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Optimizing configuration of supply chain with survival assessment model
Published in Kennis Chan, Testing and Measurement: Techniques and Applications, 2015
Felix T. S. Chan, B. Niu, A. Nayak, R. Raj, M. K. Tiwari
The simulation model has been developed and run in matlab. The Cox-PH simulation model has been run in the windows 7 platform using R Software package. The R-Square value obtained for the Cox-PH model fitting obtained is 0.474 which can be considered as a good fit. The standard error for the fitting is 0.032. Time taken for the above simulation to run was observed to be 0.32 and 0.26 seconds respectively for two Cox-PH fitting. The p-value for logrank test, wald test and likelihood ratio test is approximately 0. Since, the fact that the lower the p-value, the more statistically significant a model is, thus, it can be considered that the model proposed is a good model which is statistically significant. Logrank test is a hypothesis test to address the variable and their effect in survival model. Wald test is used to find the relationship between the data items. The Log likelihood test is used to compare two models, null model and alternate model and estimates whether the data fits which model better. For addressing a real life situation with many more factors and interactions, first a pre-screening may be used to avoid over-fitting and under-fitting of the Cox-PH model to bring the number of factors to a level so that it best represents the supply chain under study. 4 RESULT INTERPRETATION The Cox-PH model finds the coe cients corresponding to di erent variables. These exponential coe cients are interpretable as the multiplicative e ects of the variable. Thus, for example, holding other covariates (factors in the model) constant, a change of one unit of variable 3 from the optimal values as obtained
Analyzing Toxicity Data Using Statistical Models for Time-To-Death: An Introduction
Published in Michael C. Newman, Alan W. McIntosh, Metal Ecotoxicology, 2020
Philip M. Dixon, Michael C. Newman
The log-rank test can be derived as a comparison of the observed numbers of deaths to the expected number of deaths if every group had the same survival curve.17,18 If we consider only the number of animals still alive at the end of an experiment, a standard approach to answering the question, “Were the mortality rates in groups 1 and 2 different?” would be to construct 2×2 contingency table like Table 4. The log-rank test generalizes this approach to all times of death.
Influence of NMAS and groove depths on the static and fatigue shear performance of aggregate interlocking in PQC mixes
Published in International Journal of Pavement Engineering, 2022
Moreover, it is seen from the K–M plots shown in Figure 9(bd,f), that the survival probability is significantly higher in the case of GD of 1/4th diameter for a PQC mix of A26.5 and A31.5. However, in the A19 PQC mix, no significant difference in survival probability for the two GDs (1/4th of diameter and 1/thirrd of diameter) is observed. At the survival probability of 50%, the shear fatigue life of A31.5 PQC mix is largely dependent on the GD when compared to the other two PQC mixes. It is observed from K–M plots that at common stress level, GD and survival probability, the shear fatigue life of PQC mixes is in the decreasing order of A31.5, A26.5 and A19, respectively. The log-rank test is a popular test being used to compare the survival curves. The null hypothesis of this statistic test is that the survival curves of all three PQC mixes are same, either for the three stress levels or for the two different GDs (Dudley et al.2016).
Competing risks models for the deterioration of highway pavement subject to hurricane events
Published in Structure and Infrastructure Engineering, 2019
Sylvester Inkoom, John O. Sobanjo
The variations among the different modes of failure were investigated using different statistics from the Kaplan–Meier estimates and cumulative incidence function outcomes. The cumulative incidence function is a summary statistic for the evaluating the cumulative failure rates over time due to a particular cause. It estimates the cause-specific hazard function of all causes (Andersen, Klein, & Rosthøj, 2003; Klein, Gerster, Andersen, Tarima, & Perme, 2008; Scheike & Zhang, 2002). The various risks are compared using the log rank test and the hazard ratio. The log-rank test is a hypothesis test used to compare the survival distributions of two samples. It is a nonparametric test and usually employed when the data are right skewed. The statistic which yields the chi-square estimate and corresponding -value are used in this study to determine whether the survival of the pavement due to crack deterioration and the hurricane failure are significantly different along with the Kaplan–Meier curves and estimates.
Proso millet peroxidase-mediated degradation and detoxification of Rhodamine B in water
Published in Environmental Technology, 2023
Jiao Li, Wenyan Li, Jianjian Hu, Chen Li, Xiaodong Cui
Each experiment was replicated at least three times under identical conditions and the data were expressed as the mean ± SD unless otherwise stated. Statistical analyses of all data (except that from the survival assay) were conducted using Student’s t-tests (unpaired, two tailed) after testing for equal distribution and equal variance within the data set. To compare distributions between different experimental treatment groups of the survival assay, the log rank test was used. All calculations were performed using SPSS version 13.0 (Armonk, NY). A p value ≤0.05 (*) indicates a statistically significant difference; a p value ≤0.01 (**) indicates a remarkably significant difference; and a p value ≤0.001 (***) indicates an extremely significant difference.