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An Adaptive Method for Likelihood Optimization in Linear Mixed Models Under Constrained Search Spaces
Un método adaptativo para optimizar la función de verosimilitud en modelos lineales mixtos bajo espacios de búsqueda restringidos
DOI:
https://doi.org/10.15446/rce.v46n2.104019Keywords:
Hybrid genetic algorithm, Linear mixed model, Optimization, Positive de nite matrices (en)Algoritmo genético híbrido, Modelo lineal mixto, Optimización, Matrices de nidas positivas (es)
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Linear mixed effects models are highly flexible in handling correlated data by considering covariance matrices that explain variation patterns between and within clusters. For these covariance matrices, there exist a wide list of possible structures proposed by researchers in multiple scientific areas. Maximum likelihood is the most common estimation method in linear mixed models and it depends on the structured covariance matrices for random effects and errors. Classical methods used to optimize the likelihood function, such as Newton-Raphson or Fisher's scoring, require analytical procedures to obtain parametrical restrictions to guarantee positive definiteness for the structured matrices and it is not, in general, an easy task. To avoid dealing with complex restrictions, we propose an adaptive method that incorporates the so-called Hybrid Genetic Algorithms with a penalization technique based on minimum eigenvalues to guarantee positive definiteness in an evolutionary process which discards non-viable cases. The proposed method is evaluated through simulations and its performance is compared with that of Newton-Raphson algorithm implemented in SAS® PROC MIXED V9.4.
Los modelos lineales mixtos son muy flexibles cuando se trabaja con datos correlacionados ya que estos consideran matrices de covarianza que explican los patrones de variación entre individuos y dentro de sus observaciones. Para estas matrices de covarianza existe una amplia lista de posibles estructuras propuestas por investigadores en múltiples áreas científicas. El método de máxima verosimilitud es el más común para la estimación de los parámetros en modelos lineales mixtos y depende de las matrices de covarianza estructuradas para efectos aleatorios y errores. Los métodos clásicos utilizados para optimizar la función de verosimilitud, como Newton-Raphson o Fisher's scoring, requieren desarrollos analíticos para obtener restricciones sobre los parámetros que garanticen matrices estructuradas y definidas positivas, y en general, esto no es una tarea fácil. Para evitar lidiar con restricciones complejas, proponemos un método adaptativo que incorpora los llamados Algoritmos Genéticos Híbridos con una técnica de penalización basada en valores propios mínimos con el _n de garantizar matrices positivas definidas en un proceso evolutivo que descarta casos no viables. El método propuesto se evalúa a través de simulaciones y se compara su desempeño con el algoritmo de Newton-Raphson implementado en SAS® PROC MIXED V9.4.
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Revista Colombiana de Estadística - Applied Statistics 46 (2023) 1-23 An Adaptive Method for Likelihood Optimizar in LMM Under Restricted Search Spaces19
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