Читать книгу Rank-Based Methods for Shrinkage and Selection - A.K. Saleh Md.Ehsanes, A. K. Md. Ehsanes Saleh - Страница 7

1 Chapter 1Table 1.1 Comparison of mean and median on three data sets.Table 1.2 Examples comparing order and rank statistics.Table 1.3 Belgium telephone data set.Table 1.4 Comparison of LS and Theil estimations...Table 1.5 Walsh averages for the set {0.1, 1.2, 2.3, 3.4, 4.5, 5.0, 6.6, 7.7, 8.8, 9.9, 10.5}.Table 1.6 The individual terms that are summed in Dn(β) and Ln(β) for the telephone data set.Table 1.7 The terms that are summed in Dn(θ) and Ln(θ) for the telephone data set.Table 1.8 The LS and R estimations of slope and intercept...Table 1.9 Interpretation of L₁/L₂ loss and penalty functions

2 Chapter 2Table 2.1 Swiss fertility data set.Table 2.2 Swiss fertility data set definitions.Table 2.3 Swiss fertility estimates and standard errors for least squares (LS) and rank (R).Table 2.4 Swiss data subset ordering using | t.value |Table 2.5 Swiss data models with adjusted R² values.Table 2.6 Estimates with outliers from diabetes data before standardization.Table 2.7 Estimates. MSE and MAE for the diabetes dataTable 2.8 Enet estimates, training MSE and test MSE as a function of α for the diabetes data

3 Chapter 3Table 3.1 The ADRE values of ridge for different values of Δ²Table 3.2 Maximum and minimum guaranteed ADRE of the preliminary test R-estimator for different values of α.Table 3.3 The ADRE values of the Saleh-type R-estimator for λmax*=2π and different Δ²Table 3.4 The ADRE values of the positive-rule Saleh-type R-estimator for λmax*=2π and different Δ²Table 3.5 The ADRE of all R-estimators for different Δ²

4 Chapter 4Table 4.1 Table of (hypothetical) corn crop yield from six different fertilizers.Table 4.2 Table of p-values from pairwise comparisons of fertilizers.

5 Chapter 8Table 8.1 The VIF values of the diabetes data set.Table 8.2 Estimations for the diabetes data^*. (The numbers in parentheses are the corresponding standard errors).

6 Chapter 11Table 11.1 LLR algorithm.Table 11.2 RLR algorithm.Table 11.3 Car data set.Table 11.4 Ridge accuracy vs. λ₂ with n = 337 (six outliers).Table 11.5 RLR-LASSO estimates vs. λ₁ with number of correct predictions.Table 11.6 Sample of Titanic training data.Table 11.7 Specifications for the Titanic data set.Table 11.8 Number of actual data entries in each column.Table 11.9 Cross-tabulation of survivors based on sex.Table 11.10 Cross-tabulation using Embarked for the Titanic data set.Table 11.11 Sample of Titanic numerical training data.Table 11.12 Number of correct predictions for Titanic training and test sets.Table 11.13 Train/test set accuracy for LLR-ridge. Optimal value at (*).Table 11.14 Train/test set accuracy for RLR-ridge. Optimal value at (*).Table 11.15 Train/Test set accuracy for LLR-LASSO. Optimal value at (*).Table 11.16 Train/test set accuracy for RLR-LASSO. Optimal value at (*).

7 Chapter 12Table 12.1 RNN-ridge algorithm.Table 12.2 Interpretation of the confusion matrix.Table 12.3 Confusion matrix for Titanic data sets using RLR...Table 12.4 Number of correct predictions (percentages) and AUROC of LNN-ridge.Table 12.5 Input (x_ij), output (y_i) and predicted values p~(x_i) for the image classification problem.Table 12.6 Confusion matrices for RNNs and LNNs (test size = 35).Table 12.7 Accuracy metrics for RNNs vs. LNNs (test size = 35).Table 12.8 Train/test set accuracy for LNNs. F₁ score is associated with the test set.Table 12.9 Train/test set accuracy for RNNs. F₁ score is associated with the test set.Table 12.10 Confusion matrices for RNNs and LNNs (test size = 700).Table 12.11 Accuracy metrics for RNNs vs. LNNs (test size = 700).Table 12.12 MNIST training with 0 outliers.Table 12.13 MNIST training with 90 outliers.Table 12.14 MNIST training with 180 outliers.Table 12.15 MNIST training with 270 outliers.Table 12.16 Table of responses and probability outputs.