Deviance residuals based PLS regression for censored data in high dimensional setting

2008 ◽  
Vol 91 (1) ◽  
pp. 78-86 ◽  
Author(s):  
Philippe Bastien
Keyword(s):  
Censored Data ◽  
High Dimensional ◽  
Pls Regression ◽  
Deviance Residuals

scholarly journals Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data

2014 ◽  
Vol 31 (3) ◽  
pp. 397-404 ◽  
Author(s):  
Philippe Bastien ◽  
Frédéric Bertrand ◽  
Nicolas Meyer ◽  
Myriam Maumy-Bertrand
Keyword(s):  
Censored Data ◽  
Pls Regression ◽  
Sparse Kernel ◽  
Deviance Residuals

scholarly journals Estimation and application in log-Fréchet regression model using censored data

2017 ◽  
Vol 5 (1) ◽  
pp. 23 ◽  
Author(s):  
Hanan Alamoudi ◽  
Salwa‎ Mousa‎ ◽  
Lamya Baharith
Keyword(s):  
Maximum Likelihood ◽  
Regression Model ◽  
Censored Data ◽  
Empirical Distribution ◽  
Model Parameters ◽  
Monte Carlo Simulation Study ◽  
Proposed Model ◽  
Data Validity ◽  
Global Influence ◽  
Deviance Residuals

This article introduces a new location-scale regression model based on a log-Fréchet distribution. Maximum likelihood and Jackknife methods are used to estimate the new model parameters for censored data. Martingale and deviance residuals are obtained to check model assumptions, data validity, and detect outliers. Moreover, global influence is used to detect influential observations. Monte Carlo simulation study is provided to compare the performance of the maximum likelihood and jackknife estimators for different sample sizes and censoring percentages. The empirical distribution of the martingale and deviance residuals of the proposed model is examined. A real lifetime heart transplant data is analyzed under the log-Fréchet regression model to illustrate the satisfactory results of the proposed model.

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

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Jae Keun Yoo
Keyword(s):  
Dimension Reduction ◽  
Censored Data ◽  
Sliced Inverse Regression ◽  
High Dimensional ◽  
Sufficient Dimension Reduction ◽  
Inverse Regression ◽  
Right Censored Data ◽  
Practical Advantage ◽  
Linear Projection ◽  
Lower Dimensional

Abstract 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.

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