On latent process models in multi-dimensional space

DOI10.1016/j.spl.2012.03.022zbMath1398.62258OpenAlexW2044372398MaRDI QIDQ449379

Zuofeng Shang

Publication date: 30 August 2012

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2012.03.022

zbMATH Keywords

asymptotic normalityM-estimationmixing conditionsspatial linear quantile regressionspatial Poisson model

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05)

Cites Work

• On the central limit theorem for stationary mixing random fields
• Mixing: Properties and examples
• Asymptotics for Lasso-type estimators.
• Limit theorems for regression models of time series of counts
• Estimating equations for spatially correlated data in multi-dimensional space
• A negative binomial model for time series of counts
• A regression model for time series of counts
• A state space model for multivariate longitudinal count data
• A Latent Process Regression Model for Spatially Correlated Count Data
• A Regression Method for Spatial Disease Rates: An Estimating Function Approach
• On autocorrelation in a Poisson regression model
• Bayesian Detection and Modeling of Spatial Disease Clustering
• Efficiency of Generalized Estimating Equations for Binary Responses
• Time series count data regression
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