Semiparametric Mean–Covariance Regression Analysis for Longitudinal Data

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Covariance Estimation: The GLM and Regularization Perspectives (2011, background): “How ever, the success of Bayesian computation and Markov Chain Monte Carlo (MCMC) in the late 1980s did open up the possibility of using more flexible and elabo rate nonconjugate priors for covariance matrices; see Leonard and Hsu (1992), Yang and Berger (1994), Daniels and Kass (1999) and Hoff (200”
Generalized Gaussian Process Regression Model for Non-Gaussian Functional Data (2014, methodology): “Some other covariance kernels such as powered exponential and Matérn covariance functions can also be used; see more discussion on the choice of covariance function in Rasmussen and Williams (2006) and Shi and Choi (2011). In the model given by (4) the response variable zm(t) depends on xm(t) at the”
A moving average Cholesky factor model in covariance modelling for longitudinal data (2012, methodology): “The mean trajectory is consistent with 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 that in Zeger & Diggle (1994) and Leng et al. (2010).”