A couple of problems we have to solve in QSAR modeling: the first is related to the selection of the most relevant descriptors, and the second is the generation of the most reliable models. Contrary to other methods, our procedure, which is based on multiregression (MR) analysis, solves ...
By Property 0 ofLeast Squares in Multiple Regression, the sample covariance matrix can be expressed by the matrix equation whereX̄is the 1 ×krow vector of sample means. Also, the correlation matrix can be expressed as whereD= the 1 ×krow vector of sample standard deviations. Example Exam...
MDistSq(R1, R2, R3, FALSE): the Mahalanobis distance squared between the 1 ×krow vector R2 and the 1 ×krow vector R3 based on the covariance matrix contained in thek×krange R1. Examples Example 1: Assuming that the data in Figure 1 is bivariate normally distributed, estimate...
Multivariate Analysis Regression Residual: Difference between measured and calculated Y-values Multivariate Analysis Regression Residuals: Represents error in the fit for each data point. But the sum of the residuals tends to approach zero so it will not work for finding the overall error in the fit...
8.Using Double Logistic Regression to Improve Discriminant Efficiency;利用Logistic二次回归法提高判别分析效率 9.The Establishment and Evaluation of Logistic Regression Analysis Model in Excel;应用Excel完成logistic回归分析及其评价 10.Application of Logistic Model to Discriminant Analysis;Logistic回归模型在判别分...
Least absolute shrinkage and selection operator regression (LASSO) [18] is a multivariate embedded feature selection method. In a linear regression equation, the LASSO method adds a penalty term that discourages the model from assigning too much importance to any single feature. The penalty applied...
With the increasing demand for digital products, processes and services the research area of automatic detection of signal outliers in streaming data has g
Zhang, Y.et al.Data regression framework for time series data with extreme events. in:2021 IEEE International Conference on Big Data (Big Data), pp. 5327–5336,https://doi.org/10.1109/BigData52589.2021.9671387(2021). Shih, S.-Y., Sun, F.-K. & Lee, H.-Y. Temporal pattern attention...
In the context of forecasting the S&P500 and oil ETFs, the DCC-REGARCH records the highest R2 in 9 out of the 12 cases, while the MHEWMA model, leading in 3 out of the 12 cases. Table 6. Forecast Regression R-squared Value. Panel A. Forecast Regression R-squared Value for ...
Zhang, Y.et al.Data regression framework for time series data with extreme events. in:2021 IEEE International Conference on Big Data (Big Data), pp. 5327–5336,https://doi.org/10.1109/BigData52589.2021.9671387(2021). Shih, S.-Y., Sun, F.-K. & Lee, H.-Y. Temporal pattern attention...