scholarly journals Bayesian Semi-Parametric Regression for Quantile Residual Lifetime

2014 ◽  
Vol 21 (4) ◽  
pp. 285-296 ◽  
Author(s):  
Taeyoung Park ◽  
Wonho Bae
Keyword(s):  
Residual Lifetime ◽  
Parametric Regression ◽  
Quantile Residual Lifetime

Quantile residual lifetime regression with functional principal component analysis of longitudinal data for dynamic prediction

2018 ◽  
Vol 28 (4) ◽  
pp. 1216-1229
Author(s):  
Xiao Lin ◽  
Ruosha Li ◽  
Fangrong Yan ◽  
Tao Lu ◽  
Xuelin Huang
Keyword(s):  
Principal Component Analysis ◽  
Longitudinal Data ◽  
Regression Model ◽  
Principal Component ◽  
Component Analysis ◽  
Functional Principal Component Analysis ◽  
Residual Lifetime ◽  
Dynamic Prediction ◽  
Functional Principal Component ◽  
Quantile Residual Lifetime

Optimal therapeutic decisions can be made according to disease prognosis, where the residual lifetime is extensively used because of its straightforward interpretation and formula. To predict the residual lifetime in a dynamic manner, a longitudinal biomarker that is repeatedly measured during the post-baseline follow-up period should be included. In this article, we use functional principal component analysis, a powerful and flexible tool, to handle irregularly measured longitudinal data and extract the dominant features over a specific time interval. To capture the time-dependent trajectory pattern, a series of moving time windows are used to estimate window-specific functional principal component analysis scores, which are then combined with a quantile residual lifetime regression model to facilitate dynamic prediction. Estimation of this regression model can be achieved by solving estimating equations with the help of locating the minimizer of the L1-type function. Simulation studies demonstrate the advantages of our proposed method in both calibration and discrimination under various scenarios. The proposed method is applied to data from patients with chronic myeloid leukemia to illustrate its practicality, where we dynamically predict quantile residual lifetimes with longitudinal expression levels of an oncogene, BCR-ABL.

KEBANGGAAN TERHADAP BATIK MADURA BAGI WARGA SUMENEP

2014 ◽  
Vol 4 (2) ◽  
Author(s):  
Sayyida Sayyida ◽  
Nurdody Zakki
Keyword(s):  
Logistic Regression ◽  
Rural Areas ◽  
Research Method ◽  
Community Group ◽  
Parametric Regression ◽  
Logit Function ◽  
The Village ◽  
The City ◽  
Level Of Risk ◽  
Non Parametric

Diversity of Indonesian Batik hanging area. One of the very well-known Indonesian batik is Batik Madura. Batik Madura has become a pride for Indonesia, especially for Madura. The purpose of the study is to model the Sumenep pride to Batik Madura and to see the level of risk or tendency of batik madura pride for the community group Sumenep. This research method uses a non parametric regression used a non-parametric regression because the dependent variable in this study is the variable Y are variables not normally distributed. The results of this study states that the level of risk of the village in Sumenep proud of batik is almost 5 times higher than the islands while people in this city who live in the district town at risk Sumenep proud of Batik Madura 8-fold compared to the archipelago. So it can be concluded that the city is much more proud of batik than those who reside in rural areas especially those who reside in the islands. This study uses data from 100 questionnaires were analyzed using logistic regression analysis. The conclusion of this study is the pride of the batik model as follows: Function logistic regression / logit function: g (x) = 0,074 + 1,568X4(1)+2,159X4(2 this is case the islands as a comparison, X4(1)  is the place to stay in the village and X4(2)  is the place to stay in town, so the Model Opportunities p(x) = EXP(g(x))/1+EXP(g(x)).  Hopes for further research is to conduct research on the development of batik in an integrated region, the need to be disseminated to potential areas of particular potential in Madura batik, especially for residents who reside in the Islands.Keywords: Pride, Batik, Sumenep.

Reliability modeling for multi-state systems with a protective device considering multiple triggering mechanism

2021 ◽  
pp. 1748006X2110133
Author(s):  
Xian Zhao ◽  
Rong Li ◽  
Yu Fan ◽  
Qingan Qiu
Keyword(s):  
Failure Modes ◽  
Operating Time ◽  
Replacement Policy ◽  
Economic Losses ◽  
Residual Lifetime ◽  
Cumulative Number ◽  
Protective Device ◽  
Triggering Mechanism ◽  
Safety Critical ◽  
Markov Chain Imbedding

Failures of safety-critical systems may result in irretrievable economic losses and significant safety hazards, thus enhancing the reliability of safety-critical system is crucial. As applied widely in engineering fields, protective devices are commonly equipped for the systems operating in shock environment to reduce external damage, which has not been taken into consideration in existing literatures. This paper investigates the reliability of multi-state systems with competing failure patterns supported by a protective device. According to the system failure modes, state-based and shock number-based triggering mechanism of the protective device are developed. That is, the protective device is triggered once the system state or cumulative number of shocks exceeds corresponding critical thresholds respectively. After being triggered, the protective device can reduce the probability of damaging shocks for the system. The protective device fails when the number of consecutive valid shocks reaches a threshold. Based on the constructed model, a finite Markov chain imbedding approach is employed to derive reliability indices including distribution functions of system lifetime and residual lifetime, together with expected operating time of the protective device. Moreover, two age-based replacement policies together with a condition-based replacement policy are developed to accommodate different maintenance scenarios and corresponding optimal solutions are acquired. Numerical illustrations based on the application of cooling systems in engines are presented to validate the results.

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