Valid hypothesis testing in face of spatially dependent data using multi-layer perceptrons and sub-sampling techniques

DOI10.1016/j.csda.2006.01.010zbMath1161.62411OpenAlexW2074612074MaRDI QIDQ1019895

Janette F. Walde

Publication date: 29 May 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2006.01.010

zbMATH Keywords

statistical inferencespatial dependencesub-samplingmulti-layer perceptronnon-linear regression

Mathematics Subject Classification ID

General nonlinear regression (62J02) Neural nets and related approaches to inference from stochastic processes (62M45)

Cites Work

• Comparison of the performance of multi-layer perceptron and linear regression for epidemiological data
• Statistical aspects of multilayer perceptrons under data limitations
• Multivariate distributions with correlation matrices for nonlinear repeated measurements
• Efficiency evaluation of MEV spatial sampling strategies: a scenario analysis
• Misspecified models with dependent observations
• Estimating the dimension of a model
• Subsampling for heteroskedastic time series
• Multilayer feedforward networks are universal approximators
• Large sample confidence regions based on subsamples under minimal assumptions
• Nonlinear Regression with Dependent Observations
• Nonlinear Regression on Cross-Section Data
• Testing for Heteroscedasticity in Nonlinear Regression Models
• Asymptotic Properties of Maximum Likelihood Estimators for the Independent Not Identically Distributed Case
• A new look at the statistical model identification
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item