介绍样本选择模型及其估计方法。
To introduce sample selection model and its estimation method.
应用样本选择模型对模拟数据进行分析，并与传统线性回归模型进行比较。
The missing data were analysed with sample selection model and compared with traditional linear regression model.
利用UCI标准数据集对本文所提出的样本选择模型进行测试，实验结果证明了该模型的有效性。
The proposed sample selection model is tested on UCI dataset, which proves the efficiency of the model.
在更新话题模型的过程中，尝试确定性和不确定性相结合的方式作为样本选择标准。
In the process of updating topic model, a method combining certainty and uncertainty is used to select samples.
采用归一化数据处理方法，选择神经网络的训练样本，建立基于BP神经网络的居民消费价格指数预测的数学模型。
Adopted the data processing method of the normalization, choose the training sample of the neural network, the mathematical model of the consumer price index based on BP nerve network predicts set up.
本文在中、小样本试验数据下，研究响应模型的选择问题。
Binary response model choice is researched with the data of media or small sample size.
为了提高模型的预测精度，在训练样本的选择上还应具有一定的代表性。
In order to improve the accuracy of model prediction, the training samples should be representatively prepared.
选择变量，建立回归模型，代入样本数据进行回归分析。
Changing variable, set up a regression model, take regression analyse using sample data.
在这个研究领域里，基于基因表达数据的样本分类扮演着很重要的角色，它一般具有两个关键步骤：基因选择和分类模型设计。
In these research areas, sample classification based on gene expression data is acting a very important role, it generally has two pivotal steps: gene selection and Classifier design.
本文提出了一个以信息过载为中介变量的概念模型，并设置操作变量选择样本进行调查和统计分析。
This paper proposes a conceptual model with information overload as an intervening variable and some operational variables to conduct sample investigation and statistical analysis.
介绍了代表性校正集样本的选择、统计学方法校正模型建立等近红外光谱的工作原理，以及利用近红外光谱进行材料鉴别和定性、 定量分析方法。
The main principle of near-infrared spectroscopy applied in the selection of representative polypropylene pellet samples and the establishment of statistical standardization model were introduced.
该算法引入BP神经网络模型，根据用户反馈分值选择样本训练神经网络。
The algorithm used the BP neural network, and the neural network was trained by samples which were selected according to user feedback score.
为了提高模型的预测精度，在训练样本的选择上还应具有一定的代表性。
In order to improve the accuracy of model prediction, the training sample...
基于这一问题本文通过构造统计量对所给的样本点进行选择，剔除对模型的构造有很大影响力的样本，从而获得一个相对合理的样本空间。
Based on this problem, this article selects the sample points by constructing statistics. First, it removes the outliers to have a relatively reasonable sample space.
强度预测时，根据实际数据自适应选择拟和模型，这样在大样本量混凝土强度预测中得到了较好的精度。
In this way, the forecasting accuracy of concrete strength inferences with smaller margins of error to many sample data has been...
强度预测时，根据实际数据自适应选择拟和模型，这样在大样本量混凝土强度预测中得到了较好的精度。
In this way, the forecasting accuracy of concrete strength inferences with smaller margins of error to many sample data has been...
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