On modelling mean‐covariance structures in longitudinal studies

By Jianxin Pan, G. MacKenzie. 2003

175 citations recorded by the source corpus.

Subfields: Genetics, Plant Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b5cc7d1ed9b009123841ef257dcbb4eb63f4d35b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Youngjo Lee
2. John A. Nelder
3. Mohsen Pourahmadi
4. Chenlei Leng
5. Weiping Zhang
6. Wagner Hugo Bonat
7. Bent Jørgensen
8. Jianxin Pan

In Plant Science

1. Youngjo Lee
2. John A. Nelder
3. Mohsen Pourahmadi
4. Wagner Hugo Bonat
5. Bent Jørgensen
6. Jianxin Pan
7. G. MacKenzie
8. Chenlei Leng

In Statistics and Probability

1. Youngjo Lee
2. John A. Nelder
3. Wagner Hugo Bonat
4. Chenlei Leng
5. Bent Jørgensen
6. Weiping Zhang
7. Mohsen Pourahmadi
8. Annie Qu

Citations in the corpus

• Double hierarchical generalized linear models (2006, methodology): “In forming L.ρ/ the use of regression models was studied by Pourahmadi (2000) and Pan and MacKenzie (2003) .”
• Generalized Estimating Equations in Longitudinal Data Analysis: A Review and Recent Developments (2014, unknown): “also discussed the covariate selection for longitudinal data analysis [46]; also, a variance function selection was mentioned by Pan and Mackenzie [30] as well as Wang and Lin [47]; in addition, more work on “working” correlation structure selection was addressed by Chaganty and Joe [48], Wang and L”
• Consistent model selection and data‐driven smooth tests for longitudinal data in the estimating equations approach (2009, background): “Pan and Mackenzie (2003) introduced a model selection criterion for mean–covariance structure models.”
• Regression models for covariance structures in longitudinal studies (2006, methodology): “The algorithm has now been used in a variety of settings (Pan and MacKenzie, 2003; 2005).”
• Double hierarchical generalized linear models (with discussion) (2006, methodology): “In forming L.ρ/ the use of regression models was studied by Pourahmadi (2000) and Pan and MacKenzie (2003).”
• Semiparametric Mean–Covariance Regression Analysis for Longitudinal Data (2009, background): “See Pan and MacKenzie (2003) for a related discussion.”
• Covariance Estimation: The GLM and Regularization Perspectives (2011, methodology): “Furthermore, one can rely on graphical tools such as the regressogram or AIC to identify models such as (22) for the data; for more details see Pourahmadi (1999, 2001) and Pan and MacKenzie (2003).”
• Multivariate covariance generalized linear models (2015, methodology): “A better option may be to use the beta distribution inflated with 1s (Ospina and Ferrari, 2010), but this model is complicated to fit and interpret.”
• A moving average Cholesky factor model in covariance modelling for longitudinal data (2012, methodology): “Similar to Pan and Mackenzie (2003), we propose a search strategy for finding the optimum model by using the following searches involving the likelihood obtained by saturating the parameter sets in pairs: min Sβ∈S+β ∪S − β BIC(Sβ, Fγ , Fλ), min Sγ∈S+γ ∪S−γ BIC(Fβ, Sγ , Fλ), min Sλ∈S+λ ∪S − λ…”