Identifying Clusters in Spatial Data Via Sequential Importance Sampling

DOI10.1007/978-3-319-99648-6_10zbMath1420.62275OpenAlexW2889662987MaRDI QIDQ5234029

Nishanthi Raveendran, Georgy Sofronov

Publication date: 9 September 2019

Published in: Recent Advances in Computational Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-99648-6_10

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (1)

A Sequential Importance Sampling Algorithm for Counting Linear Extensions

Uses Software

• changepoint
• wbs

Cites Work

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• Wild binary segmentation for multiple change-point detection
• Parametric Statistical Change Point Analysis
• Change Curve Estimation via Wavelets
• A statistical approach to identifying closed object boundaries in images
• Bayesian Detection and Modeling of Spatial Disease Clustering
• Applications of Binary Segmentation to the Estimation of Quantal Response Curves and Spatial Intensity
• Statistics for Spatial Data
• Circular binary segmentation for the analysis of array-based DNA copy number data
• Simulation and the Monte Carlo Method
• Statistical methods for DNA sequence segmentation

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