Selection Criterion of Working Correlation Structure for Spatially Correlated Data

References

• Albert, P. S., and McShane, L. M. (1995), “A Generalized Estimating Equations Approach for Spatially Correlated Binary Data: Applications to the Analysis of Neuroimaging Data,” Biometrics, 51, 627–638.
   PubMed Web of Science ®Google Scholar
• Belsley, D. A., Kuh, E., and Welsch, R. E. (2005), Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, New York: Wiley.
   Google Scholar
• Bevilacqua, M., Faouzi, T., Furrer, R., and Porcu, E. (2019), “Estimation and Prediction using Generalized Wendland Covariance Functions under Fixed Domain Asymptotics,” The Annals of Statistics, 47, 828–856.
   Web of Science ®Google Scholar
• Bonat, W. H. (2017), “Modelling Mixed Types of Outcomes in Additive Genetic Models,” The International Journal of Biostatistics, 13, 1–16.
   Google Scholar
• Cario, M. C., and Nelson, B. L. (1997), “Modeling and Generating Random Vectors with Arbitrary Marginal Distributions and Correlation Matrix,” Technical Report, Citeseer.
   Google Scholar
• Chapra, S. C., and Canale, R. P. (2016), Métodos Numéricos para Engenharia-7a Edição. Sao Paulo: McGraw Hill Brasil.
   Google Scholar
• Gneiting, T. (2002), “Compactly Supported Correlation Functions,” Journal of Multivariate Analysis, 83, 493–508.
   Web of Science ®Google Scholar
• Gosho, M., Hamada, C., and Yoshimura, I. (2011), “Criterion for the Selection of a Working Correlation Structure in the Generalized Estimating Equation Approach for Longitudinal Balanced Data,” Communications in Statistics-Theory and Methods, 40, 3839–3856.
   Web of Science ®Google Scholar
• Hin, L.-Y., Carey, V. J., and Wang, Y.-G. (2007), “Criteria for Working–Correlation–Structure Selection in GEE: Assessment via Simulation,” The American Statistician, 61, 360–364.
   Web of Science ®Google Scholar
• Hin, L.-Y., and Wang, Y.-G. (2009), “Working-Correlation-Structure Identification in Generalized Estimating Equations,” Statistics in Medicine, 28, 642–658.
   PubMed Web of Science ®Google Scholar
• Jaman, A., Latif, M. A., Bari, W., and Wahed, A. S. (2016), “A Determinant-based Criterion for Working Correlation Structure Selection in Generalized Estimating Equations,” Statistics in Medicine, 35, 1819–1833. DOI: 10.1002/sim.6821.
   PubMed Web of Science ®Google Scholar
• Liang, K.-Y., and Zeger, S. L. (1986), “Longitudinal Data Analysis using Generalized Linear Models,” Biometrika, 73, 13–22.
   Web of Science ®Google Scholar
• Nava, D., De Bastiani, F., Uribe-Opazo, M., Nicolis, O., and Galea, M. (2017), “Local Influence for Spatially Correlated Binomial Data: An Application to the Spodoptera Frugiperda Infestation in Corn,” Journal of Agricultural, Biological and Environmental Statistics, 22, 540–561.
   Web of Science ®Google Scholar
• Niaki, S. T. A., and Abbasi, B. (2008), “Generating Correlation Matrices for Normal Random Vectors in Norta Algorithm Using Artificial Neural Networks,” Journal of Uncertain Systems, 2, 192–201.
   Google Scholar
• Pan, W. (2001), “Akaike’s Information Criterion in Generalized Estimating Equations,” Biometrics, 57, 120–125.
   PubMed Web of Science ®Google Scholar
• Prentice, R. L. (1988), “Correlated Binary Regression with Covariates Specific to each Binary Observation,” Biometrics, 44, 1033–1048.
   PubMed Web of Science ®Google Scholar
• R Core Team (2020), R: A Language and Environment for Statistical Computing, Vienna, Austria: R Foundation for Statistical Computing.
   Google Scholar
• Rotnitzky, A., and Jewell, N. P. (1990), “Hypothesis Testing of Regression Parameters in Semiparametric Generalized Linear Models for Cluster Correlated Data,” Biometrika, 77, 485–497.
   Web of Science ®Google Scholar
• Silva, J. A. D., and Cirillo, M. A. (2018), “Selection Criterion of Work Matrix as a Function of Limiting Estimates of the Covariance Matrix of Correlated Data in GEE,” Biometrical Journal, 60, 979–990. DOI: 10.1002/bimj.201800035.
   PubMed Web of Science ®Google Scholar
• Sutradhar, B. C., and Das, K. (1999), “On the Efficiency of Regression Estimators in Generalised Linear Models for Longitudinal Data,” Biometrika, 86, 459–465.
   Web of Science ®Google Scholar
• Sutradhar, B. C., and Das, K. (2000), “On the Accuracy of Efficiency of Estimating Equation Approach,” Biometrics, 56, 622–625.
   PubMed Web of Science ®Google Scholar
• Venables, W. N., and Ripley, B. D. (2002), Modern Applied Statistics with S (4th ed.), New York: Springer.
   Google Scholar
• Venezuela, K., Botter, D. M. A., and Sandoval, M. C. (2007), “Diagnostic Techniques in Generalized Estimating Equations,” Journal of Statistical Computation and Simulation, 77, 879–888.
   Web of Science ®Google Scholar
• Wedderburn, R. W. (1974), “Quasi-Likelihood Functions, Generalized Linear Models, and the Gauss–Newton Method,” Biometrika, 61, 439–447.
   Web of Science ®Google Scholar
• Yahav, I., and Shmueli, G. (2012), “On Generating Multivariate Poisson Data in Management Science Applications,” Applied Stochastic Models in Business and Industry, 28, 91–102.
   Web of Science ®Google Scholar
• Zastavnyi, V. (2006), “On Some Properties of Buhmann Functions,” Ukrainian Mathematical Journal, 58, 1184–1208.
   Google Scholar
• Zeger, S. L., and Liang, K.-Y. (1986), “Longitudinal Data Analysis for Discrete and Continuous Outcomes,” Biometrics, 42, 121–130.
   PubMed Web of Science ®Google Scholar
• Zhao, L. P., Prentice, R. L., and Self, S. G. (1992), “Multivariate Mean Parameter Estimation by Using a Partly Exponential Model,” Journal of the Royal Statistical Society, Series B, 54, 805–811.
   Google Scholar