Sufficient dimension reduction
View article: Loadings of the latent dimension of cardiometabolic health.
Loadings of the latent dimension of cardiometabolic health. Open
Risk factor loadings and brain loadings in the main/mixed sample for cortical thickness (left) and gray matter volume (right). The arrows depict the Spearman correlation of the loadings across models. The loadings represent the average ove…
View article: Estimating the true number of principal components under the random design
Estimating the true number of principal components under the random design Open
Principal component analysis (PCA) is frequently employed as a dimension reduction tool when the number of covariates is large. However, the number of principal components to be retained in PCA is typically determined in a researcher-depen…
View article: Estimating the true number of principal components under the random design
Estimating the true number of principal components under the random design Open
Principal component analysis (PCA) is frequently employed as a dimension reduction tool when the number of covariates is large. However, the number of principal components to be retained in PCA is typically determined in a researcher-depen…
View article: EuroQol 5 Dimension score at each measurement timepoint.
EuroQol 5 Dimension score at each measurement timepoint. Open
EuroQol 5 Dimension score at each measurement timepoint.
View article: Multi-variable linear regression analyses on factors associated with dimension total scores.
Multi-variable linear regression analyses on factors associated with dimension total scores. Open
Multi-variable linear regression analyses on factors associated with dimension total scores.
View article: Active Variable Selection and Dimension Reduction Aided Design in History Matching
Active Variable Selection and Dimension Reduction Aided Design in History Matching Open
In this thesis, uncertainty quantification techniques such as Gaussian process emulation and history matching were applied to two complex simulators. The first application, a high-dimensional output model which featured a collaboration bet…
View article: Stepwise Regression Coefficients for Dimension 1 of Waley’s and Pound’s Translations.
Stepwise Regression Coefficients for Dimension 1 of Waley’s and Pound’s Translations. Open
Stepwise Regression Coefficients for Dimension 1 of Waley’s and Pound’s Translations.
View article: Parameters of the Stepwise Regression Models for the Top Variable Predicting the Scores of Dimension 6 of Waley’s and Pound’s Translations.
Parameters of the Stepwise Regression Models for the Top Variable Predicting the Scores of Dimension 6 of Waley’s and Pound’s Translations. Open
Parameters of the Stepwise Regression Models for the Top Variable Predicting the Scores of Dimension 6 of Waley’s and Pound’s Translations.
View article: Stepwise Regression Coefficients for Dimension 6 of Waley’s and Pound’s Translations.
Stepwise Regression Coefficients for Dimension 6 of Waley’s and Pound’s Translations. Open
Stepwise Regression Coefficients for Dimension 6 of Waley’s and Pound’s Translations.
View article: Parameters of the Stepwise Regression Models for the Top Five Variables Predicting the Scores of Dimension 1 of Waley’s and Pound’s Translations.
Parameters of the Stepwise Regression Models for the Top Five Variables Predicting the Scores of Dimension 1 of Waley’s and Pound’s Translations. Open
Parameters of the Stepwise Regression Models for the Top Five Variables Predicting the Scores of Dimension 1 of Waley’s and Pound’s Translations.
View article: Stepwise Regression Coefficients for Dimension 2 of Waley’s and Pound’s Translations.
Stepwise Regression Coefficients for Dimension 2 of Waley’s and Pound’s Translations. Open
Stepwise Regression Coefficients for Dimension 2 of Waley’s and Pound’s Translations.
View article: Parameters of the Stepwise Regression Models for the Top Five Variables Predicting the Scores of Dimension 2 of Waley’s and Pound’s Translations.
Parameters of the Stepwise Regression Models for the Top Five Variables Predicting the Scores of Dimension 2 of Waley’s and Pound’s Translations. Open
Parameters of the Stepwise Regression Models for the Top Five Variables Predicting the Scores of Dimension 2 of Waley’s and Pound’s Translations.
View article: Evaluation metrics for different output dimensions of dimension reduction networks.
Evaluation metrics for different output dimensions of dimension reduction networks. Open
Evaluation metrics for different output dimensions of dimension reduction networks.
View article: Weighted proportional odds logistic regression comparing EQ-5D-5L dimension levels for 2012 and 2020. Reference group: age 18-29, no lockdown.
Weighted proportional odds logistic regression comparing EQ-5D-5L dimension levels for 2012 and 2020. Reference group: age 18-29, no lockdown. Open
Weighted proportional odds logistic regression comparing EQ-5D-5L dimension levels for 2012 and 2020. Reference group: age 18-29, no lockdown.
View article: A principal mixed-order moments method for CKMS in dimension reduction
A principal mixed-order moments method for CKMS in dimension reduction Open
View article: Dimension Reduction and Error Estimates
Dimension Reduction and Error Estimates Open
The aim of this article is twofold: to provide asymptotic behavior and error estimates results for an elliptic dimension reduction problem. The problem is posed in a bounded and thin domain , where when …
View article: Dimension reduction for the estimation of the conditional tail index
Dimension reduction for the estimation of the conditional tail index Open
We are interested in the relationship between the large values of a real random variable and its associated multidimensional covariate, in the context where the conditional distribution is heavy‐tailed. Estimating the positive conditional …
View article: Binary logistic regressions for the full set of traditional analysis (model 1) and the extension of this model with either the correlation dimension (model 2), kAR (model 3), and kRA (model 4). Low p-values are indicated with <sup><i>o</i></sup> (p < 0.1), <sup>*</sup> (p < 0.05), or <sup>**</sup> (p < 0.001). The Akaike Information Criterion and Bayesian Information Criterion are calculated in rows AIC and BIC.
Binary logistic regressions for the full set of traditional analysis (model 1) and the extension of this model with either the correlation dimension (model 2), kAR (model 3), and kRA (model 4). Low p-values are indicated with <sup><i>o</i></sup> (p < 0.1), <sup>*</sup> (p < 0.05), or <sup>**</sup> (p < 0.001). The Akaike Information Criterion and Bayesian Information Criterion are calculated in rows AIC and BIC. Open
Binary logistic regressions for the full set of traditional analysis (model 1) and the extension of this model with either the correlation dimension (model 2), kAR (model 3), and kRA (model 4). Low p-values are indicated with o…
View article: The effect of modeling dimension on prediction accuracy.
The effect of modeling dimension on prediction accuracy. Open
The effect of modeling dimension on prediction accuracy.
View article: Different dimension reduction of a single category model prediction result.
Different dimension reduction of a single category model prediction result. Open
Different dimension reduction of a single category model prediction result.
View article: Partially sufficient dimension reduction for MEPS data.
Partially sufficient dimension reduction for MEPS data. Open
Partially sufficient dimension reduction for MEPS data.
View article: Estimated nonparametric components for simulation studies.
Estimated nonparametric components for simulation studies. Open
a: Estimated g(x) for case (A, I). b: Estimated f(t) for case (A, I). c: Estimated g(x) for case (B, I). d: Estimated f(t) for case (B, I). e: Estimated f(t) for case II…
View article: Sufficient Dimension Reduction for the Conditional Quantiles of Functional Data
Sufficient Dimension Reduction for the Conditional Quantiles of Functional Data Open
View article: On Metric Choice in Dimension Reduction for Fréchet Regression
On Metric Choice in Dimension Reduction for Fréchet Regression Open
Summary Fréchet regression is becoming a mainstay in modern data analysis for analysing non‐traditional data types belonging to general metric spaces. This novel regression method is especially useful in the analysis of complex health data…
View article: Parametric study results for varying numbers of FMs and latent dimensions on the H&N tumor model.
Parametric study results for varying numbers of FMs and latent dimensions on the H&N tumor model. Open
The average nodal displacement errors (i.e., mean nodal error and max nodal error) are plotted together with error bars. (a) Varying number of FMs, with latent dimension of 5. (b) Varying latent dimension with 5 FMs.
View article: Dimension Reduction for Symbolic Regression
Dimension Reduction for Symbolic Regression Open
Solutions of symbolic regression problems are expressions that are composed of input variables and operators from a finite set of function symbols. One measure for evaluating symbolic regression algorithms is their ability to recover formu…
View article: Using Fractal-Fractional operator, the value of <i>R</i> ( <i>t</i> ) at multiple dimensions with different fractional values (a) 0.6 dimension (b) 0.8 dimension.
Using Fractal-Fractional operator, the value of <i>R</i> ( <i>t</i> ) at multiple dimensions with different fractional values (a) 0.6 dimension (b) 0.8 dimension. Open
Using Fractal-Fractional operator, the value of R ( t ) at multiple dimensions with different fractional values (a) 0.6 dimension (b) 0.8 dimension.
View article: Using Fractal-Fractional operator, the value of <i>E</i> ( <i>t</i> ) at multiple dimensions with different fractional values (a) 0.6 dimension (b) 0.8 dimension.
Using Fractal-Fractional operator, the value of <i>E</i> ( <i>t</i> ) at multiple dimensions with different fractional values (a) 0.6 dimension (b) 0.8 dimension. Open
Using Fractal-Fractional operator, the value of E ( t ) at multiple dimensions with different fractional values (a) 0.6 dimension (b) 0.8 dimension.
View article: Using Fractal-Fractional operator, the value of <i>I</i> ( <i>t</i> ) at multiple dimensions with different fractional values (a) 0.6 dimension (b) 0.8 dimension.
Using Fractal-Fractional operator, the value of <i>I</i> ( <i>t</i> ) at multiple dimensions with different fractional values (a) 0.6 dimension (b) 0.8 dimension. Open
Using Fractal-Fractional operator, the value of I ( t ) at multiple dimensions with different fractional values (a) 0.6 dimension (b) 0.8 dimension.
View article: Using Fractal-Fractional operator, the value of <i>B</i> ( <i>t</i> ) at multiple dimensions with different fractional values (a) 0.6 dimension (b) 0.8 dimension
Using Fractal-Fractional operator, the value of <i>B</i> ( <i>t</i> ) at multiple dimensions with different fractional values (a) 0.6 dimension (b) 0.8 dimension Open
Using Fractal-Fractional operator, the value of B ( t ) at multiple dimensions with different fractional values (a) 0.6 dimension (b) 0.8 dimension