Building a Theoretical Foundation for the Spline‐Mixed Model Connection

ABSTRACT In fitting a non‐parametric regression model, the spline‐mixed model connection is well known, which associates fitting of a penalised spline model with that of a linear mixed model. We establish a theoretical framework for justifying the spline‐mixed model connection, and study the asymptotic behaviour of the empirical best linear unbiased predictor of the regression mean and associated model parameters.

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Publication Details

Journal
Australian & New Zealand Journal of Statistics
Published
2026-04-15
DOI
https://doi.org/10.1111/anzs.70050
Primary Topic
Statistical Methods and Inference
Type
article
Field-Weighted Citation Impact
0.00

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article

Building a Theoretical Foundation for the Spline‐Mixed Model Connection

Jiming Jiang
Australian & New Zealand Journal of Statistics
Statistical Methods and Inference
article

Building a Theoretical Foundation for the Spline‐Mixed Model Connection

Jiming Jiang
article en

Abstract

ABSTRACT In fitting a non‐parametric regression model, the spline‐mixed model connection is well known, which associates fitting of a penalised spline model with that of a linear mixed model. We establish a theoretical framework for justifying the spline‐mixed model connection, and study the asymptotic behaviour of the empirical best linear unbiased predictor of the regression mean and associated model parameters.

Australian & New Zealand Journal of StatisticsVol. 68(2)
University of California, Davis (US)
National Science Foundation
Reduced inequalities
Openalex Percentile: Top 12%
Statistical Methods and Inference
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