Three prediction models were calculated based on the data to predict the risk of recurrent lateral patellar dislocation. The inter-observer and intra-observer reliability of each measurement parameter was evaluated. The predictive capacity of the three-prediction model was investigated using the receiver...
Multiple linear regressionSARIMAThe study utilizes 30years of monthly average discharge data from the Nigeria Hydrological Service Agency and meteorological data (temperature, rainfall, and evaporation) from the Nigeria Meteorological Agency, spanning from 1988 to 2017 to compare the predictive accuracy of...
Sample 1 (6,508 fetuses) was used to compute a new linear regression model estimating fetal weight (FW) from ultrasound measurements. Sample 2, with 705 fetuses, was used to compare the accuracy of the new equation with Hadlock’s equation. Sample 3 (1,461 fetuses) was used to ...
We more rigorously evaluated our multi-scale model by assessing accuracy within geographic subsections of the nesting range and found it was still high to very high (86-100%), as most studies rarely exceed 85% accuracy. The results confirm that logistic regression can be very useful for ...
model selectionmixed covariatesThe paper proposes a cross-validation method to address the question of specification search in a multiple nonlinear quantile regression framework. Linear parametric, spline-based partially linear and kernel-based fully nonparametric specifications are contrasted as competitors ...
Differences between the multiple linear regression model with Corrected R~2 and Corrected F and the ordered variable regression model with R~2 and F when i... GL Baird,SL Bieber - 《Journal of Modern Applied Statistical Methods》 被引量: 0发表: 2019年 Comparison of prediction methods for mul...
Model selection We have applied multiple linear regression model, including age, sex, and BMI, as fixed factors for lipid profile traits. The stepwise approach, which is a combination of the forward and backward selection, considered all three above covariates to be included in the predictor model...
Regressors include linear models (Bayesian ridge regression (BR) and linear regression with elastic net regularization (EN)), random forest (RF), kernel ridge regression (KRR) and two types of neural networks, graph convolutions (GC) and gated graph networks (GG). We present numerical evidence...
In model 2, only biallelic markers are considered and the value þ ½aj is arbitrarily assigned to the first allele, whereas À½aj is assigned to the second allele. Hence, the effects of homozygotes for the first allele are þ aj, whereas those of the alternate homozygotes are ...
However, even if the GWS allows adequate prediction of genetic effects, the heritability has importance in the model, because the lower the trait heritability the lower the phenotypic data accuracy and, therefore, lower the heritability of marker effects. Consequently, the lower will be the ability...