Comparison of partial least square algorithms in hierarchical latent variable model with missing data

Cheng, Huang

doi:10.1177/0037549720944467

Cited by 2 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In second order model with CQR, manifest variables cannot be estimated through traditional PLS estimation process and instead they are the information sources that both path and loading coefficients estimations and LVs scores rely on. Therefore, the missing data problem will be investigated under the framework of second order model with CQR according to the existing works [20][21][22][23].…”

Section: Remark2mentioning

confidence: 99%

Second Order Model with Composite Quantile Regression

Cheng

2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

In many fields, comprehensive evaluation is a very important topic. Both second order model and its modified one with quantile regression have been widely used in evaluation. Although quantile-type second order model has abilities in capturing a complete picture of different variables’ relations at different quantile levels. Sometimes we find it complex to summarize the conclusions of all quantile levels. Therefore, we propose a new second order model with composite quantile regression, which has the advantage of obtaining a whole evaluation result based on all quantile levels. More specifically, one of our paper’s main contributions is to develop a comprehensive evaluation model based on the classical second order model, quantile regression and composite quantile regression. What’s more, we introduce a modified partial least square estimation algorithm under the well-known partial least squares framework. The new algorithm has the ability in estimating both path and loading coefficients reflecting the relationships among manifest variables and latent variables at all quantile levels.

show abstract

Section: Remark2mentioning

confidence: 99%

Second Order Model with Composite Quantile Regression

Cheng

2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…In the case of categorical variables, relatively limited methods can be used to impute new values. In this paper, we consider k-nearest neighbours imputation method (KNN) based on the most frequent value due to the case of categorical variables in real data [6].…”

Section: Introductionmentioning

confidence: 99%

KNN-SVM Classifiers in Complex Diagnosis

Cheng

2024

J. Phys.: Conf. Ser.

Self Cite

View full text Add to dashboard Cite

In many applications, classification plays an indispensable role due to its powerful detection and diagnosis function. Especially in real data on disease, the detection of important factors and the diagnosis of the result usually bring huge contributions to patients. Simultaneously, complex problems in real data such as imbalanced data and missing data also lead to more challenges and difficulties. The ignorance of missing data will undermine study efficiency, and sometimes introduce substantial bias. Imbalanced data tends to be overwhelmed by the majority classes and ignoring the minority ones. The paper develops new support vector machine classifiers using k-nearest neighbors’ information (KNN-SVM), to impute missing data by calculating k-nearest neighbors’ statistical characteristic values and to interpolate some new samples between k-nearest minority class examples. As comparisons, the paper uses different kernel functions in KNN-SVM classifiers to show the different performances in disease diagnosis accuracy.

show abstract

Comparison of partial least square algorithms in hierarchical latent variable model with missing data

Cited by 2 publications

References 29 publications

Second Order Model with Composite Quantile Regression

Second Order Model with Composite Quantile Regression

KNN-SVM Classifiers in Complex Diagnosis

Contact Info

Product

Resources

About