scholarly journals A Non-Parametric Approach for Survival Analysis of Component-Based Software

2020 ◽  
Vol 5 (2) ◽  
pp. 309-318
Author(s):  
Sandeep Chopra ◽  
Lata Nautiyal ◽  
Preeti Malik ◽  
Mangey Ram ◽  
Mahesh K. Sharma
Keyword(s):  
Survival Analysis ◽  
Failure Rate ◽  
Growth Models ◽  
Reliability Estimation ◽  
Parametric Models ◽  
Model Parameters ◽  
Parametric Approach ◽  
Estimation Model ◽  
Failure Data ◽  
Non Parametric

Reliability of a software or system is the probability of system to perform its functions adequately for the stated time period under specific environment conditions. In case of component-based software development reliability estimation is a crucial factor. Existing reliability estimation model falls into two broad categories parametric and non-parametric models. Parametric models approximate the model parameters based on the assumptions of fundamental distributions. Non-parametric models enable parameter estimation of the software reliability growth models without any assumptions. We have proposed a novel non-parametric approach for survival analysis of components. Failure data is collected based on which we have calculated failure rate and reliability of the software. Failure rate increases with the time whereas reliability decreases with the time.

scholarly journals Modeling Software Fault-Detection and Fault-Correction Processes by Considering the Dependencies between Fault Amounts

2021 ◽  
Vol 11 (15) ◽  
pp. 6998
Author(s):  
Qiuying Li ◽  
Hoang Pham
Keyword(s):  
Time Delay ◽  
Fault Detection ◽  
Software Reliability ◽  
Growth Models ◽  
Predictive Performance ◽  
Real Data ◽  
Reliability Estimation ◽  
Model Parameters ◽  
Dual Processes ◽  
Correction Process

Many NHPP software reliability growth models (SRGMs) have been proposed to assess software reliability during the past 40 years, but most of them have focused on modeling the fault detection process (FDP) in two ways: one is to ignore the fault correction process (FCP), i.e., faults are assumed to be instantaneously removed after the failure caused by the faults is detected. However, in real software development, it is not always reliable as fault removal usually needs time, i.e., the faults causing failures cannot always be removed at once and the detected failures will become more and more difficult to correct as testing progresses. Another way to model the fault correction process is to consider the time delay between the fault detection and fault correction. The time delay has been assumed to be constant and function dependent on time or random variables following some kind of distribution. In this paper, some useful approaches to the modeling of dual fault detection and correction processes are discussed. The dependencies between fault amounts of dual processes are considered instead of fault correction time-delay. A model aiming to integrate fault-detection processes and fault-correction processes, along with the incorporation of a fault introduction rate and testing coverage rate into the software reliability evaluation is proposed. The model parameters are estimated using the Least Squares Estimation (LSE) method. The descriptive and predictive performance of this proposed model and other existing NHPP SRGMs are investigated by using three real data-sets based on four criteria, respectively. The results show that the new model can be significantly effective in yielding better reliability estimation and prediction.

scholarly journals Technical note: A bootstrapped LOESS regression approach for comparing soil depth profiles

2016 ◽  
Vol 13 (13) ◽  
pp. 3863-3868 ◽  
Author(s):  
Aidan M. Keith ◽  
Peter A. Henrys ◽  
Rebecca L. Rowe ◽  
Niall P. McNamara
Keyword(s):  
Land Use ◽  
Soil Profile ◽  
Soil Depth ◽  
Technical Note ◽  
Model Parameters ◽  
Land Uses ◽  
Parametric Approach ◽  
Soil System ◽  
Depth Profiles ◽  
Non Parametric

Abstract. Understanding the consequences of different land uses for the soil system is important to make better informed decisions based on sustainability. The ability to assess change in soil properties, throughout the soil profile, is a critical step in this process. We present an approach to examine differences in soil depth profiles between land uses using bootstrapped LOESS regressions (BLRs). This non-parametric approach is data-driven, unconstrained by distributional model parameters and provides the ability to determine significant effects of land use at specific locations down a soil profile. We demonstrate an example of the BLR approach using data from a study examining the impacts of bioenergy land use change on soil organic carbon (SOC). While this straightforward non-parametric approach may be most useful in comparing SOC profiles between land uses, it can be applied to any soil property which has been measured at satisfactory resolution down the soil profile. It is hoped that further studies of land use and land management, based on new or existing data, can make use of this approach to examine differences in soil profiles.

scholarly journals Technical note: A new approach for comparing soil depth profiles using bootstrapped Loess regression (BLR)

2015 ◽  
Vol 12 (23) ◽  
pp. 19199-19211 ◽  
Author(s):  
A. M. Keith ◽  
P. Henrys ◽  
R. L. Rowe ◽  
N. P. McNamara
Keyword(s):  
Land Use ◽  
Soil Profile ◽  
Soil Depth ◽  
Technical Note ◽  
Model Parameters ◽  
Land Uses ◽  
Parametric Approach ◽  
Soil System ◽  
Depth Profiles ◽  
Non Parametric

Abstract. Understanding the consequences of different land uses for the soil system is important to better inform decisions based on sustainability. The ability to assess change in soil properties, throughout the soil profile, is a critical step in this process. We present an approach to examine differences in soil depth profiles between land uses using bootstrapped Loess regressions (BLR). This non-parametric approach is data-driven, unconstrained by distributional model parameters and provides the ability to determine significant effects of land use at specific locations down a soil profile. We demonstrate an example of the BLR approach using data from a study examining the impacts of bioenergy land use change on soil carbon (C). While this straightforward non-parametric approach may be most useful in comparing soil C or organic matter profiles between land uses, it can be applied to any soil property which has been measured at satisfactory resolution down the soil profile. It is hoped that further studies of land use and land management, based on new or existing data, can make use of this approach to examine differences in soil profiles.

TWO SECTIONAL MODELS INVOLVING TWO WEIBULL DISTRIBUTIONS

1999 ◽  
Vol 06 (02) ◽  
pp. 103-122 ◽  
Author(s):  
RENYAN JIANG ◽  
MING J. ZUO ◽  
D. N. P. MURTHY
Keyword(s):  
Probability Density ◽  
Failure Rate ◽  
Real Data ◽  
Model Parameters ◽  
Weibull Distributions ◽  
Data Set ◽  
Failure Data ◽  
Rate Functions ◽  
Estimation Of Model Parameters

In this paper, we study two sectional models, each involving two Weibull distributions. Characterization of the plot on Weibull plotting paper (WPP) for each model is carried out. We also study the shapes of the probability density and the failure rate functions. These are useful in determining if a given failure data set can be modeled by such a model. We discuss the estimation of model parameters based on the WPP plot and illustrate through two examples involving real data.

scholarly journals Fitting a non-parametric stock–recruitment model in R that is useful for deriving MSY reference points and accounting for model uncertainty

2012 ◽  
Vol 70 (1) ◽  
pp. 56-67 ◽  
Author(s):  
Noel G. Cadigan
Keyword(s):  
Confidence Intervals ◽  
Model Uncertainty ◽  
Fishery Management ◽  
Reference Points ◽  
Parametric Models ◽  
Parametric Approach ◽  
Stock Size ◽  
Stock Recruitment ◽  
Recruitment Model ◽  
Non Parametric

Abstract Cadigan, N. G. 2013. Fitting a non-parametric stock–recruitment model in R that is useful for deriving MSY reference points and accounting for model uncertainty. – ICES Journal of Marine Science, 70:56–67. Modelling the relationship between parental stock size and subsequent recruitment of fish to a fishery is often required when deriving reference points, which are a fundamental component of fishery management. A non-parametric approach to estimate stock–recruitment relationships is illustrated using a simulated example and nine case studies. The approach preserves compensatory density dependence in which the recruitment rate monotonically decreases as stock size increases, which is a basic assumption of commonly used parametric stock–recruitment models. The implications of the non-parametric estimates on maximum sustainable yield (MSY) reference points are illustrated. The approach is used to provide non-parametric bootstrapped confidence intervals for reference points. The efficacy of the approach is investigated using simulations. The results demonstrate that the non-parametric approach can provide a more realistic estimation of the stock–recruitment relationship when informative data are available compared with common parametric models. Also, bootstrap confidence intervals for MSY reference points based on different parametric stock–recruitment models often do not overlap. The confidence intervals based on the non-parametric approach tend to be much wider, and reflect better uncertainty due to stock–recruit model choice.

A Reliability Model Validation Method for Mitigating the Effects of Measurement Uncertainty

2019 ◽  
Author(s):  
Mohammadkazem Sadoughi ◽  
Meng Li ◽  
Joseph Beck ◽  
Chao Hu
Keyword(s):  
Measurement Uncertainty ◽  
Model Validation ◽  
Experimental Measurement ◽  
Limit State ◽  
Reliability Estimation ◽  
State Function ◽  
Reliability Model ◽  
Estimation Model ◽  
Validation Method ◽  
Failure Data

Abstract With the increasing role of numerical modeling in engineering design and development processes, improved techniques are needed for validating computational results against experimental measurements. Most existing validation methods suffer from two main limitations: (i) they are often highly sensitive to the experimental measurement uncertainty, and (ii) extending these methods for reliability model validation requires large quantities of failure data that may be very time-consuming or costly to obtain. In order to overcome the aforementioned limitations, this study proposes an indirect reliability model validation method. First, a new procedure for computing a validation metric is developed based on Richardson extrapolation (RE) to reduce the sensitivity of the metric to the experimental measurement uncertainty. Second, a new validation metric is defined based on the limit state function (LSF) approximation to extend numerical model validation to reliability model validation. The proposed method is illustrated by validating a reliability estimation model for a cantilever beam under a vertical load.

scholarly journals Survival Analysis of Cholera Patients a Parametric and Non-parametric Approach

2019 ◽  
pp. 1-18
Author(s):  
Umar M. Hassan ◽  
A. A. Abiodun
Keyword(s):  
Risk Factors ◽  
Survival Analysis ◽  
Survival Probability ◽  
Failure Time ◽  
Parametric Method ◽  
Significant Risk ◽  
Rank Test ◽  
Parametric Approach ◽  
Survival Times ◽  
Non Parametric

Aims: The aim of this study is to investigate survival probability of cholera patients who were under follow-up and identify significant risk factors for mortality. Methodology: In this research, we present the basic concepts, nonparametric methods (the Kaplan-Meier method and the log-rank test) and parametric method. Parametric AFT models (Exponential, Weibull, Lognormal and Log logistic) were compared using Akaike’s Information Criterion (AIC). Results: Recorded data of 513 patients were obtained from UNICEF Cholera Hospital for Internally Displaced Persons Camps within Maiduguri, Borno State. Non-Parametric and Parametric approach were used to estimate the survival probability of the patients and examine the association between the survival times with different risk factors. The analysis shows that some factors significantly contribute to longer survival time of cholera patients. These factors include being a female, age less than twenty, being vaccinated before the infection and mild degree of dehydration. Conclusion: The vaccination, age, sex and degree of dehydration of a cholera patient affects its survival hence, much attention should be given to older patients, degree of dehydration and vaccine (killed oral 01 with whole-cell with Bsubunit) should be administered whenever there is outbreak. When carrying out survival analysis of this kind, a Weibull model is Recommended for used while if dealing with Accelerated Failure Time models.

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