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Influence diagnostics in mixed effects logistic regression models

Author

Listed:
  • Alejandra Tapia

    (Universidad Austral de Chile)

  • Victor Leiva

    (Pontificia Universidad Católica de Valparaíso)

  • Maria del Pilar Diaz

    (Universidad Nacional de Córdoba)

  • Viviana Giampaoli

    (Universidade de São Paulo)

Abstract

Correlated binary responses are commonly described by mixed effects logistic regression models. This article derives a diagnostic methodology based on the Q-displacement function to investigate local influence of the responses in the maximum likelihood estimates of the parameters and in the predictive performance of the mixed effects logistic regression model. An appropriate perturbation strategy of the probability of success is established, as a form of assessing the perturbation in the response. The diagnostic methodology is evaluated with Monte Carlo simulations. Illustrations with two real-world data sets (balanced and unbalanced) are conducted to show the potential of the proposed methodology.

Suggested Citation

  • Alejandra Tapia & Victor Leiva & Maria del Pilar Diaz & Viviana Giampaoli, 2019. "Influence diagnostics in mixed effects logistic regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 920-942, September.
    • Handle: RePEc:spr:testjl:v:28:y:2019:i:3:d:10.1007_s11749-018-0613-3
    DOI: 10.1007/s11749-018-0613-3
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    References listed on IDEAS

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    Cited by:

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    2. Yonghui Liu & Guohua Mao & Víctor Leiva & Shuangzhe Liu & Alejandra Tapia, 2020. "Diagnostic Analytics for an Autoregressive Model under the Skew-Normal Distribution," Mathematics, MDPI, vol. 8(5), pages 1-19, May.
    3. Zhonghao Su & Fukang Zhu & Shuangzhe Liu, 2024. "Local influence analysis in the softplus INGARCH model," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 33(3), pages 951-985, September.
    4. Francisco J. A. Cysneiros & Víctor Leiva & Shuangzhe Liu & Carolina Marchant & Paulo Scalco, 2019. "A Cobb–Douglas type model with stochastic restrictions: formulation, local influence diagnostics and data analytics in economics," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(4), pages 1693-1719, July.

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