Pages that link to "Item:Q1731184"
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The following pages link to Variable selection for spatial Poisson point processes via a regularization method (Q1731184):
Displaying 11 items.
- Efficient modelling of presence-only species data via local background sampling (Q782727) (← links)
- Penalized composite likelihoods for inhomogeneous Gibbs point process models (Q1662861) (← links)
- Convex and non-convex regularization methods for spatial point processes intensity estimation (Q1746561) (← links)
- Spatial logistic regression and change-of-support in Poisson point processes (Q1952095) (← links)
- Regularized Estimating Equations for Model Selection of Clustered Spatial Point Processes (Q3195195) (← links)
- Variable selection for inhomogeneous spatial point process models (Q5256382) (← links)
- Variable selection using penalised likelihoods for point patterns on a linear network (Q6051638) (← links)
- Inference for low‐ and high‐dimensional inhomogeneous Gibbs point processes (Q6073438) (← links)
- Data-driven chimney fire risk prediction using machine learning and point process tools (Q6138624) (← links)
- Heterogeneity pursuit for spatial point pattern with application to tree locations: a Bayesian semiparametric recourse (Q6626411) (← links)
- Regularised semi-parametric composite likelihood intensity modelling of a Swedish spatial ambulance call point pattern (Q6655992) (← links)
