Papers › Inference for generalized additive mixed models via penalized marginal likelihood
Inference for generalized additive mixed models via penalized marginal likelihood
Alex Stringer
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The Laplace approximation is sometimes not sufficiently accurate for smoothing parameter estimation in generalized additive mixed models. A novel estimation strategy is proposed that solves this problem and leads to estimates exhibiting the correct statistical properties.
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