Optimal model averaging for multivariate regression models

Liao, Jun; Wan, Alan T. K.; He, Shuyuan; Zou, Guohua

doi:10.1016/j.jmva.2021.104858

Cited by 3 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…the column vectors of W are therefore linearly independent [26][27]. Since W is a symmetric array, the row vectors of W are also linearly independent.…”

Section: Analysis Of the Effects Of Overfittingmentioning

confidence: 99%

The Impact of the Big Ideology Program on the Construction of Academic Ethos in Colleges and Universities in the Context of Three-Whole Parenting

Liu,

Tao

2023

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

This paper uses multiple linear regression model to study the correlation between the ideological and political courses and the construction of university academic style, explores the calculation process of multiple linear regression, studies the impact analysis of linear regression model and the diagnosis of abnormal points, and analyzes the underfitting and overfitting respectively. The linear regression analysis model was used to study the differences in the academic style level of college students of different grades and genders, the correlation analysis of various dimensions of academic style and ideological and political level was carried out, and the linear fit of ideological and political curriculum education and academic style was analyzed by MATLAB to infer the significance level of ideological and political education and academic style. The results show that the correlation coefficient between ideology and politics and the dimension of learning purpose is 0.053, P=0.003<0.05. The correlation coefficient between ideology and politics and learning attitude was 0.239, P=0.008<0.05. The correlation coefficient between ideology and politics and learning effect dimension was 0.682, P=0.018<0.05. The data presented shows that ideology and politics have a significant impact on the construction of academic style.

show abstract

“…the column vectors of W are therefore linearly independent [26][27]. Since W is a symmetric array, the row vectors of W are also linearly independent.…”

Section: Analysis Of the Effects Of Overfittingmentioning

confidence: 99%

The Impact of the Big Ideology Program on the Construction of Academic Ethos in Colleges and Universities in the Context of Three-Whole Parenting

Liu,

Tao

2023

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

show abstract

“…Fortunately, such a dilemma can be well avoided by adopting the model averaging (MA) technique and various MA methods are discussed under different models and conditions, more details can be found in Hansen, 22 Liu and Okui, 23 Qiu et al, 24 Fang et al, 25 Li et al, 26 and so forth. To the best of our knowledge, there are a few MA techniques designed under univariate count model, [27][28][29][30] and a handful of works on MA estimation/predictions for multi-dimensional continuous data, [31][32][33][34] while relevant MA technique for multivariate count model has never been studied before. Therefore, we would like to provide a new MA prediction approach for multivariate count data analysis.…”

mentioning

confidence: 99%

Predicting the multivariate zero‐inflated counts: A novel model averaging method under Pearson loss

Liu,

Gao

2024

Statistics in Medicine

View full text Add to dashboard Cite

Excessive zeros in multivariate count data are often observed in scenarios of biomedicine and public health. To provide a better analysis on this type of data, we first develop a marginalized multivariate zero‐inflated Poisson (MZIP) regression model to directly interpret the overall exposure effects on marginal means. Then, we define a multiple Pearson residual for our newly developed MZIP regression model by simultaneously taking heterogeneity and correlation into consideration. Furthermore, a new model averaging prediction method is introduced based on the multiple Pearson residual, and the asymptotical optimality of this model averaging prediction is proved. Simulations and two empirical applications in medicine are used to illustrate the effectiveness of the proposed method.

show abstract

Optimal model averaging for partially linear models with missing response variables and error‐prone covariates

Liang,

Wang,

Cai

2024

Statistics in Medicine

View full text Add to dashboard Cite

We consider the problem of optimal model averaging for partially linear models when the responses are missing at random and some covariates are measured with error. A novel weight choice criterion based on the Mallows‐type criterion is proposed for the weight vector to be used in the model averaging. The resulting model averaging estimator for the partially linear models is shown to be asymptotically optimal under some regularity conditions in terms of achieving the smallest possible squared loss. In addition, the existence of a local minimizing weight vector and its convergence rate to the risk‐based optimal weight vector are established. Simulation studies suggest that the proposed model averaging method generally outperforms existing methods. As an illustration, the proposed method is applied to analyze an HIV‐CD4 dataset.

show abstract

Optimal model averaging for multivariate regression models

Cited by 3 publications

References 25 publications

The Impact of the Big Ideology Program on the Construction of Academic Ethos in Colleges and Universities in the Context of Three-Whole Parenting

The Impact of the Big Ideology Program on the Construction of Academic Ethos in Colleges and Universities in the Context of Three-Whole Parenting

Predicting the multivariate zero‐inflated counts: A novel model averaging method under Pearson loss

Optimal model averaging for partially linear models with missing response variables and error‐prone covariates

Contact Info

Product

Resources

About