regression coefficient

1. In this paper, the smallest risk estimators of regression coefficient in the linear model are investigated.
   研究了线性模型中回归系数的最小风险估计问题。
2. The Linear Minimax Estimation of Regression Coefficient Under Balanced Loss Function
   平衡损失下回归系数的线性Minimax估计
3. Results: Standardization regression coefficient from big to small in proper order were: oxygenation index, CT, body temperature of the admission day, BMI were the most influence on ALI;
   结果：按标准化回归系数绝对值由大到小：氧合指数、CT、入院体温、体质量指数对ALI的影响最大；
4. In this article we consider the influence of the regression coefficient the arbitrary rank restricted linear model based on linear transformations of the dependent variable.
   本文应用凸分析理论与方法对一类带线性约束的非线性规划问题提出了一种算法，并就本算法对二次规划问题进行了探讨。
5. In this paper, we consider the Minimax admissible estimates of the multivariate regression coefficient in the constraint growth curve model under a matrix loss function.
   本文在一种新的矩阵损失函数下，给出了随机回归系数和参数同时估计在齐次线性估计类和非齐次线性估计类中可容许的充分必要条件。
6. Then, a multivariate regression model was developed between measured and calculated results of geostress at measurement points, and the regression coefficient of the model was derived.
   在应力实测值和应力计算回归值之间建立多元回归模型，求出最优回归系数，从而获得初始地应力场分布规律。
7. All Admissible Linear Estimators of Regression Coefficient in a Linear Model with Respect to an Inequality Constraint
   不等式约束下线性模型中回归系数的所有可容许估计
8. The linear minimax estimation of regression coefficient in a general Gauss-Markov Model under balanced loss function
   平衡损失下一般Gauss-Markov模型中回归系数的线性Minimax估计
9. The Minimax Estimate for Restricted Multivariate Regression Coefficient Under Matrix Loss Function
   矩阵损失下带约束的多元回归系数的Minimax估计
10. To determine the rank of regression coefficient matrix in a multivariate linear regression model, a model selection procedure is proposed based on the M-estimation.
    针对只有一个参数是以非线性形式出现的纯非线性回归模型，提出一种新方法确定模型中的回归系数。
11. Also, we need to know how much individual stocks are correlated with rm; we measure that by the regression coefficient.
    我们必须清楚，有多少个股与市场总体收益率相关；，我们用回归系数，即β系数来表示。
12. Estimating the precipitation using new regression coefficient estimation and new correction method of errors
    将新回归系数估计法和新误差订正法应用于雷达估测降水
13. In this paper we construct the empirical Bayes estimator of regression coefficient in multiple linear model, by using the kernel estimator method of multiple density function and its partial derivative. We obtain the uniform convergence rate of the estimator.
    利用多元密度函数及其导数的核估计方法，建立了多元线性模型回归系数的经验Bayes估计，并给出了这种估计的一致收敛速度。
14. In this paper, Bayes linear estimates of regression coefficient in a variance component model are studied both under the quadratic loss function and matrix loss function.
    本文研究了方差分量模型中在平方和损失函数和矩阵损失函数下回归系数的线性Bayes估计。
15. Starting with the equivalence condition of admissibility, this paper discusses respectively the linear biased estimate of the regression coefficient of thee models: the G-M Linear Regression Model, the Multivariate Linear Regression and the Mixed-effect Coefficient Linear Model.
    本文以可容许性的等价条件为起点，分别讨论了G-M线性回归模型、多元线性回归模型和混合系数线性模型的回归系数的一类线性有偏估计。
16. This paper presents regression model with same regression coefficient and different regression constants.
    本文讨论回归系数相同，回归常数不同的回归分析模型。
17. In this paper, the estimation of the direction of the regression coefficient of general regression model is considered.
    本文考虑一般回归模型中回归系数的方向的估计问题。
18. This paper infer a series of recursive equation based on lease square estimation in order to reduce computing amount, include recurrence equation of regression coefficient, total dispersion square sum, residual square sum, regression square sum etc.
    为了减少计算量，本文推导了基于最小二乘估计的一系列递推公式，包括回归系数、总离差平方和、残差平方和、回归平方和等的递推公式。
19. Regression coefficient of NC and scale N arrived significance level.
    ⑶NC和N维度的回归系数达到显著性水平。
20. To determine the rank of regression coefficient matrix in a multivariate linear regression model, a model selection procedure is proposed based on the M-estimation. It is derived that the estimator of the rank of regression coefficient matrix under some mild conditions is strongly consistent.
    为了确定多重线性回归模型中回归系数矩阵的秩，本文提出了一个基于M估计的模型选择程序，且在较弱的条件下建立了回归系数矩阵的秩的估计的强相合性。
21. No significant correlation existed between yield and regression coefficient b stable parameter.
    产量与稳定性参数回归系数b之间并无明显相关性。
22. In this paper, the sufficient condition of consistency of MLE of regression coefficient in linear models is established. It is interest that the condition is only given by the design sequence, like LSE.
    讨论了线性模型中回归系数的MLE的相合性的充分条件，有趣的是，与LSE相似，所得的条件只与试验点列有关。
23. Results: The regression coefficient of linearity of the GPS method and the NBT method was 0.969 and 0.977, respectively.
    结果：GPS法和NBT法的线性回归系数分别为0.969和0.977。
24. An important result obtained by discussion of linear biased estimation of regression coefficient is the linear biased estimation for the mininum mean square error.
    文中讨论的是回归系数的线性有偏估计问题，并给出了一个重要结果，这就是最小均方误差意义下的回归系数的线性有偏估计。
25. This paper introduces the global stationary test of regression relation and the stationary test of each regression coefficient changes with spatial location in geographically weighted regression model by theory of least squares estimate.
    利用最小二乘估计理论，给出了在地理加权回归模型（GWR模型）中回归关系的全局平稳性检验方法和各回归系数随空间位置变化的平稳性检验方法。
26. By constructing the logit function between the dependent variable and argument, doing regression analysis, we obtain the regression coefficient, thereby we obtain the influence degree of every dependent variable.
    通过构建因变量和自变量之间的logit函数，进行回归分析，得到因变量的回归系数，从而得到各个因变量对就业的影响程度。
27. Estimating regression coefficient is the most fundamental issue in linear regression model from theory and application.
    从理论上和应用上来看，线性回归模型中最基本的问题是估计回归参数。
28. This thesis discussed the estimation of regression coefficient in the linear model.
    本文研究线性回归模型中回归系数的估计。
29. According to the standard partial regression coefficient, among these factors, the work condition and individual development have influence on the stability of the workers. 3.
    在这几个因素中，从标准偏回归系数的值来看，以工作环境和个人发展对工作人员稳定性影响作用较大。

noun

1. when the regression line is linear (y = ax + b) the regression coefficient is the constant (a) that represents the rate of change of one variable (y) as a function of changes in the other (x)