Efficient Shrinkage for Generalized Linear Mixed Models Under Linear Restrictions

Thomson, T.; Hossain, Shakhawat

doi:10.1007/s13171-017-0122-6

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…One of the choices of this matrix is the identity matrix which will be used in the simulation study. Other choices of this matrix are also available, for example, W = I −1 or a general W which gives a loss Proof Similar proofs can be found in Thomson and Hossain (2018). □ Remark 5.2 Under H 0 ∶ A = h , that is, when = 0 , ̂ R is the best choice and it strongly dominates ̂ F .…”

Section: Theorem 52 If the Conditions Of Theorem 51 Hold Then The mentioning

confidence: 98%

Estimation strategy of multilevel model for ordinal longitudinal data

Hossain

Hiebert

Mandal

2019

Jpn J Stat Data Sci

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This paper considers the shrinkage estimation of multilevel models that are appropriate for ordinal longitudinal data. These models can accommodate multiple random effects and, additionally, allow for a general form of model covariates that are related to the overall level of the responses and changes to the response over time. The likelihood inference for multilevel models is computationally burdensome due to intractable integrals. A maximum marginal likelihood (MML) method with Fisher's scoring procedure is therefore followed to estimate the random and fixed effects parameters. In real life data, researchers may have collected many covariates for the response. Some of these covariates may satisfy certain constraints which can be used to produce a restricted estimate from the unrestricted likelihood function. The unrestricted and restricted MMLs can then be combined optimally to form the pretest and shrinkage estimators. Asymptotic properties of these estimators including biases and risks will be discussed. A simulation study is conducted to assess the performance of the estimators with respect to the unrestricted MML estimator. Finally, the relevance of the proposed estimators will be illustrated with a real data set.

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Section: Theorem 52 If the Conditions Of Theorem 51 Hold Then The mentioning

confidence: 98%

Estimation strategy of multilevel model for ordinal longitudinal data

Hossain

Hiebert

Mandal

2019

Jpn J Stat Data Sci

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“…This algorithm is available in the R programming language, namely glmnet package [9]. Some researchers have discussed variable selection procedures in GLMM using the L1 penalty, including Thomson and Hossain (2018) [10], Groll and Tutz (2014) [2], Schelldorfer et al (2011) [11], Ibrahim et al (2010) [12]. The LASSO GLMM produces stable estimations because penalty L1 can select the important predictor variables used in GLMM [2].…”

Section: Introductionmentioning

confidence: 99%

A Combination of Generalized Linear Mixed Model and LASSO Methods for Estimating Number of Patients Covid 19 in the Intensive Care Units

Dwinata

Notodiputro²,

Sartono³

2021

CAUCHY

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Generalized linear mixed models (GLMM) combined with the L1 penalty (Least Absolute Shrinkage and Selection Operator/LASSO) is called LASSO GLMM. LASSO GLMM reduces overfitting and selects predictor variables in modeling. The aim of this study is to evaluate the model's performance for predicting Covid-19 patients with certain congenital disease that require ICU based on the results of blood tests laboratory and patient’s vital signs. This study used binary response variables, 1 if the patient was admitted to the ICU and 0 if the patient was not admitted to the ICU. The fixed effect predictor variables are the results of blood tests laboratory and patient’s vital signs. The random effect predictor variable is patient's congenital disease. The result showed that the average of accuracy and AUC from LASSO GLMM is more than the average of accuracy and AUC from LASSO GLM by using 5% level of significance. Respiratory rate and Lactate show a significance effect to predict the ICU needs of Covid-19 patients. The random effects patient's congenital disease has significance effect at 5% level of significance. It means that the ICU needs for Covid-19 patients varies among patient's congenital disease. We can conclude that GLMM LASSO with the random effect of patient’s congenital diseases has better modeling performance to predict the ICU needs of Covid-19 patients based on the results of blood tests laboratory and patient’s vital signs. The results of this modeling can quickly detect Covid-19 patients who need the ICU and can help medical staff use ICU resources optimally

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Improved estimation in a multivariate regression with measurement error

Nkurunziza,

(Eric) Li

2024

Journal of Statistical Computation and Simulation

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Efficient Shrinkage for Generalized Linear Mixed Models Under Linear Restrictions

Cited by 6 publications

References 24 publications

Estimation strategy of multilevel model for ordinal longitudinal data

Estimation strategy of multilevel model for ordinal longitudinal data

A Combination of Generalized Linear Mixed Model and LASSO Methods for Estimating Number of Patients Covid 19 in the Intensive Care Units

Improved estimation in a multivariate regression with measurement error

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