Composite likelihood methods for histogram-valued random variables

DOI10.1007/s11222-020-09955-5zbMath1452.62337arXiv1908.11548OpenAlexW3034604828MaRDI QIDQ2209717

T. Whitaker, Scott A. Sisson, Boris Beranger

Publication date: 4 November 2020

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1908.11548

zbMATH Keywords

climate modelssymbolic data analysisspatial extremescomposite likelihoodsrandom histograms

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Applications of statistics to environmental and related topics (62P12) Statistics of extreme values; tail inference (62G32) Statistical aspects of big data and data science (62R07) Climate science and climate modeling (86A08)

Related Items (6)

Logistic Regression Models for Aggregated Data ⋮ New models for symbolic data analysis ⋮ Copulas and Histogram-Valued Data ⋮ Frugal Gaussian clustering of huge imbalanced datasets through a bin-marginal approach ⋮ High-dimensional inference using the extremal skew-\(t\) process ⋮ Analysis of dependent data aggregated into intervals

Uses Software

• SpatialExtremes

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Likelihood functions and some maximum likelihood estimators for symbolic data
• Spatial modeling of extreme snow depth
• A spectral representation for max-stable processes
• Models for stationary max-stable random fields
• Principal component analysis for histogram-valued data
• Models for Extremal Dependence Derived from Skew-symmetric Families
• On the likelihood function of Gaussian max-stable processes
• A note on composite likelihood inference and model selection
• Introduction à l'approche symbolique en analyse des données
• Estimating Space and Space-Time Covariance Functions for Large Data Sets: A Weighted Composite Likelihood Approach
• Brief overview of symbolic data and analytic issues
• Linear regression model with histogram‐valued variables
• Constructing likelihood functions for interval‐valued random variables
• Modelling interval data with Normal and Skew-Normal distributions
• Likelihood-Based Inference for Max-Stable Processes
• An Optimum Property of Regular Maximum Likelihood Estimation
• Analysis of symbolic data. Exploratory methods for extracting statistical information from complex data
• Statistical modeling of spatial extremes
• Set-valued and interval-valued stationary time series
• Discriminant analysis of interval data: an assessment of parametric and distance-based approaches

This page was built for publication: Composite likelihood methods for histogram-valued random variables