Asymptotic distributions of M-estimators in a spatial regression model under some fixed and stochastic spatial sampling designs

DOI10.1007/BF02530543zbMath1056.62031OpenAlexW2070722611MaRDI QIDQ1768124

Kanchan Mukherjee, Soumendra Nath Lahiri

Publication date: 14 March 2005

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02530543

zbMATH Keywords

random fieldcentral limit theoremstrong mixinglong range dependenceinfill samplingstochastic designspatial sampling designincreasing-domain asymptotics

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Inference from spatial processes (62M30) Asymptotic distribution theory in statistics (62E20) Linear regression; mixed models (62J05)

Related Items (11)

Asymptotic analysis of the role of spatial sampling for covariance parameter estimation of Gaussian processes ⋮ On the non-standard distribution of empirical likelihood estimators with spatial data ⋮ Bootstrapping the empirical distribution function of a spatial process ⋮ A general frequency domain method for assessing spatial covariance structures ⋮ Resampling methods for spatial regression models under a class of stochastic designs ⋮ Statistical analysis of a spatio-temporal model with location-dependent parameters and a test for spatial stationarity ⋮ Uncertainty quantification in robust inference for irregularly spaced spatial data using block bootstrap ⋮ Asymptotic properties of rank estimators in a simple spatial linear regression model under spatial sampling designs ⋮ The Dependent Random Weighting ⋮ A frequency domain empirical likelihood method for irregularly spaced spatial data ⋮ Asymptotic Analysis of Maximum Likelihood Estimation of Covariance Parameters for Gaussian Processes: An Introduction with Proofs

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