Pages that link to "Item:Q1746561"
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The following pages link to Convex and non-convex regularization methods for spatial point processes intensity estimation (Q1746561):
Displaying 14 items.
- Variable selection for spatial Poisson point processes via a regularization method (Q1731184) (← links)
- Point process estimation with Mirror Prox algorithms (Q2019904) (← links)
- Sparse spatially clustered coefficient model via adaptive regularization (Q2084064) (← links)
- Regularized estimation for highly multivariate log Gaussian Cox processes (Q2302515) (← links)
- Regularized Estimating Equations for Model Selection of Clustered Spatial Point Processes (Q3195195) (← links)
- Intensity Estimation for Spatial Point Processes Observed with Noise (Q3608271) (← links)
- Variable selection for inhomogeneous spatial point process models (Q5256382) (← links)
- What is the effective sample size of a spatial point process? (Q6051621) (← links)
- Inference for low‐ and high‐dimensional inhomogeneous Gibbs point processes (Q6073438) (← links)
- Information criteria for inhomogeneous spatial point processes (Q6075103) (← links)
- Adaptive Lasso and Dantzig selector for spatial point processes intensity estimation (Q6103217) (← links)
- Data-driven chimney fire risk prediction using machine learning and point process tools (Q6138624) (← links)
- kppmenet: combining the kppm and elastic net regularization for inhomogeneous Cox point process with correlated covariates (Q6571988) (← links)
- Regularised semi-parametric composite likelihood intensity modelling of a Swedish spatial ambulance call point pattern (Q6655992) (← links)
