Multivariate seeded dimension reduction

Yoo, Jae Keun; Im, Yunju

doi:10.1016/j.jkss.2014.03.002

Cited by 5 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The total uncensored cases (deceased) are 138 among 240 patients. More detailed description on the data is founded in 6–8 .…”

Section: Resultsmentioning

confidence: 99%

Analysis of microarray right-censored data through fused sliced inverse regression

Yoo

2019

Sci Rep

Self Cite

View full text Add to dashboard Cite

Sufficient dimension reduction (SDR) for a regression pursue a replacement of the original p-dimensional predictors with its lower-dimensional linear projection. The so-called sliced inverse regression (SIR; [5]) arguably has the longest history in SDR methodologies, but it is still one of the most popular one. The SIR is known to be easily affected by the number of slices, which is one of its critical deficits. Recently, a fused approach for SIR is proposed to relieve this weakness, which fuses the kernel matrices computed by the SIR application from various numbers of slices. In the paper, the fused SIR is applied to a large-p-small n regression of a high-dimensional microarray right-censored data to show its practical advantage over usual SIR application. Through model validation, it is confirmed that the fused SIR outperforms the SIR with any number of slices under consideration.

show abstract

“…The total uncensored cases (deceased) are 138 among 240 patients. More detailed description on the data is founded in 6–8 .…”

Section: Resultsmentioning

confidence: 99%

Analysis of microarray right-censored data through fused sliced inverse regression

Yoo

2019

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…In case 2, where p ≥ n and r < n, reducing the dimensions of predictors is essential. This can be achieved through multivariate seeded dimension reduction, as suggested by Yoo and Im (2014), thereby enabling the application of the YC-method, PFRR, and UPFRR.…”

Section: Implementation Along With Sample Sizesmentioning

confidence: 99%

“…Given the higher number of predictors compared to the sample size in the training data, dimension reduction of predictors is necessary for applying UPFRR and PFRR. This reduction is performed using multivariate seeded reduction (Yoo and Im, 2014). During this process and subsequent analysis, the response variables are standardized to have zero mean and unit variance, ensuring numerical stability in the predictor dimension reduction and the partial least squares fit.…”

Section: Real Data Applicationsmentioning

confidence: 99%

Applications of response dimension reduction in large p-small n problems

Kim,

Yoo

2024

Communications for Statistical Applications and Methods

Self Cite

View full text Add to dashboard Cite

The goal of this paper is to show how multivariate regression analysis with high-dimensional responses is facilitated by the response dimension reduction. Multivariate regression, characterized by multi-dimensional response variables, is increasingly prevalent across diverse fields such as repeated measures, longitudinal studies, and functional data analysis. One of the key challenges in analyzing such data is managing the response dimensions, which can complicate the analysis due to an exponential increase in the number of parameters. Although response dimension reduction methods are developed, there is no practically useful illustration for various types of data such as so-called large p-small n data. This paper aims to fill this gap by showcasing how response dimension reduction can enhance the analysis of high-dimensional response data, thereby providing significant assistance to statistical practitioners and contributing to advancements in multiple scientific domains.

show abstract

Bibliography

2018

An Introduction to Envelopes

View full text Add to dashboard Cite

Multivariate seeded dimension reduction

Cited by 5 publications

References 12 publications

Analysis of microarray right-censored data through fused sliced inverse regression

Analysis of microarray right-censored data through fused sliced inverse regression

Applications of response dimension reduction in large p-small n problems

Bibliography

Contact Info

Product

Resources

About