While interpretingthe coefficientof one of the predictors (saya continuous variable X1) of a linear model — with multiple explanatory variables (X1, X2, …..Xn) predicting the value of an outcome variable (Y) — you must have used statements such as the following: “Controlling for ot...
aremaining factor 剩余的因素[translate] aDemographic variablesc were also entered into the regression model to control for their relationships with the dependent variable 人口统计的variableswere也加入回归模型为他们的与因变量的关系控制[translate]
For regression-based control design of the second kind, machine learning is exploited to identify arbitrary nonlinear control laws that minimize the cost function of the system. In this case, it is not necessary to know the model, control law structure, or the optimizing actuation command, and ...
Table 6. A probabilistic model for the automobile front axle. VariableMeanCOV (%) a (mm) 12 6 b (mm) 65 32.5 t (mm) 14 7 h (mm) 85 42.5 M (N.mm) 3.5 × 106 1.75 × 105 T (N.mm) 3.1 × 106 1.55 × 105 In this study, the limit str...
6. In our proposed design, these residuals are obtained through the implementation of an SVM regression model. Following the methodology outlined by Tighkhorshid et al.6, the statistic for the RAEWMA control chart is constructed using the Standardized Residuals from the SVM regression model, ...
Control-function linear regression Number of obs = 6,000 Wald chi2(4) = 1973.78 Prob > chi2 = 0.0000 R-squared = 0.2432 Root MSE = 1.2172 Endogenous variable model: Probit: 1.ins Robust lndrug Coefficient std. err. z P>|z| [95% conf. interval]lndrug ...
2016. Regression-based Monte Carlo methods for stochastic control models: variable annuities with lifelong guarantees. Quantitative Finance, 16(6), 905-928.Huang Y.T., Kwok, Y.K., 2014. Regression-based Monte Carlo methods for stochastic control models: Variable annuities with lifelong guarantees....
For each of these analyses, the alpha level was set at 0.05. Finally, we investigated the relationship between \(\omega\) and weight status on a continuous scale by running a post hoc linear regression model including BMI and BMI2 as orthogonal predictors....
Gaussian process regressionTorque predictionInduction motor is used for different applications in industries such as grinding, milling, mining and automation. ... NT Alberto,M Mistry,F Stulp 被引量: 0发表: 2014年 A Gaussian process regression based on variable parameters fuzzy dominance genetic algor...
model-based calculations, or other custom functions of the sensory signals. In the low-level, position or torque controllers are used to carry out the desired actions. In addition to a more detailed description of these methods, the variants of implementation within each one are also compared an...