Our Seminar Lists


Multisource data Inference on multi-source data

Intro

  1. A review on machine learning principles for multi-view biological data integration

  2. Statistical methods in integrative genomics

  3. General overview on the merits of multimodal neuroimaging data fusion

  4. Multiple factor analysis: principal component analysis for multitable and multiblock data sets

Multi source

  1. Joint and individual variation explained (JIVE) for integrated analysis of multiple data types

  2. Structural learning and integrative decomposition of multi-view data

  3. A general framework for association analysis of heterogeneous data

  4. Integrative Multi-View Reduced-Rank Regression: Bridging Group-Sparse and Low-Rank Models

  5. Supervised multiway factorization

  6. A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data

  7. Integrating multi-source block-wise missing data in model selection

  8. Joint and individual analysis of breast cancer histologic images and genomic covariates

  9. D-CCA: A Decomposition-Based Canonical Correlation Analysis for High-Dimensional Datasets

  10. Integrative Factorization of Bidimensionally Linked Matrices

  11. Incorporating biological information in sparse principal component analysis with application to genomic data

  12. OnPLS—A Novel Multiblock Method for the Modelling of Predictive and Orthogonal Variation

Covariance esimation and PCA

  1. Sparsistency and rates of convergence in large covariance matrix estimation
  2. Large covariance estimation by thresholding principal orthogonal complements
  3. Asymptotics of empirical eigen-structure for ultra-high dimensional spiked covariance model
  4. Adaptive thresholding for sparse covariance matrix estimation
  5. Sparse PCA: Optimal rates and adaptive estimation
  6. Sparse principal component analysis and iterative thresholding
  7. Minimax rates of estimation for sparse PCA in high dimensions
  8. Minimax bounds for sparse PCA with noisy high-dimensional data

Factor models

  1. Factor models and variable selection in high-dimensional regression analysis
  2. Statistical analysis of factor models of high-dimension
  3. Factor-adjusted regularized model selection
  4. Imputed factor regression for high-dimensional block-wise missing data

Post selection inference

  1. Confidence intervals and Hypothesis Testing for High-Dimensional Regression
  2. A general theory of hypothesis tests and confidence regions for sparse high dimensional models
  3. Linear hypothesis testing for high dimensional GLM