Semiparametric method and theory for continuously indexed spatio-temporal processes

DOI10.1016/j.jmva.2021.104735zbMath1465.62155OpenAlexW3131934604MaRDI QIDQ2022565

Jun Zhu, Haonan Wang, Tingjin Chu, Jialuo Liu

Publication date: 29 April 2021

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2021.104735

zbMATH Keywords

spatial statisticsrandom fieldsspatio-temporal statisticsbimodal kernel

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Random fields; image analysis (62M40) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Uses Software

• SemiPar

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Penalized maximum likelihood estimation and variable selection in geostatistics
• Profile likelihood inferences on semiparametric varying-coefficient partially linear models
• Statistical inference in single-index and partially nonlinear models
• Interpolation of spatial data. Some theory for kriging
• Maximum likelihood estimation of parameters under a spatial sampling scheme
• Properties of nonparametric estimators of autocovariance for stationary random fields
• Resampling methods for dependent data
• Nonparametric regression with correlated errors.
• Estimation in a semiparametric partially linear errors-in-variables model
• Optimal global rates of convergence for nonparametric regression
• A semiparametric spatial dynamic model
• The asymptotic distribution of REML estimators
• Fixed-domain asymptotics for a subclass of Matérn-type Gaussian random fields
• Nonseparable dynamic nearest neighbor Gaussian process models for large spatio-temporal data with an application to particulate matter analysis
• Estimation in semiparametric spatial regression
• Static and roving sensor data fusion for spatio-temporal hazard mapping with application to occupational exposure assessment
• Dynamic Models for Spatiotemporal Data
• Adaptively Varying-Coefficient Spatiotemporal Models
• A Spectral Domain Test for Stationarity of Spatio‐Temporal Data
• A Class of Convolution-Based Models for Spatio-Temporal Processes with Non-Separable Covariance Structure
• Maximum likelihood estimation of models for residual covariance in spatial regression
• Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter
• Semiparametric Regression
• Nonseparable, Stationary Covariance Functions for Space–Time Data
• Classes of Nonseparable, Spatio-Temporal Stationary Covariance Functions
• Nonparametric Estimation of Probability Density Functions for Irregularly Observed Spatial Data
• Semiparametric Modeling with Nonseparable and Nonstationary Spatio-Temporal Covariance Functions and Its Inference
• Variable Selection for Partially Linear Models With Measurement Errors
• A Test for Stationarity for Irregularly Spaced Spatial Data
• Nonparametric estimation of a periodic sequence in the presence of a smooth trend
• Inconsistent Estimation and Asymptotically Equal Interpolations in Model-Based Geostatistics
• Smoothing parameter selection methods for nonparametric regression with spatially correlated errors
• Space–Time Covariance Functions
• Modeling Temporally Evolving and Spatially Globally Dependent Data

This page was built for publication: Semiparametric method and theory for continuously indexed spatio-temporal processes