Pages that link to "Item:Q1020973"
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The following pages link to Gaussian model selection with an unknown variance (Q1020973):
Displaying 29 items.
- A Kernel Multiple Change-point Algorithm via Model Selection (Q80474) (← links)
- Estimator selection in the Gaussian setting (Q141397) (← links)
- Segmentation of the mean of heteroscedastic data via cross-validation (Q637994) (← links)
- Estimator selection with respect to Hellinger-type risks (Q644788) (← links)
- Exact posterior distributions and model selection criteria for multiple change-point detection problems (Q693323) (← links)
- Finite mixture regression: a sparse variable selection by model selection for clustering (Q902208) (← links)
- High-dimensional Gaussian model selection on a Gaussian design (Q985331) (← links)
- Mixing least-squares estimators when the variance is unknown (Q1002537) (← links)
- Gaussian model selection with an unknown variance (Q1020973) (← links)
- Model selection for Gaussian regression with random design (Q1763101) (← links)
- Minimax risks for sparse regressions: ultra-high dimensional phenomenons (Q1950804) (← links)
- Optimal upper and lower bounds for the true and empirical excess risks in heteroscedastic least-squares regression (Q1950830) (← links)
- Spatial adaptation in heteroscedastic regression: propagation approach (Q1950843) (← links)
- Optimal model selection in heteroscedastic regression using piecewise polynomial functions (Q1951154) (← links)
- Estimation of Gaussian graphs by model selection (Q1951762) (← links)
- Simultaneous estimation of the mean and the variance in heteroscedastic Gaussian regression (Q1951804) (← links)
- Adaptive estimation of covariance matrices via Cholesky decomposition (Q1952094) (← links)
- Sparsity considerations for dependent variables (Q1952207) (← links)
- How can we identify the sparsity structure pattern of high-dimensional data: an elementary statistical analysis to interpretable machine learning (Q2170515) (← links)
- Prediction error bounds for linear regression with the TREX (Q2273161) (← links)
- Joint rank and variable selection for parsimonious estimation in a high-dimensional finite mixture regression model (Q2397123) (← links)
- Multivariate intensity estimation via hyperbolic wavelet selection (Q2404408) (← links)
- Estimating composite functions by model selection (Q2438264) (← links)
- Exact Posterior Distributions over the Segmentation Space and Model Selection for Multiple Change-Point Detection Problems (Q3298515) (← links)
- Penalized likelihood and multiple testing (Q4626707) (← links)
- Block-Diagonal Covariance Selection for High-Dimensional Gaussian Graphical Models (Q4690959) (← links)
- Gaussian model selection (Q5945247) (← links)
- High-dimensional regression with unknown variance (Q5965306) (← links)
- Trade-off between predictive performance and FDR control for high-dimensional Gaussian model selection (Q6595785) (← links)
