Pages that link to "Item:Q5256382"
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The following pages link to Variable selection for inhomogeneous spatial point process models (Q5256382):
Displaying 17 items.
- Penalized composite likelihoods for inhomogeneous Gibbs point process models (Q1662861) (← links)
- Variable selection for spatial Poisson point processes via a regularization method (Q1731184) (← links)
- Convex and non-convex regularization methods for spatial point processes intensity estimation (Q1746561) (← links)
- Single-index model for inhomogeneous spatial point processes (Q2977516) (← links)
- Regularized Estimating Equations for Model Selection of Clustered Spatial Point Processes (Q3195195) (← links)
- Variable selection for spatial nonparametric regression (Q5063666) (← links)
- Analyzing order flows in limit order books with ratios of Cox-type intensities (Q5215440) (← links)
- What is the effective sample size of a spatial point process? (Q6051621) (← 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)
- 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)
- Goodness-of-fit test for point processes first-order intensity (Q6561265) (← links)
- kppmenet: combining the kppm and elastic net regularization for inhomogeneous Cox point process with correlated covariates (Q6571988) (← 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)
