scholarly journals Reliability Analysis of Distribution Transformer with Bayesian Mixture and Cox Regression Approach

2021 ◽  
Vol 179 ◽  
pp. 305-312
Author(s):  
Rinda Nariswari ◽  
Herena Pudjihastuti
Keyword(s):  
Reliability Analysis ◽  
Cox Regression ◽  
Distribution Transformer ◽  
Regression Approach ◽  
Bayesian Mixture

Regression approach to tire reliability analysis

2002 ◽  
Vol 78 (3) ◽  
pp. 267-273 ◽  
Author(s):  
V.V Krivtsov ◽  
D.E Tananko ◽  
T.P Davis
Keyword(s):  
Reliability Analysis ◽  
Regression Approach

A sparse surrogate model for structural reliability analysis based on the generalized polynomial chaos expansion

2018 ◽  
Vol 233 (3) ◽  
pp. 487-502 ◽  
Author(s):  
Qian Liu ◽  
Xufang Zhang ◽  
Xianzhen Huang
Keyword(s):  
Reliability Analysis ◽  
Surrogate Model ◽  
Structural Reliability ◽  
Polynomial Chaos ◽  
Global Sensitivity Analysis ◽  
Sparse Regression ◽  
Global Sensitivity ◽  
Training Samples ◽  
Generalized Polynomial ◽  
Regression Approach

The reliability analysis of a structural system is typically evaluated based on a multivariate model that describes the underlying mechanistic relationship between the system’s input and output random variables. This is the need to develop an effective surrogate model to mimic the input–output relationship as the Monte Carlo simulation–based on the mechanistic model might be computationally intensive. In this regard, the article presents a sparse regression method for structural reliability analysis based on the generalized polynomial chaos expansion. However, results from the global sensitivity analysis have justified that it is unnecessary to contain all polynomial terms in the surrogate model, instead of comprising a rather small number of principle components only. One direct benefit of the sparse approximation allows utilizing a small number of training samples to calibrate the surrogate model, bearing in mind that the required sample size is positively proportional to the number of unknowns in regression analysis. Therefore, by utilizing the standard polynomial chaos basis functions to constitute an explanatory dictionary, an adaptive sparse regression approach characterized by introducing the most significant explanatory variable in each iteration is presented. A statistical approach for detecting and excluding spuriously explanatory polynomials is also introduced to maintain the high sparsity of the meta-modeling result. Combined with a variety of low-discrepancy schemes in generating training samples, structural reliability and global sensitivity analysis of originally true but computationally demanding models are alternatively realized based on the sparse surrogate method in conjunction with the brutal Monte Carlo simulation method. Numerical examples are carried out to demonstrate the applicability of the sparse regression approach to structural reliability problems. Results have shown that the proposed method is an effective, non-intrusive approach to deal with uncertainty analysis of various structural systems.

Optimal pricing in retail: a Cox regression approach

2013 ◽  
Vol 41 (4) ◽  
pp. 311-320 ◽  
Author(s):  
Rudi Meijer ◽  
Sandjai Bhulai
Keyword(s):  
Cox Regression ◽  
Optimal Pricing ◽  
Regression Approach

Integration of pathway structure information into a reweighted partial Cox regression approach for survival analysis on high-dimensional gene expression data

2015 ◽  
Vol 11 (7) ◽  
pp. 1876-1886 ◽  
Author(s):  
Wei Liu ◽  
Qiuyu Wang ◽  
Jianmei Zhao ◽  
Chunlong Zhang ◽  
Yuejuan Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Survival Analysis ◽  
Gene Expression Data ◽  
Clinical Utility ◽  
Cox Regression ◽  
High Dimensional ◽  
Expression Data ◽  
Structure Information ◽  
Regression Approach ◽  
Risk Of Cancer

Accurately predicting the risk of cancer relapse or death is important for clinical utility.

scholarly journals Predictive Factors of Survival Time of Breast Cancer in Kurdistan Province of Iran between 2006-2014: A Cox Regression Approach

2014 ◽  
Vol 15 (19) ◽  
pp. 8483-8488 ◽  
Author(s):  
Asrin Karimi ◽  
Ali Delpisheh ◽  
Kourosh Sayehmiri ◽  
Hojjatollah Saboori ◽  
Ezzatollah Rahimi
Keyword(s):  
Breast Cancer ◽  
Survival Time ◽  
Predictive Factors ◽  
Cox Regression ◽  
Regression Approach

Human reliability analysis for control room upgrades

2009 ◽  
Author(s):  
Ronald Laurids Boring ◽  
Johanna Oxstrand ◽  
Michael Hildebrandt
Keyword(s):  
Reliability Analysis ◽  
Control Room ◽  
Human Reliability ◽  
Human Reliability Analysis
Sign in / Sign up

Export Citation Format

Share Document