On the estimation of variance parameters in non-standard generalised linear mixed models: application to penalised smoothing

We present a novel method for the estimation of variance parameters in generalised linear mixed models. The method has its roots in Harville (J Am Stat Assoc 72(358):320–338, 1977 )’s work, but it is able to deal with models that have a precision matrix for the random effect vector that is linear in...

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Bibliographic Details
Published in:Statistics and computing 2019-05, Vol.29 (3), p.483-500
Main Authors: Rodríguez-Álvarez, María Xosé, Durban, Maria, Lee, Dae-Jin, Eilers, Paul H. C.
Format: Article
Language:English
Subjects:
Artificial Intelligence
Mathematical models
Mathematics and Statistics
Matrix algebra
Matrix methods
Parameter estimation
Probability and Statistics in Computer Science
Regression coefficients
Smoothness
Splines
Statistical Theory and Methods
Statistics
Statistics and Computing/Statistics Programs
Variance
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Summary:We present a novel method for the estimation of variance parameters in generalised linear mixed models. The method has its roots in Harville (J Am Stat Assoc 72(358):320–338, 1977 )’s work, but it is able to deal with models that have a precision matrix for the random effect vector that is linear in the inverse of the variance parameters (i.e., the precision parameters). We call the method SOP (separation of overlapping precision matrices). SOP is based on applying the method of successive approximations to easy-to-compute estimate updates of the variance parameters. These estimate updates have an appealing form: they are the ratio of a (weighted) sum of squares to a quantity related to effective degrees of freedom. We provide the sufficient and necessary conditions for these estimates to be strictly positive. An important application field of SOP is penalised regression estimation of models where multiple quadratic penalties act on the same regression coefficients. We discuss in detail two of those models: penalised splines for locally adaptive smoothness and for hierarchical curve data. Several data examples in these settings are presented.
ISSN:0960-3174
1573-1375
DOI:10.1007/s11222-018-9818-2