Nonlinear general path models for degradation data with dynamic covariates

2015 ◽  
Vol 32 (2) ◽  
pp. 153-167 ◽  
Author(s):  
Zhibing Xu ◽  
Yili Hong ◽  
Ran Jin
Keyword(s):  
Degradation Data ◽  
Path Models

The Contributions of Propensity, Delinquent Peers, Low Parental Supervision, and Empathy to the Emergence of Antisocial Behavior in Childhood and Adolescence: Testing Developmental Path Models Combining Psychological- and Sociological-Criminological Approaches

2020 ◽  
pp. 1-14
Author(s):  
Susanne Wallner ◽  
Stemmler Mark ◽  
Jost Reinecke
Keyword(s):  
Risk Factor ◽  
Antisocial Behavior ◽  
Action Theory ◽  
Cumulative Risk ◽  
Factor Models ◽  
Individual Risk ◽  
Parental Supervision ◽  
Childhood And Adolescence ◽  
Path Models ◽  
Risk Factor Models

Psychological- and sociological-criminological research refers to, for example, cumulative risk factor models (e.g., Lösel & Bender, 2003) and Situational Action Theory (SAT; e.g., Wikström, 2006). The German longitudinal study “Chances and Risks in the Life Course“ (research project A2, Collaborative Research Center 882; e.g., Reinecke, Stemmler, & Wittenberg, 2016) focuses upon the development of antisocial behavior from a psychological and sociological point of view. Two-wave panel data of two cohorts (children and adolescents) were utilized to test the power of developmental path models investigating the development of antisocial behavior. Individual risk seems to have both direct and indirect influences on antisocial behavior, supporting the ideas of risk factor models; antisocial behavior might be the outcome of the interaction between propensity and criminogenic exposure, so there is evidence for SAT. Additionally, empathy seems to be related to both propensity and low parental supervision. Implications for the study of antisocial behavior in childhood and adolescence are discussed in line with developmental criminology.

Testing competing path models linking the biochemical variables in red blood cells from Li+ -treated bipolar patients

2003 ◽  
Vol 5 (5) ◽  
pp. 320-329 ◽  
Author(s):  
Nicole Williams ◽  
Brian T Layden ◽  
Joyce Suhy ◽  
Tabitha Metreger ◽  
Kathleen Foley ◽  
...  
Keyword(s):  
Red Blood Cells ◽  
Blood Cells ◽  
Biochemical Variables ◽  
Path Models ◽  
Bipolar Patients

scholarly journals Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data

Technometrics ◽  
2015 ◽  
Vol 57 (2) ◽  
pp. 180-193 ◽  
Author(s):  
Yili Hong ◽  
Yuanyuan Duan ◽  
William Q. Meeker ◽  
Deborah L. Stanley ◽  
Xiaohong Gu
Keyword(s):  
Statistical Methods ◽  
Degradation Data ◽  
Outdoor Weathering

scholarly journals Bivariate Nonlinear Diffusion Degradation Process Modeling via Copula and MCMC

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Huibing Hao ◽  
Chun Su
Keyword(s):  
Nonlinear Diffusion ◽  
Goodness Of Fit ◽  
Real Data ◽  
Degradation Process ◽  
Copula Function ◽  
Assessment Method ◽  
Unknown Parameters ◽  
Degradation Data ◽  
Proposed Model ◽  
Frank Copula

A novel reliability assessment method for degradation product with two dependent performance characteristics (PCs) is proposed, which is different from existing work that only utilized one dimensional degradation data. In this model, the dependence of two PCs is described by the Frank copula function, and each PC is governed by a random effected nonlinear diffusion process where random effects capture the unit to unit differences. Considering that the model is so complicated and analytically intractable, Markov Chain Monte Carlo (MCMC) method is used to estimate the unknown parameters. A numerical example about LED lamp is given to demonstrate the usefulness and validity of the proposed model and method. Numerical results show that the random effected nonlinear diffusion model is very useful by checking the goodness of fit of the real data, and ignoring the dependence between PCs may result in different reliability conclusion.

Multisensor Degradation Data Fusion and Remaining Life Prediction

2017 ◽  
Author(s):  
Changxi Wang ◽  
E. A. Elsayed ◽  
Kang Li ◽  
Javier Cabrera
Keyword(s):  
Data Fusion ◽  
Prediction Accuracy ◽  
Life Prediction ◽  
Clustering Algorithm ◽  
Partial Information ◽  
Degradation Process ◽  
Sensor Data ◽  
Multiple Sensors ◽  
Degradation Data ◽  
Degradation Monitoring

Multiple sensors are commonly used for degradation monitoring. Since different sensors may be sensitive at different stages of the degradation process and each sensor data contain only partial information of the degraded unit, data fusion approaches that integrate degradation data from multiple sensors can effectively improve degradation modeling and life prediction accuracy. We present a non-parametric approach that assigns weights to each sensor based on dynamic clustering of the sensors observations. A case study that involves a fatigue-crack-growth dataset is implemented in order evaluate the prognostic performance of the unit. Results show that the fused path obtained with the proposed approach outperforms any individual sensor data and other paths obtained with an adaptive threshold clustering algorithm in terms of life prediction accuracy.

Sign in / Sign up

Export Citation Format

Share Document