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- W4320147292 abstract "The accommodation of correlations associated with baseline relationships is a recurrent theme in this book. For the most part, this is done using Gaussian models with several additive variance components. The preferred method for variance-component estimation is maximum likelihood based on residuals. In practice, all linear-model specifications are made in the observation space $${mathbb R}^n$$ . This chapter develops the theory needed for maximum-likelihood based on residuals. That includes both point estimation of variance components, and likelihood-ratio tests either for components in the mean model or for components in the variance model. There are very few examples in applied work where measure-theoretic issues intrude into computations, but likelihood-ratio testing in this setting is one such example. The text emphasizes the role of the kernel subspace in connection with residuals, and explains how to compute a likelihood ratio keeping the kernel fixed." @default.
- W4320147292 created "2023-02-13" @default.
- W4320147292 creator A5020415617 @default.
- W4320147292 date "2022-01-01" @default.
- W4320147292 modified "2023-09-27" @default.
- W4320147292 title "Residual Likelihood" @default.
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- W4320147292 doi "https://doi.org/10.1007/978-3-031-14275-8_18" @default.
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