Joint Shared Frailty Survival Modeling Approach to Reliability Analysis of Rolls Used in Rolling Mills

2014 ◽  
Vol 5 (4) ◽  
pp. 64-90 ◽  
Author(s):  
Sumana Das ◽  
Sujit Kumar Majumdar
Keyword(s):  
Recurrent Events ◽  
Survival Models ◽  
Dependence Structure ◽  
Model Parameters ◽  
Rolling Mills ◽  
Modeling Approach ◽  
Multivariate Survival ◽  
Terminal Events ◽  
Shared Frailty ◽  
Survival Modeling

Reliability of repairable rolls used in Rolling Mills was modeled taking to survival modeling route to address the presence of recurrent failure events, censoring event and terminal event processes observed longitudinally in rolls. All the event processes were influenced by measured and unmeasured covariates. Prior to fitting appropriate model, Archimedean Gumbel and Clayton Copula analyses confirmed that the measured covariates had no significant dependence structure. Since the censoring events were “informative terminations”, joint shared frailty multivariate survival models involving Log-Normal, Gamma and Log Gamma frailty distributions were fitted to recurrent and terminal events data where, the ‘frailty' parameter represented the effect of unmeasured covariates related to condition of rolling operation. Gaussian quadrature method helped in estimating the model parameters. Statistical significance of the frailty parameter and its variance in all the models confirmed existence of heterogeneity across the recurrent failure events within and between rolls on account of unmeasured covariates. The statistically significant positive association between hazard functions of both the recurrent failure events and the terminal events justified joint modeling approach to the recurrent events and the terminal events processes observed in rolls. The joint lognormal shared frailty multivariate survival models were considered appropriate for analyzing the reliability of rolls.

scholarly journals Modeling of German Low Voltage Cables with Ground Return Path

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1265 ◽  
Author(s):  
Johanna Geis-Schroer ◽  
Sebastian Hubschneider ◽  
Lukas Held ◽  
Frederik Gielnik ◽  
Michael Armbruster ◽  
...  
Keyword(s):  
Low Voltage ◽  
Measurement Data ◽  
Analytical Calculation ◽  
Laboratory Measurement ◽  
Model Parameters ◽  
Calculation Methods ◽  
Modeling Approach ◽  
Measurement Results ◽  
Simulation Results ◽  
Return Path

In this contribution, measurement data of phase, neutral, and ground currents from real low voltage (LV) feeders in Germany is presented and analyzed. The data obtained is used to review and evaluate common modeling approaches for LV systems. An alternative modeling approach for detailed cable and ground modeling, which allows for the consideration of typical German LV earthing conditions and asymmetrical cable design, is proposed. Further, analytical calculation methods for model parameters are described and compared to laboratory measurement results of real LV cables. The models are then evaluated in terms of parameter sensitivity and parameter relevance, focusing on the influence of conventionally performed simplifications, such as neglecting house junction cables, shunt admittances, or temperature dependencies. By comparing measurement data from a real LV feeder to simulation results, the proposed modeling approach is validated.

Effects of covariates on alternating recurrent events in accelerated failure time models

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Moumita Chatterjee ◽  
Sugata Sen Roy
Keyword(s):  
Recurrent Events ◽  
Failure Time ◽  
Likelihood Method ◽  
Model Parameters ◽  
Accelerated Failure Time ◽  
Survival Times ◽  
Copula Functions ◽  
Event Time ◽  
Accelerated Failure Time Models ◽  
Accelerated Failure

AbstractIn this article, we model alternately occurring recurrent events and study the effects of covariates on each of the survival times. This is done through the accelerated failure time models, where we use lagged event times to capture the dependence over both the cycles and the two events. However, since the errors of the two regression models are likely to be correlated, we assume a bivariate error distribution. Since most event time distributions do not readily extend to bivariate forms, we take recourse to copula functions to build up the bivariate distributions from the marginals. The model parameters are then estimated using the maximum likelihood method and the properties of the estimators studied. A data on respiratory disease is used to illustrate the technique. A simulation study is also conducted to check for consistency.

scholarly journals Estimating the ratio of multivariate recurrent event rates with application to a blood transfusion study

2015 ◽  
Vol 26 (4) ◽  
pp. 1969-1981 ◽  
Author(s):  
Jing Ning ◽  
Mohammad H Rahbar ◽  
Sangbum Choi ◽  
Jin Piao ◽  
Chuan Hong ◽  
...  
Keyword(s):  
Blood Cells ◽  
Latent Variables ◽  
Recurrent Events ◽  
Blood Product ◽  
Massive Transfusion ◽  
Informative Censoring ◽  
Recurrent Event ◽  
Dependence Structure ◽  
Blood Product Transfusion ◽  
Finite Sample

In comparative effectiveness studies of multicomponent, sequential interventions like blood product transfusion (plasma, platelets, red blood cells) for trauma and critical care patients, the timing and dynamics of treatment relative to the fragility of a patient’s condition is often overlooked and underappreciated. While many hospitals have established massive transfusion protocols to ensure that physiologically optimal combinations of blood products are rapidly available, the period of time required to achieve a specified massive transfusion standard (e.g. a 1:1 or 1:2 ratio of plasma or platelets:red blood cells) has been ignored. To account for the time-varying characteristics of transfusions, we use semiparametric rate models for multivariate recurrent events to estimate blood product ratios. We use latent variables to account for multiple sources of informative censoring (early surgical or endovascular hemorrhage control procedures or death). The major advantage is that the distributions of latent variables and the dependence structure between the multivariate recurrent events and informative censoring need not be specified. Thus, our approach is robust to complex model assumptions. We establish asymptotic properties and evaluate finite sample performance through simulations, and apply the method to data from the PRospective Observational Multicenter Major Trauma Transfusion study.

Simple Linear Modeling Approach for Linking Hydrological Model Parameters to the Physical Features of a River Basin

2015 ◽  
Vol 29 (9) ◽  
pp. 3265-3289 ◽  
Author(s):  
Chengcheng Huang ◽  
Guoqiang Wang ◽  
Xiaogu Zheng ◽  
Jingshan Yu ◽  
Xinyi Xu
Keyword(s):  
River Basin ◽  
Hydrological Model ◽  
Model Parameters ◽  
Linear Modeling ◽  
Modeling Approach ◽  
Physical Features

scholarly journals Early origins of longevity: prenatal exposures to food shortage among early Utah pioneers

2012 ◽  
Vol 4 (2) ◽  
pp. 170-181 ◽  
Author(s):  
H. A. Hanson ◽  
K. R. Smith
Keyword(s):  
Later Life ◽  
Adult Mortality ◽  
In Utero ◽  
Food Shortage ◽  
Survival Models ◽  
Critical Periods ◽  
Birth Cohorts ◽  
Population Database ◽  
Mortality Risks ◽  
Shared Frailty

Undernutrition during critical or sensitive prenatal periods may ‘program’ the fetus for increased chronic disease and mortality in later life. Using birth cohorts that were or were not exposed to severe food shortage in Utah in the mid-19th century, this study examines how in utero exposure to undernutrition is associated with mortality after age 50. The Utah Population Database is used to identify 1392 prenatally exposed individuals and 29,022 individuals from subsequent, unexposed birth cohorts. Gompertz hazards with parametric frailty show that males born between April and June of the famine period (and hence exposed during critical periods in utero during the winter months) have higher mortality risks compared with post-famine cohorts. Alternative Cox non-proportional hazard models suggest that females born during the same period have higher initial mortality risks (starting at age 50) that decline over time creating a mortality crossover with unexposed women at approximately age 70, a result not found for men. An ancillary sibling analysis that uses shared frailty survival models to compare individuals with prenatal exposure to undernutrition to their younger (post-famine) same-sex siblings finds no significant differences in adult mortality for males but the pattern for females support the findings from the previous analysis. Although findings are sensitive to model choice, this study presents evidence that is consistent with an association between undernutrition in utero and adult mortality, shows that effects may be sensitive to the duration and gestational period of exposure, and illustrates the differential exposure effects between genders.

scholarly journals Weighted proportional mean inactivity time model

2021 ◽  
Vol 7 (3) ◽  
pp. 4038-4060
Author(s):  
Mohamed Kayid ◽  
◽  
Adel Alrasheedi
Keyword(s):  
Frailty Model ◽  
Real Data ◽  
Survival Models ◽  
Dependence Structure ◽  
Time Model ◽  
Data Set ◽  
Population Variable ◽  
Inactivity Time ◽  
Mean Inactivity Time ◽  
Special Case

<abstract><p>In this paper, a mean inactivity time frailty model is considered. Examples are given to calculate the mean inactivity time for several reputable survival models. The dependence structure between the population variable and the frailty variable is characterized. The classical weighted proportional mean inactivity time model is considered as a special case. We prove that several well-known stochastic orderings between two frailties are preserved for the response variables under the weighted proportional mean inactivity time model. We apply this model on a real data set and also perform a simulation study to examine the accuracy of the model.</p></abstract>

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