method in the linear regression model for generation because of its optimal properties and simplicity of calculation. However, the OLS estimators can be strongly affected by the existence of multicollinearity which is a near linear dependency between two or more independent variables in the regression model. Even though in the presence of multicollinearity the OLS estimate still remained unbiased, they will be inaccurate prediction about the dependent variable with the inflated standard errors of the estimated parameter coefficient of the regression model. It is now evident that the existence of high leverage points which are the outliers in x-direction are the prime factor of collinearity influential observations. In this paper, we proposed some alternative to regression methods for estimating the regression parameter coefficient in the presence of multiple high leverage points which cause the multicollinearity problem. This procedure utilized the ordinary least squares estimates of the parameter as the initial followed by an estimate of the ridge regression. We incorporated the Least Trimmed Squares (LTS) robust regression estimate to down weight the effects of multiple high leverage points which lead to the reduction of the effects of multicollinearity. The result seemed to suggest that the RLTS give a substantial improvement over the Ridge Regression.

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Oloro, J. O., i T. E. Akhihiero. "Multilinear Regression Model to Estimate Mud Weight". March 2021 5, nr 1 (1.03.2021): 1–12. http://dx.doi.org/10.36263/nijest.2021.01.0229.

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Estimation of mud weight poses a serious challenge to mud industries. In this study, a model was developed to tackle the problem of estimation of mud weight using multilinear regression techniques. The model was developed using data obtained from production records. The data include mud weight, water and other chemicals (materials) for nine different samples. The data were analysed to establish linearity and the data was substituted into the multiple regression to form a matrix with nine unknown regression parameters which was substituted into the regression equation to form the model. T-test and F –test was used to validate the model. Results from the test suggest that the developed model was reliable. The model was used to estimate mud weight for four samples and the results are reliable. The effect of each variable was also considered and results also show that each of the variables affects the mud weight.

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Ohsowski, Brian M., Kari E. Dunfield, John N. Klironomos i Miranda M. Hart. "Improving plant biomass estimation in the field using partial least squares regression and ridge regression". Botany 94, nr 7 (lipiec 2016): 501–8. http://dx.doi.org/10.1139/cjb-2016-0009.

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Estimating primary productivity over time is challenging for plant ecologists. The most accurate biomass measurements require destructive sampling and weighing. This is often not possible for manipulative studies that involve repeated measures over time, or for studies in protected areas. Estimates of aboveground plant biomass using allometric equations or linear regression on single plant traits have been used, but tend to have poor predictability both within and across systems, or are limited to specific plant taxa. Here we estimate aboveground plant biomass using multiple collinear plant traits to generate a standard curve specific to the site of interest. Partial least squares regression (PLS) and ridge regression (RR), addressing predictor collinearity, are robust, highly accurate statistical methods to estimate plant biomass across gross differences in plant morphology and growth habit.

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Mahamid, Ibrahim. "Early cost estimating for road construction projects using multiple regression techniques". Construction Economics and Building 11, nr 4 (9.12.2011): 87–101. http://dx.doi.org/10.5130/ajceb.v11i4.2195.

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The objective of this study is to develop early cost estimating models for road construction projects using multiple regression techniques, based on 131 sets of data collected in the West Bank in Palestine. As the cost estimates are required at early stages of a project, considerations were given to the fact that the input data for the required regression model could be easily extracted from sketches or scope definition of the project. 11 regression models are developed to estimate the total cost of road construction project in US dollar; 5 of them include bid quantities as input variables and 6 include road length and road width. The coefficient of determination r2 for the developed models is ranging from 0.92 to 0.98 which indicate that the predicted values from a forecast models fit with the real-life data. The values of the mean absolute percentage error (MAPE) of the developed regression models are ranging from 13% to 31%, the results compare favorably with past researches which have shown that the estimate accuracy in the early stages of a project is between ±25% and ±50%.

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JHERIMAE C. ANCHETA, PRIMO IVAN D. LAZO, LLOVELYN B. MEDINA, CRISELLE J. CENTENO, ARIEL ANTWAUN ROLANDO C. SISON i MARK ANTHONY S. MERCADO. "Road construction analysis using regression technique". World Journal of Advanced Research and Reviews 18, nr 3 (30.06.2023): 658–64. http://dx.doi.org/10.30574/wjarr.2023.18.3.1125.

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Estimating cost in construction is important to the city's design and planning management hence, the construction cost estimate must not be overpriced which may cause corruption or underpricing that leads to unreliable or low-quality road projects. The total estimated cost is only valid in the same year it was proposed because of the inflation rate the costs may change. The researchers applied Multiple Linear Regression technique in predicting total estimated cost for road construction analysis. The model is evaluated by the means of R-squared to determine the variables if they are correlated or overfitting. The calculated R-squared is equals to 0.696598 with the predictor variables (x1 & x2) Roadbed width and Net length and it means that the predictors (Xi) explain 69.7% of the variance of Y. The higher the R-squared result, the better fit it is for the Multiple Linear Regression model. It also shows that X1 and X2 are significant predictor variables. The coefficient of multiple correlation (R) is equals to 0.834624 and it means that there is a very strong correlation between the predicted data and the observed data whereas the dependent variable (y) is the Estimated cost. CCS CONCEPTS: Multiple Linear Regression • Construction Estimation • Engineering

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McConachie, Sean M., Claudia M. Hanni, Joshua N. Raub, Rima A. Mohammad i Sheila M. Wilhelm. "The Impact of Multiple Renal Estimates on Pharmacist Dosing Recommendations: A Randomized Trial". Annals of Pharmacotherapy 55, nr 1 (24.06.2020): 25–35. http://dx.doi.org/10.1177/1060028020935447.

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Background: Numerous equations are used for estimation of renal function, and many electronic medical records report multiple clearance estimates to assist with drug dosing. It is unknown whether the presence of multiple clearance estimates affects clinical decision-making. Objective: To determine whether the presence of multiple renal clearance estimates affects pharmacist drug dosing decisions. Methods: A randomized trial in the form of an electronic survey including 4 clinical vignettes was delivered to hospital pharmacists. Vignettes consisted of a patient presenting with an acute pulmonary embolism requiring enoxaparin therapy. Pharmacists were randomized to receive a single estimate of renal function or multiple estimates for all vignettes. The primary outcome was deviation from approved recommendations on at least 1 vignette. The χ2 test was used to detect differences in deviation rates between groups. Logistic regression was performed to adjust for the effects of potentially confounding variables. Results: A total of 154 studies were completed (73 in the multiple-estimate group and 81 in the single-estimate group). Pharmacists presented with multiple renal estimates were significantly more likely to deviate from recommended dosing regimens than pharmacists presented with a single estimate (54.7% vs 38.2%; P = 0.04). The results were driven primarily by the 2 vignettes that included discordance among Cockcroft-Gault equation creatinine clearance estimates. Logistic regression identified multiple estimates as the only independent predictor of deviation ( P = 0.04). Conclusion and Relevance: Pharmacists provided with a single renal clearance estimate were more likely to adhere to approved dosing recommendations than pharmacists provided with multiple estimates.

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Ramadhan, Muhammad Fauzan, Guruh Samodra, Muhammad Rizky Shidiq Nugraha i Djati Mardiatno. "PERBANDINGAN METODE MULTIPLE LINEAR REGRESSION (MLR) DAN REGRESSION KRIGING (RK) DALAM PEMETAAN KETEBALAN TANAH DIGITAL". Jurnal Tanah dan Sumberdaya Lahan 10, nr 1 (1.01.2023): 65–74. http://dx.doi.org/10.21776/ub.jtsl.2023.010.1.7.

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Soil thickness has a significant influence on many of earth surface processes, and it can be mapped using various methods. Digital soil mapping can be used to estimate the spatial distribution of soil thickness and can estimate the uncertainty of the soil prediction map. Digital soil mapping using regression methods such as Multiple Linear Regression (MLR) and Regression Krigging (RK) was used to estimate soil thickness of the slope of Bener Reservoir. Bener Dam is a national strategic project which is built for irrigation to improve farming quantity. The aim of this research was to determine the spatial variation of the soil thickness at the slope of Bener Reservoir. The accuracy of MLR and RK were compared to select the best soil thickness prediction. There were 212 and 53 soil thickness samples from fieldwork which were used for data training and testing, respectively. There were 5 environmental variables such as elevation, distance from river, slope, plan curvature, and topographic wetness index. R programming language with gstat, krige, and stats Packages was employed for MLR and RK prediction. The result showed that KR is more accurate than MLR.

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Liu, Yuheng. "Stock Analysis Method based on Multiple Linear Regression". Tobacco Regulatory Science 7, nr 5 (30.09.2021): 4222–28. http://dx.doi.org/10.18001/trs.7.5.1.198.

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In order to research the application of multivariate linear regression in stock price analysis, seven independent variables were selected to estimate in this paper. The regression coefficient was calculated by SPSS and the statistical test was carried out. And finally, the model was applied to the closing price prediction, and the prediction error was within acceptable range.

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Henshall, John M., i Michael E. Goddard. "Multiple-Trait Mapping of Quantitative Trait Loci After Selective Genotyping Using Logistic Regression". Genetics 151, nr 2 (1.02.1999): 885–94. http://dx.doi.org/10.1093/genetics/151.2.885.

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Abstract Experiments to map QTL usually measure several traits, and not uncommonly genotype only those animals that are extreme for some trait(s). Analysis of selectively genotyped, multiple-trait data presents special problems, and most simple methods lead to biased estimates of the QTL effects. The use of logistic regression to estimate QTL effects is described, where the genotype is treated as the dependent variable and the phenotype as the independent variable. In this way selection on phenotype does not bias the results. If normally distributed errors are assumed, the logistic-regression analysis is almost equivalent to a maximum-likelihood analysis, but can be carried out with standard statistical packages. Analysis of a simulated half-sib experiment shows that logistic regression can estimate the effect and position of a QTL without bias and confirms the increased power achieved by multiple-trait analysis.

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Cobuci, Jaime Araújo, Claudio Napolis Costa, José Braccini Neto i Ary Ferreira de Freitas. "Genetic parameters for milk production by using random regression models with different alternatives of fixed regression modeling". Revista Brasileira de Zootecnia 40, nr 3 (marzec 2011): 557–67. http://dx.doi.org/10.1590/s1516-35982011000300013.

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Records of test-day milk yields of the first three lactations of 25,500 Holstein cows were used to estimate genetic parameters for milk yield by using two alternatives of definition of fixed regression of the random regression models (RRM). Legendre polynomials of fourth and fifth orders were used to model regression of fixed curve (defined based on averages of the populations or multiple sub-populations formed by grouping animals which calved at the same age and in the same season of the year) or random lactation curves (additive genetic and permanent enviroment). Akaike information criterion (AIC) and Bayesian information criterion (BIC) indicated that the models which used multiple regression of fixed lactation curves of lactation multiple regression model with fixed lactation curves had the best fit for the first lactation test-day milk yields and the models which used a single regression of fixed curve had the best fit for the second and third lactations. Heritability for milk yield during lactation estimates did not vary among models but ranged from 0.22 to 0.34, from 0.11 to 0.21, and from 0.10 to 0.20, respectively, in the first three lactations. Similarly to heridability estimates of genetic correlations did not vary among models. The use of single or multiple fixed regressions for fixed lactation curves by RRM does not influence the estimates of genetic parameters for test-day milk yield across lactations.

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Koç, Deniz Levent, i Müge Erkan Can. "Reference evapotranspiration estimate with missing climatic data and multiple linear regression models". PeerJ 11 (27.04.2023): e15252. http://dx.doi.org/10.7717/peerj.15252.

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The reference evapotranspiration (ETo) is considered one of the primary variables for water resource management, irrigation practices, agricultural and hydro-meteorological studies, and modeling different hydrological processes. Therefore, an accurate prediction of ETo is essential. A large number of empirical methods have been developed by numerous scientists and specialists worldwide to estimate ETo from different climatic variables. The FAO56 Penman-Monteith (PM) is the most accepted and accurate model to estimate ETo in various environments and climatic conditions. However, the FAO56-PM method requires radiation, air temperature, air humidity, and wind speed data. In this study in Adana Plain, which has a Mediterranean climate for the summer growing season, using 22-year daily climatic data, the performance of the FAO56-PM method was evaluated with different combinations of climatic variables when climatic data were missing. Additionally, the performances of Hargreaves-Samani (HS) and HS (A&G) equations were assessed, and multiple linear regression models (MLR) were developed using different combinations of climatic variables. The FAO56-PM method could accurately estimate daily ETo when wind speed (U) and relative humidity (RH) data were unavailable, using the procedures suggested by FAO56 Paper (RMSEs were smaller than 0.4 mm d−1, and percent relative errors (REs) were smaller than 9%). Hargreaves-Samani (A&G) and HS equations could not estimate daily ETo accurately according to the statistical indices (RMSEs = 0.772-0.957 mm d−1; REs (%) = 18.2–22.6; R2 = 0.604–0.686, respectively). On the other hand, MLR models’ performance varied according to a combination of different climatic variables. According to t-stat and p values of independent variables for MLR models, solar radiation (Rs) and sunshine hours (n) variables had more effect on estimating ETo than other variables. Therefore, the models that used Rs and n data estimated daily ETo more accurately than the others. RMSE values of the models that used Rs were between 0.288 to 0.529 mm d−1; RE(%) values were between 6.2%–11.5% in the validation process. RMSE values of the models that used n were between 0.457 to 0.750 mm d−1; RE(%) values were between 9.9%–16.3% in the validation process. The models based only on air temperature had the worst performance (RMSE = 1.117 mm d−1; RE(%) = 24.2; R2 = 0.423).

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M.T., Nwakuya, i Nkwocha C.C. "Quantile Regression for Count Data as a Robust Alternative to Negative Binomial Regression". African Journal of Mathematics and Statistics Studies 6, nr 1 (2.02.2023): 1–11. http://dx.doi.org/10.52589/ajmss-clq73euz.

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The study investigated the robustness of Quantile regression of count data over negative binomial regression, when there is overdispersion and presence of outlier. The study made use of a complete data and the data with 30% missing data which was imputed using Multiple Imputation by Chain Equation (MICE) in R and also an outlier was injected into the data during imputation of missing values. The Quantile Regression and Negative Binomial Regression estimates were compared and their model fits were also compared. Results showed that the quantile regression for count data provided a better model estimate with both complete data and data with multiple imputed value with comparison to the negative binomial regression in terms of AIC, BIC RMSE and MSE. Hence, Quantile Regression is better than the negative binomial regression when the researcher is interested in the effect of the independent variable on different points of the distribution of the response variable and when there is overdispersion and presence of an outlier.

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Bloch-Johnson, Jonah, Maria Rugenstein i Dorian S. Abbot. "Spatial Radiative Feedbacks from Internal Variability Using Multiple Regression". Journal of Climate 33, nr 10 (15.05.2020): 4121–40. http://dx.doi.org/10.1175/jcli-d-19-0396.1.

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AbstractThe sensitivity of the climate to CO2 forcing depends on spatially varying radiative feedbacks that act both locally and nonlocally. We assess whether a method employing multiple regression can be used to estimate local and nonlocal radiative feedbacks from internal variability. We test this method on millennial-length simulations performed with six coupled atmosphere–ocean general circulation models (AOGCMs). Given the spatial pattern of warming, the method does quite well at recreating the top-of-atmosphere flux response for most regions of Earth, except over the Southern Ocean where it consistently overestimates the change, leading to an overestimate of the sensitivity. For five of the six models, the method finds that local feedbacks are positive due to cloud processes, balanced by negative nonlocal shortwave cloud feedbacks associated with regions of tropical convection. For four of these models, the magnitudes of both are comparable to the Planck feedback, so that changes in the ratio between them could lead to large changes in climate sensitivity. The positive local feedback explains why observational studies that estimate spatial feedbacks using only local regressions predict an unstable climate. The method implies that sensitivity in these AOGCMs increases over time due to a reduction in the share of warming occurring in tropical convecting regions and the resulting weakening of associated shortwave cloud and longwave clear-sky feedbacks. Our results provide a step toward an observational estimate of time-varying climate sensitivity by demonstrating that many aspects of spatial feedbacks appear to be the same between internal variability and the forced response.

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Frančić, Vlado, Nermin Hasanspahić, Mario Mandušić i Marko Strabić. "Estimation of Tanker Ships’ Lightship Displacement Using Multiple Linear Regression and XGBoost Machine Learning". Journal of Marine Science and Engineering 11, nr 5 (30.04.2023): 961. http://dx.doi.org/10.3390/jmse11050961.

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It is of the utmost importance to accurately estimate different ships’ weights during their design stages. Additionally, lightship displacement (LD) data are not always easily accessible to shipping stakeholders, while other ships’ dimensions are within hand’s reach (for example, through data from the online Automatic Identification System (AIS)). Therefore, determining lightship displacement might be a difficult task, and it is traditionally performed with the help of mathematical equations developed by shipbuilders. Distinct from the traditional approach, this study offers the possibility of employing machine learning methods to estimate lightship displacement weight as accurately as possible. This paper estimates oil tankers’ lightship displacement using two ships’ dimensions, length overall, and breadth. The dimensions of oil tanker ships were collected from the INTERTANKO Chartering Questionnaire Q88, available online, and, because of similar block coefficients, all tanker sizes were used for estimation. Furthermore, multiple linear regression and extreme gradient boosting (XGBoost) machine learning methods were utilised to estimate lightship displacement. Results show that XGBoost and multiple linear regression machine learning methods provide similar results, and both could be powerful tools for estimating the lightship displacement of all types of ships.

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Hu, L., Z. G. Zhang, A. Mouraux i G. D. Iannetti. "Multiple linear regression to estimate time-frequency electrophysiological responses in single trials". NeuroImage 111 (maj 2015): 442–53. http://dx.doi.org/10.1016/j.neuroimage.2015.01.062.

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Jin, Xin, Suk-Kyo Hong . i Qiang Ma . "An Algorithm to Estimate Continuous-time Traffic Speed Using Multiple Regression Model". Information Technology Journal 5, nr 2 (15.02.2006): 281–84. http://dx.doi.org/10.3923/itj.2006.281.284.

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Yu, Lili, Liang Liu i Karl E. Peace. "Regression multiple imputation for missing data analysis". Statistical Methods in Medical Research 29, nr 9 (4.03.2020): 2647–64. http://dx.doi.org/10.1177/0962280220908613.

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Iterative multiple imputation is a popular technique for missing data analysis. It updates the parameter estimators iteratively using multiple imputation method. This technique is convenient and flexible. However, the parameter estimators do not converge point-wise and are not efficient for finite imputation size m. In this paper, we propose a regression multiple imputation method. It uses the parameter estimators obtained from multiple imputation method to estimate the parameter estimators based on expectation maximization algorithm. We show that the resulting estimators are asymptotically efficient and converge point-wise for small m values, when the iteration k of the iterative multiple imputation goes to infinity. We evaluate the performance of the new proposed methods through simulation studies. A real data analysis is also conducted to illustrate the new method.

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Jung, Chulwon, i Woongchul Choi. "Rapid Estimation of Battery Storage Capacity through Multiple Linear Regression". Batteries 9, nr 8 (12.08.2023): 424. http://dx.doi.org/10.3390/batteries9080424.

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Due to global warming issues, the rapid growth of electric vehicle sales is fully expected to result in a dramatic increase in returned batteries after the first use. Naturally, industries have shown great interest in establishing business models for retired battery reuse and recycling. However, they still have many challenges, such as high costs from the logistics of returned batteries and evaluating returned battery quality. One of the most important characteristics of a returned battery is the battery storage capacity. Conventionally, the battery’s energy capacity is measured through the low current full charging and discharging process. While this traditional measurement procedure gives a reliable estimate of battery storage capacity, the time required for a reliable estimate is unacceptably long to support profitable business models. In this paper, we propose a new algorithm to estimate battery storage capacity that can dramatically reduce the time for estimation through the partial discharging process. To demonstrate the applicability of the proposed algorithm, cylindrical and prismatic cells were used in the experiments. Initially, five indicators were selected from the voltage response curves that can identify battery storage capacity. Then, the five indicators were applied to principal component analysis (PCA) to extract dominant factors. The extracted factors were applied to a multiple linear regression model to produce a reliable estimation of battery storage capacity.

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Briševac, Zlatko, i Trpimir Kujundžić. "Models to estimate Brazilian indirect tensile strength of limestone in saturated state". Rudarsko-geološko-naftni zbornik 31, nr 2 (2016): 59–67. http://dx.doi.org/10.17794/rgn.2016.2.5.

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There are a number of methods for estimating physical and mechanical characteristics. Principally, the most widely used method is regression, but recently, more sophisticated methods such as neural networks have frequently been applied as well. This paper presents the models of a simple and a multiple regression and neural networks –types Radial Basis Function and Multiple Layer Perceptron, which can be used for the estimate of the Brazilian indirect tensile strength in saturated conditions. The paper includes the issues of collecting data for the analysis and modelling and an overview of the performed analysis with an efficacy assessment of the estimate for each model. After the assessment, the model which provided the best estimate was selected, including the model which could have the most wide-spread application in the engineering practice.

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Çankaya, Soner, Samet Eker i Samet Hasan Abacı. "Comparison of Least Squares, Ridge Regression and Principal Component Approaches in the Presence of Multicollinearity in Regression Analysis". Turkish Journal of Agriculture - Food Science and Technology 7, nr 8 (9.08.2019): 1166. http://dx.doi.org/10.24925/turjaf.v7i8.1166-1172.2515.

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The aim of this study was to compare estimation methods: least squares method (LS), ridge regression (RR), Principal component regression (PCR) to estimate the parameters of multiple regression model in situations when the underlying assumptions of least squares estimation are untenable because of multicollinearity. For this aim, the effect of some body measurements on body weights (height at withers and rumps, body length, chest width, chest girth and chest depth, front, middle and hind rump width) obtained from totally 85 Karayaka lambs at weaning period raised at Research Farm of Ondokuz Mayis University was examined. Mean square error, R2 value and significance of parameters were used to evaluate estimator performance. The multicollinearity, between front and middle rump width which were used to estimate live weight, was eliminated by using RR and PCR. Although research findings showed that RR method had the smallest MSE and the highest R2 value, the estimates of PCR were determined to be more consistent when the importance tests of parameters were taken into account. The results showed that principal component regression approach should be used to estimate the live weight of Karayaka lambs at weaning period.

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Bai, Jushan. "Estimating Multiple Breaks One at a Time". Econometric Theory 13, nr 3 (czerwiec 1997): 315–52. http://dx.doi.org/10.1017/s0266466600005831.

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Sequential (one-by-one) rather than simultaneous estimation of multiple breaks is investigated in this paper. The advantage of this method lies in its computational savings and its robustness to misspecification in the number of breaks. The number of least-squares regressions required to compute all of the break points is of order T, the sample size. Each estimated break point is shown to be consistent for one of the true ones despite underspecification of the number of breaks. More interestingly and somewhat surprisingly, the estimated break points are shown to be T-consistent, the same rate as the simultaneous estimation. Limiting distributions are also derived. Unlike simultaneous estimation, the limiting distributions are generally not symmetric and are influenced by regression parameters of all regimes. A simple method is introduced to obtain break point estimators that have the same limiting distributions as those obtained via simultaneous estimation. Finally, a procedure is proposed to consistently estimate the number of breaks.

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Chakraborty, Antik, Anirban Bhattacharya i Bani K. Mallick. "Bayesian sparse multiple regression for simultaneous rank reduction and variable selection". Biometrika 107, nr 1 (23.11.2019): 205–21. http://dx.doi.org/10.1093/biomet/asz056.

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Summary We develop a Bayesian methodology aimed at simultaneously estimating low-rank and row-sparse matrices in a high-dimensional multiple-response linear regression model. We consider a carefully devised shrinkage prior on the matrix of regression coefficients which obviates the need to specify a prior on the rank, and shrinks the regression matrix towards low-rank and row-sparse structures. We provide theoretical support to the proposed methodology by proving minimax optimality of the posterior mean under the prediction risk in ultra-high-dimensional settings where the number of predictors can grow subexponentially relative to the sample size. A one-step post-processing scheme induced by group lasso penalties on the rows of the estimated coefficient matrix is proposed for variable selection, with default choices of tuning parameters. We additionally provide an estimate of the rank using a novel optimization function achieving dimension reduction in the covariate space. We exhibit the performance of the proposed methodology in an extensive simulation study and a real data example.

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Simon, H., P. V. Bhave, J. L. Swall, N. H. Frank i W. C. Malm. "Determining the spatial and seasonal variability in OM/OC ratios across the US using multiple regression". Atmospheric Chemistry and Physics 11, nr 6 (30.03.2011): 2933–49. http://dx.doi.org/10.5194/acp-11-2933-2011.

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Abstract. Data from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network are used to estimate organic mass to organic carbon (OM/OC) ratios across the United States by extending previously published multiple regression techniques. Our new methodology addresses common pitfalls of multiple regression including measurement uncertainty, colinearity of covariates, dataset selection, and model selection. As expected, summertime OM/OC ratios are larger than wintertime values across the US with all regional median OM/OC values tightly confined between 1.80 and 1.95. Further, we find that OM/OC ratios during the winter are distinctly larger in the eastern US than in the West (regional medians are 1.58, 1.64, and 1.85 in the great lakes, southeast, and northeast regions, versus 1.29 and 1.32 in the western and central states). We find less spatial variability in long-term averaged OM/OC ratios across the US (90% of our multiyear regressions estimate OM/OC ratios between 1.37 and 1.94) than previous studies (90% fell between 1.30 and 2.10). We attribute this difference largely to the inclusion of EC as a covariate in previous regression studies. Due to the colinearity of EC and OC, we find that up to one-quarter of the OM/OC estimates in a previous study are biased low. Assumptions about OC measurement artifacts add uncertainty to our estimates of OM/OC. In addition to estimating OM/OC ratios, our technique reveals trends that may be contrasted with conventional assumptions regarding nitrate, sulfate, and soil across the IMPROVE network. For example, our regressions show pronounced seasonal and spatial variability in both nitrate volatilization and sulfate neutralization and hydration.

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Lee, Chia-Ying, Michael K. Tippett, Suzana J. Camargo i Adam H. Sobel. "Probabilistic Multiple Linear Regression Modeling for Tropical Cyclone Intensity". Monthly Weather Review 143, nr 3 (27.02.2015): 933–54. http://dx.doi.org/10.1175/mwr-d-14-00171.1.

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Abstract The authors describe the development and verification of a statistical model relating tropical cyclone (TC) intensity to the local large-scale environment. A multiple linear regression framework is used to estimate the expected intensity of a tropical cyclone given the environmental and storm conditions. The uncertainty of the estimate is constructed from the empirical distribution of model errors. NCEP–NCAR reanalysis fields and historical hurricane data from 1981 to 1999 are used for model development, and data from 2000 to 2012 are used to evaluate model performance. Seven predictors are selected: initial storm intensity, the change of storm intensity over the past 12 h, the storm translation speed, the difference between initial storm intensity and its corresponding potential intensity, deep-layer (850–200 hPa) vertical shear, atmospheric stability, and 200-hPa divergence. The system developed here models storm intensity changes in response to changes in the surrounding environment with skill comparable to existing operational forecast tools. Since one application of such a model is to predict changes in TC activity in response to natural or anthropogenic climate change, the authors examine the performance of the model using data that is most readily available from global climate models, that is, monthly averages. It is found that statistical models based on monthly data (as opposed to daily) with only a few essential predictors, for example, the difference between storm intensity and potential intensity, perform nearly as well at short leads as when daily predictors are used.

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Hirano, I. "Using multiple regression analysis to estimate the contributions of engine-radiated noise components". JSAE Review 20, nr 3 (lipiec 1999): 363–68. http://dx.doi.org/10.1016/s0389-4304(99)00015-6.

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Naoum, S., i I. K. Tsanis. "A multiple linear regression GIS module using spatial variables to model orographic rainfall". Journal of Hydroinformatics 6, nr 1 (1.01.2004): 39–56. http://dx.doi.org/10.2166/hydro.2004.0004.

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This paper aims to document the development of a new GIS-based spatial interpolation module that adopts a multiple linear regression technique. The functionality of the GIS module is illustrated through a test case represented by the island of Crete, Greece, where the models generated were applied to locations where estimates of annual precipitation were required. The response variable is ‘precipitation’ and the predictor variables are ‘elevation’, ‘longitude’ and ‘latitude’, or any combination of these. The module is capable of performing a sequence of tasks which will eventually lead to an estimation of mean areal precipitation and the total volume of precipitation. In addition, it can generate up to nine predictor variables and their parameters, and can estimate areal rainfall for a user-specified three-dimensional extent. The developed module performed satisfactorily. Precipitation estimates at ungauged locations were obtained using the multiple linear regression method in addition to some conventional spatial interpolation techniques (i.e. IDW, Spline, Kriging, etc.). The multiple linear regression models provided better estimates than the other spatial interpolation techniques.

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Caille, Agnès, Clémence Leyrat i Bruno Giraudeau. "A comparison of imputation strategies in cluster randomized trials with missing binary outcomes". Statistical Methods in Medical Research 25, nr 6 (11.07.2016): 2650–69. http://dx.doi.org/10.1177/0962280214530030.

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In cluster randomized trials, clusters of subjects are randomized rather than subjects themselves, and missing outcomes are a concern as in individual randomized trials. We assessed strategies for handling missing data when analysing cluster randomized trials with a binary outcome; strategies included complete case, adjusted complete case, and simple and multiple imputation approaches. We performed a simulation study to assess bias and coverage rate of the population-averaged intervention-effect estimate. Both multiple imputation with a random-effects logistic regression model or classical logistic regression provided unbiased estimates of the intervention effect. Both strategies also showed good coverage properties, even slightly better for multiple imputation with a random-effects logistic regression approach. Finally, this latter approach led to a slightly negatively biased intracluster correlation coefficient estimate but less than that with a classical logistic regression model strategy. We applied these strategies to a real trial randomizing households and comparing ivermectin and malathion to treat head lice.

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Su, Rina, Wala Du, Hong Ying, Yu Shan i Yang Liu. "Estimation of Aboveground Carbon Stocks in Forests Based on LiDAR and Multispectral Images: A Case Study of Duraer Coniferous Forests". Forests 14, nr 5 (11.05.2023): 992. http://dx.doi.org/10.3390/f14050992.

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The correct estimation of forest aboveground carbon stocks (AGCs) allows for an accurate assessment of the carbon sequestration potential of forest ecosystems, which is important for in-depth studies of the regional ecological environment and global climate change. How to estimate forest AGCs quickly and accurately and realize dynamic monitoring has been a hot topic of research in the forestry field worldwide. LiDAR and remote sensing optical imagery can be used to monitor forest resources, enabling the simultaneous acquisition of forest structural properties and spectral information. A high-density LiDAR-based point cloud cannot only reveal stand-scale forest parameters but can also be used to extract single wood-scale forest parameters. However, there are multiple forest parameter estimation model problems, so it is especially important to choose appropriate variables and models to estimate forest AGCs. In this study, we used a Duraer coniferous forest as the study area and combined LiDAR, multispectral images, and measured data to establish multiple linear regression models and multiple power regression models to estimate forest AGCs. We selected the best model for accuracy evaluation and mapped the spatial distribution of AGC density. We found that (1) the highest accuracy of the multiple multiplicative power regression model was obtained for the estimated AGC (R2 = 0.903, RMSE = 10.91 Pg) based on the LiDAR-estimated DBH; the predicted AGC values were in the range of 4.1–279.12 kg C. (2) The highest accuracy of the multiple multiplicative power regression model was obtained by combining the normalized vegetation index (NDVI) with the predicted AGC based on the DBH estimated by LiDAR (R2 = 0.906, RMSE = 10.87 Pg); the predicted AGC values were in the range of 3.93–449.07 kg C. (3) The LiDAR-predicted AGC values and the combined LiDAR and optical image-predicted AGC values agreed with the field AGCs.

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Thompson, Frances E., Douglas Midthune, Amy F. Subar, Lisa L. Kahle, Arthur Schatzkin i Victor Kipnis. "Performance of a short tool to assess dietary intakes of fruits and vegetables, percentage energy from fat and fibre". Public Health Nutrition 7, nr 8 (grudzień 2004): 1097–106. http://dx.doi.org/10.1079/phn2004642.

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AbstractObjectives:We describe the methods used to develop and score a 17-item ‘screener’ designed to estimate intake of fruit and vegetables, percentage energy from fat and fibre. The ability of this screener and a food-frequency questionnaire (FFQ) to measure these exposures is evaluated.Design:Using US national food consumption data, stepwise multiple regression was used to identify the foods to be included on the instrument; multiple regression analysis was used to develop scoring algorithms. The performance of the screener was evaluated in three different studies. Estimates of intakes measured by the screener and the FFQ were compared with true usual intake based on a measurement error model.Setting:US adult population.Subjects:For development of instrument, n = 9323 adults. For testing of instrument, adult men and women in three studies completing multiple 24-hour dietary recalls, FFQ and screeners, n = 484, 462 and 416, respectively.Results:Median recalled intakes for examined exposures were generally estimated closely by the screener. In the various validation studies, the correlations between screener estimates and estimated true intake were 0.5–0.8. In general, the performances of the screener and the full FFQ were similar; estimates of attenuation were lower for screeners than for full FFQs.Conclusions:When coupled with appropriate reference data, the screener approach described may yield useful estimates of intake, for both surveillance and epidemiological purposes.

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Morin, Antoine, Pierre-Paul Harper i Robert Henry Peters. "Microhabitat–Preference Curves of Blackfly Larvae (Diptera: Simuliidae): A Comparison of Three Estimation Methods". Canadian Journal of Fisheries and Aquatic Sciences 43, nr 6 (1.06.1986): 1235–41. http://dx.doi.org/10.1139/f86-153.

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Three analytical techniques are available to describe the observed response of stream-dwelling organisms to variations in their physicochemical environment: the incremental method, polynomial regression on a single factor, and multiple regression. The efficacy of these tools as descriptors of the responses has not been compared. We used the three methods to describe density and microhabitat–preference curves for Prosimulium mixtum/fuscum, Stegopterna mutata, and Simulium aureum in response to distance from the lake, current velocity, and water depth in a stream draining a Laurentian lake. The incremental method yielded the least precise estimates of density and biased estimates of optimal current velocity for two of the three species; multiple regression yielded the most precise estimates of density and unbiased estimates of optimal conditions, whereas polynomial regression on a single factor was intermediate. From this comparison, we suggest that the multiple-regression approach to estimate microhabitat–preference curves be used in developing optimal management stategies. Further, we suggest that rare species should be excluded from such analysis because of the low precision of density estimates for rare organisms.

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Zhang, Jianlong, Yanrong Zhuang, Hengyi Ji i Guanghui Teng. "Pig Weight and Body Size Estimation Using a Multiple Output Regression Convolutional Neural Network: A Fast and Fully Automatic Method". Sensors 21, nr 9 (6.05.2021): 3218. http://dx.doi.org/10.3390/s21093218.

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Pig weight and body size are important indicators for producers. Due to the increasing scale of pig farms, it is increasingly difficult for farmers to quickly and automatically obtain pig weight and body size. Due to this problem, we focused on a multiple output regression convolutional neural network (CNN) to estimate pig weight and body size. DenseNet201, ResNet152 V2, Xception and MobileNet V2 were modified into multiple output regression CNNs and trained on modeling data. By comparing the estimated performance of each model on test data, modified Xception was selected as the optimal estimation model. Based on pig height, body shape, and contour, the mean absolute error (MAE) of the model to estimate body weight (BW), shoulder width (SW), shoulder height (SH), hip width (HW), hip width (HH), and body length (BL) were 1.16 kg, 0.33 cm, 1.23 cm, 0.38 cm, 0.66 cm, and 0.75 cm, respectively. The coefficient of determination (R2) value between the estimated and measured results was in the range of 0.9879–0.9973. Combined with the LabVIEW software development platform, this method can estimate pig weight and body size accurately, quickly, and automatically. This work contributes to the automatic management of pig farms.

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Quartezani, Waylson Zancanella, Julião Soares de Souza Lima, Talita Aparecida Pletsch, Evandro Chaves de Oliveira, Sávio da Silva Berilli, Euzileni Mantoanelli, Robson Prucoli Posse i Luana Mendes Suci. "Multiple linear and spatial regressions to estimate the influence of Latosol properties on black pepper productivity". June 2019, nr 13(06) 2019 (20.06.2019): 857–62. http://dx.doi.org/10.21475/ajcs.19.13.06.p1424.

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There is little knowledge available on the best techniques for transferring spatial information such as stochastic interpolation and multivariate analyses for black pepper. This study applies multiple linear and spatial regression to estimate black pepper productivity based on physical and chemical properties of the soil. A multiple linear regression including all properties of a Latosol was performed and followed by variance analysis to verify the validity of the model. The adjusted variograms and data interpolation by kriging allowed the use of spatial multiple regression with the properties that were significant in the multiple linear regression. The forward stepwise method was used and the model was validated by the F-test. The influence of the Latosol properties was greater than the residual on the prediction of productivity. The model was composed by the physical properties fine sand (FS), penetration resistance (PR), and Bulk density (BD), and by the chemical properties K, Ca, and Mg (except for Mg in the spatial regression). The physical properties were of greater relevance in determining productivity, and the maps estimated by ordinary kriging and predicted by the spatial multiple regression were very similar in shape.

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McCormick, Kyle A. "Comparing Traditional Age Estimation at the Defense POW/MIA Accounting Agency to Age Estimation Using Random Forest Regression". Forensic Sciences 3, nr 2 (19.04.2023): 273–83. http://dx.doi.org/10.3390/forensicsci3020020.

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Age estimation from developmental traits is typically assessed in isolation, where an age range is derived from known individuals that exhibit that degree of fusion. There are no objective means for incorporating developmental evidence from multiple areas of the skeleton into one cohesive age estimate. This limitation is obvious in the casework at the Defense POW/MIA Accounting Agency (DPAA), where subjectivity is introduced into age estimates based on multiple age indictors. This holds true even when age is derived from one source, The 1957 study by McKern and Stewart). This study uses 388 individuals from the McKern and Stewart study and 41 individuals from the Battle of Tarawa and uses Random Forest Regression (RFR) to estimate an age interval using multiple age indicators. These RFR estimates are compared to age estimates from the Forensic Anthropology Reports (FARs). Overall, FAR age estimates are more accurate (92.7%) than those from the two RFR models (80.5% and 76.6%). This increase in accuracy comes at the cost of some precision (FARs average age interval of 8.1 years and RFR average age intervals of 6.3 and 6.4 years). The RFR models prefer age indicators with late fusion, such as the medial clavicle, and the pubic symphysis, which exhibit a combination of developmental and degenerative ages in morphology. Some avenues for further research are discussed.

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Khairul, Khairul, Asyahri Hadi Nasyuha, Ali Ikhwan, Moustafa H. Aly i Ahyanuardi Ahyanuardi. "Implementation of Multiple Linear Regression to Estimate Profit on Sales of Screen Printing Equipment". JURNAL INFOTEL 15, nr 2 (7.06.2023): 55–61. http://dx.doi.org/10.20895/infotel.v15i2.934.

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Traditional marketing strategies are no longer practical to implement because the process requires more costs and time to disseminate information which is much longer. Data Mining is a science that discusses knowledge from previous data to estimate the amount of production in the future. Data mining is a term used to find hidden knowledge in databases. “Data mining is a semi-automatic process using statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify valuable and helpful information in large databases. It is necessary to solve the problem by using one of the five methods in the field of Data Mining, namely the Multiple Linear Regression method, where this method will analyze the variables that have an influence and can make estimates. Multiple Linear Regression Is a method that can be used to analyze data and obtain meaningful conclusions about a relationship between one variable and another. This relationship is generally expressed by a mathematical equation expressing the relationship between the independent and dependent variables in the form of a simple equation

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Chang, Hyunho, Dongjoo Park, Younginn Lee i Byoungjo Yoon. "Multiple time period imputation technique for multiple missing traffic variables: nonparametric regression approach". Canadian Journal of Civil Engineering 39, nr 4 (kwiecień 2012): 448–59. http://dx.doi.org/10.1139/l2012-018.

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The objective of this study is to introduce an effective and practical model, based on non-parametric regression, to instantaneously estimate multivariate imputations replacing multiple missing variables during multiple time periods. The developed model was essentially designed for system-oriented, real-world applications. In an empirical study with real-world data, the proposed model, on the whole, outperformed the seasonal auto-regressive integrated moving average (ARIMA). The analysis of the results indicates that the introduced model was more applicable to multivariate imputation during multiple time intervals than that of ARIMA. In addition, it was revealed that ARIMA could somewhat deform the relationship between the volume (q) and speed (s), whereas the developed model reproduced the q–s relationship more similarly than ARIMA. Moreover, the proposed model is very simple and does not require system operators to input or recalibrate any external parameters because it was developed for applications of real data management systems.

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Ikeda, Keigo, Jyunpei Kuroda, Daigo Uchino, Kazuki Ogawa, Ayato Endo, Taro Kato, Hideaki Kato i Takayoshi Narita. "A Study of a Ride Comfort Control System for Ultra-Compact Vehicles Using Biometric Information". Applied Sciences 12, nr 15 (24.07.2022): 7425. http://dx.doi.org/10.3390/app12157425.

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We investigated whether there is a correlation between the comfort felt by occupants and the comfort estimated from biological information obtained by experiments to improve the ride quality of an ultra-compact vehicle. A single frequency in the vertical direction can help to estimate occupant comfort. However, we hypothesized that the study of a single frequency was insufficient. We oscillated the occupants with vibrations containing multiple vibration frequencies and obtained biometric information. The vibration frequency was set based on the difference in ride quality felt by humans. Biometric information was obtained using a cerebral hemodynamic meter and electrocardiogram. Acquiring multiple types of biometric information helps to more accurately estimate the psychological state. After the experiment, we obtained a subjective evaluation of comfort against vibrations using the visual analog scale (VAS). The biometric information obtained by the experiment was cluster-analyzed, and experimental participants with similar characteristics of the biometric information were grouped. Multiple regression analysis was performed based on the values of the typical biometric information of the cluster. Comfort was estimated from biometric information using multiple regression analysis. A correlation was confirmed between measured and estimated VAS scores.

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Eum, Seung-Hoon, Hu-Rak Park, Jakyeom Seo, Seong-Keun Cho, Sun-Jin Hur i Byeong-Woo Kim. "Multiple Regression Analysis to Estimate the Unit Price of Hanwoo (Bos taurus coreanae) Beef". Korean Journal for Food Science of Animal Resources 37, nr 5 (31.10.2017): 663–69. http://dx.doi.org/10.5851/kosfa.2017.37.5.663.

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Bocianowski, Jan. "The use of weighted multiple linear regression to estimate QTL-by-QTL epistatic effects". Genetics and Molecular Biology 35, nr 4 (2012): 802–9. http://dx.doi.org/10.1590/s1415-47572012005000071.

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