| scholarly article | Q13442814 |
| P356 | DOI | 10.1007/978-3-642-20192-9 |
| P894 | zbMATH Open document ID | 1273.62015 |
| P50 | author | Peter Bühlmann | Q30544174 |
| Sara van de Geer | Q21055946 | ||
| P921 | main subject | statistics | Q12483 |
| high-dimensional data | Q105702318 | ||
| P6104 | maintained by WikiProject | WikiProject Mathematics | Q8487137 |
| P577 | publication date | 2011-01-01 | |
| P1433 | published in | Springer Series in Statistics | Q22670902 |
| P1476 | title | Statistics for High-Dimensional Data |
| Q30868549 | 'Big data' approaches to trauma outcome prediction and autonomous resuscitation |
| Q58143326 | A Note on High-Dimensional Linear Regression With Interactions |
| Q91526792 | A Partial Correlation Screening Approach for Controlling the False Positive Rate in Sparse Gaussian Graphical Models |
| Q38833354 | A Penalized Likelihood Method for Structural Equation Modeling |
| Q35119273 | A systematic evaluation of high-dimensional, ensemble-based regression for exploring large model spaces in microbiome analyses |
| Q38987763 | Adjusted adaptive Lasso for covariate model-building in nonlinear mixed-effect pharmacokinetic models. |
| Q52651178 | An ANOVA approach for statistical comparisons of brain networks. |
| Q35915687 | An Adaptive Ridge Procedure for L0 Regularization |
| Q30361306 | An individualized predictor of health and disease using paired reference and target samples. |
| Q103585111 | Analysis of population functional connectivity data via multilayer network embeddings |
| Q35213066 | Analyzing genome-wide association studies with an FDR controlling modification of the Bayesian Information Criterion |
| Q54966148 | Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables. |
| Q104064451 | Characterizing the type 2 diabetes mellitus epidemic in Jordan up to 2050 |
| Q45361773 | Classical Statistics and Statistical Learning in Imaging Neuroscience |
| Q30886894 | Classification of longitudinal data through a semiparametric mixed-effects model based on lasso-type estimators |
| Q41718605 | Combining biomarkers linearly and nonlinearly for classification using the area under the ROC curve |
| Q30872383 | Comparison of classification methods that combine clinical data and high-dimensional mass spectrometry data |
| Q38542222 | Component-wise gradient boosting and false discovery control in survival analysis with high-dimensional covariates |
| Q41671843 | Construction of environmental risk score beyond standard linear models using machine learning methods: application to metal mixtures, oxidative stress and cardiovascular disease in NHANES. |
| Q57427185 | Contemporaneous causal orderings of US corn cash prices through directed acyclic graphs |
| Q37960775 | Controlling false positive selections in high-dimensional regression and causal inference |
| Q97595701 | Early detection of type 2 diabetes mellitus using machine learning-based prediction models |
| Q38771327 | Empirical extensions of the lasso penalty to reduce the false discovery rate in high-dimensional Cox regression models. |
| Q51020823 | Exploring the areas of applicability of whole-genome prediction methods for Asian rice (Oryza sativa L.). |
| Q36900716 | Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification |
| Q35040123 | Fine-mapping additive and dominant SNP effects using group-LASSO and fractional resample model averaging |
| Q30976798 | From big data analysis to personalized medicine for all: challenges and opportunities |
| Q37659372 | Fused Lasso Additive Model |
| Q39110019 | Graph reconstruction using covariance-based methods |
| Q58187228 | Guns and Suicides |
| Q35735862 | Homogeneity Pursuit |
| Q41811230 | Integrative Analysis of Cancer Diagnosis Studies with Composite Penalization |
| Q35164722 | Integrative analysis of high-throughput cancer studies with contrasted penalization |
| Q27320776 | Lasso adjustments of treatment effect estimates in randomized experiments |
| Q98623225 | Machine learning for effectively avoiding overfitting is a crucial strategy for the genetic prediction of polygenic psychiatric phenotypes |
| Q39669174 | Mixed scale joint graphical lasso |
| Q45265273 | Multivariate gene-set testing based on graphical models |
| Q30715251 | Overfitting, generalization, and MSE in class probability estimation with high-dimensional data |
| Q38012608 | Overview of LASSO-related penalized regression methods for quantitative trait mapping and genomic selection |
| Q30653367 | PUMA: a unified framework for penalized multiple regression analysis of GWAS data |
| Q39819043 | Penalized estimation for competing risks regression with applications to high-dimensional covariates |
| Q31030304 | Rare variants analysis using penalization methods for whole genome sequence data |
| Q39664671 | Regularity Properties for Sparse Regression |
| Q47037073 | Regularized learning schemes in feature Banach spaces |
| Q27902330 | Reliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validation |
| Q31013008 | Review and evaluation of penalised regression methods for risk prediction in low-dimensional data with few events. |
| Q30858189 | Robust selection of cancer survival signatures from high-throughput genomic data using two-fold subsampling |
| Q34363908 | Sample size planning for survival prediction with focus on high-dimensional data |
| Q30831307 | Scale-Invariant Sparse PCA on High Dimensional Meta-elliptical Data |
| Q40542121 | Score test variable screening |
| Q47108124 | ShinyGPAS: interactive genomic prediction accuracy simulator based on deterministic formulas |
| Q31172638 | Sparse Proteomics Analysis - a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data |
| Q28651378 | Spatially Varying Coefficient Model for Neuroimaging Data with Jump Discontinuities |
| Q38963988 | Stagewise generalized estimating equations with grouped variables |
| Q41497984 | Structure detection of semiparametric structural equation models with Bayesian adaptive group lasso. |
| Q30978345 | The center for causal discovery of biomedical knowledge from big data |
| Q37238486 | The lasso for high dimensional regression with a possible change point. |
| Q57239148 | Tuning Parameter Selection for the Adaptive Lasso Using ERIC |
| Q39130084 | Understanding the mTOR signaling pathway via mathematical modeling |
| Q36454190 | Variable Selection for Support Vector Machines in Moderately High Dimensions |
| Q39923740 | Variable selection in discrete survival models including heterogeneity |
| Q59054411 | Weaker Regularity Conditions and Sparse Recovery in High-Dimensional Regression |
| Q113307854 | hdme: High-Dimensional Regression with Measurement Error |
| Q113307042 | serp: An R package for smoothing in ordinal regression |
Search more.