Search results

The main purpose of this study is to evaluate the extent of dropout students and identify the relationship between risk factors of dropout and the survival time of students.

The Kaplan–Meier estimator (KM), also known as the product-limit technique, is a nonparametric model function that is commonly used in estimating survival function events (Kaplan and Meier, 1958). The survival function's Kaplan–Meier estimators are used to estimate and graph survival probabilities as a function of time, as well as explanatory data analysis (EDA) for the survival data, including the median survival time, and compare for two or more of the survival events. In addition, Cox proportional hazards model is employed for modelling purpose.

Results of the Kaplan–Meier curves show that male students have lower survival rates than female, researchers have found that there is a difference between the survival times of the student's school types, results show students from English-based schools are higher than Arabic-based schools as suggested by the survival curve. Similarly, there is a difference between the survival times of students aging equal or greater than 25 and students aging less than 25 and survival function estimates of dropout according to high school grade marks has huge difference. These results were confirmed using log rank test as age, school type and marks were statistically significantly different while gender is not statistically significant.

There is no study of this kind from the Somalia context about the student's dropout. Subsequent to the outbreak of civil war in 1988 and the collapse of the central government in 1991, all public social services in Somalia including education centers were severely disrupted.

The statistical methods discussed in the previous section will be applied on a real dataset obtained from different offices of the university; most of the data were extracted from faculty of economics office and admission and record office. The data set comprised of 70 students from SIMAD university, consists of full-time faculty of economics students who enrolled at the university in the academic year of 2017–2018 until two years of diploma, students either complete 24 months of diploma or leave the university and that is the event of interest.

The purpose of this paper is to apply survival analysis, using Cox proportional hazards regression (CPHR), to the problem of predicting if and when supply chain (SC) customers or suppliers might file a petition for bankruptcy so that proactive steps may be taken to avoid a SC disruption.

CPHR is first compared to multiple discriminant analysis (MDA) and logistic regression (LR) to assess its suitability and accuracy to SC applications using three years of financial quarterly data for 69 non-bankrupt and 74 bankrupt organizations. A k-means clustering approach is then applied to the survival curves of all 143 organizations to explore heuristics for predicting the timing of bankruptcy petitions.

CPHR makes bankruptcy predictions at least as accurately as MDA and LR. The survival function also provides valuable information on when bankruptcy might occur. This information allows SC members to be prioritized into three groups: financially healthy companies of no immediate risk, companies with imminent risk of bankruptcy and companies with intermediate levels of risk that need monitoring.

The current paper proposes a new analytical approach to scanning and assessing the financial risk of SC members (suppliers or customers). Traditional models are able to predict if but not when a financial failure will occur. Lacking this information, it is impossible for SC managers to prioritize risk mitigation activities. A simple decision rule is developed to guide SC managers in setting these priorities.

The purpose of this paper is to propose that simple measures of linear association are unable to capture accurately the dependence between the survival of hedge funds and funds of funds, respectively. The paper then aims to advocate the use of copulas to model the joint survival of hedge funds and funds of funds managed by the same manager.

The paper uses both a one‐step approach where the margins and the copula parameters are estimated jointly, and a two‐step approach where the margins are fitted first and the copula parameter is estimated thereafter given the fixed margins. The margins are estimated non‐parametrically, semi‐parametrically, and parametrically, respectively.

First, the paper finds that Kendall's tau and Spearman's rho are anywhere between three and eight times larger than the corresponding sample based measures when various families of copulas are employed. Second, additional tests show that the two survival functions are strongly dependent, with the degree of nonlinear association increasing in the lower left quadrant.

This is the first paper to use copulas to model the joint survival of hedge funds and funds of funds. The results highlight the asymmetric dependence between hedge funds and funds of funds, which has implications for risk management practices.

The authors analyse the determinants of loan defaults in micro, small and medium enterprises (MSME) loans in India from the survival duration perspective to draw inferences that have implications for lenders and policymakers.

The authors use the Kaplan–Meier survivor function and the Cox Proportional Hazard model to analyse 4.29 lakhs MSME loan account data originated by a large bank having a national presence from 1st January 2016 to 31st December 2020.

The estimated Kaplan–Meier survival function by various categories of loan and socio-demographic characteristics reflects heterogeneity and identifies the trigger points for actions. The authors identify the key identified default drivers. The authors find that the subsidy amount is more effective at the lower level and its effectiveness diminishes significantly beyond an optimum level. The simulated values show that the effects of rising interest rates on survival rates vary across industries and types of loans.

The identified points of inflection in the default dynamics would help banks to initiate actions to prevent loan defaults. The default drivers identified would foster more nuanced lending decisions. The study estimation of the survival rate based on the simulated values of interest rate and subsidy provides insight for policymakers.

This study is the first to investigate default drivers in MSME loans in India using micro-data. The study findings will act as signposts for the planners to guide the direction of the interest rate to be charged by banks in MSME loans, interest subvention and tailoring subsidy levels to foster sustainable growth.

Studying the availability of medical equipment based on various maintenance types has been a major concern for hospitals. Most of the methodologies used are empirical and few are based on mathematical modelling. The objective of this paper is to present a mathematical maintenance model that analyses the effect of maintenance on the survival probability of medical equipment based on maintenance history and age of that equipment.

A global model is proposed to measure the probability of equipment being available using real data extracted from maintenance history of infusion pumps and ventilators and analysed using Matlab. To confirm the validity of the developed model, the survival analysis approach is used to develop a model that measures the survival of equipment as a function of maintenance and age of equipment. The method is first tested using simulated data and the findings confirm the validity of the proposed approach.

The analysis using survival approach reveals that conducting preventive maintenance (PM) on the selected medical equipment had an impact on survival of equipment. However, the manufacturer's recommended PM intervals do not correlate to the failure rate encountered. This will contribute to the debate on PM manufacturer's recommended intervals and might lead to the revision of maintenance strategies implemented by hospitals and clinical engineering (CE) practitioners.

Although the data collected for the infusion pumps were quite sufficient, that collected for the ventilators were more limited. The major difficulty is that of the availability of historical maintenance data and the effect of user errors may cause uncertainty in the analysis. A closer collaboration with the medical professional should facilitate the recording and access to such information.

The use of mathematical modelling to analyse the effect of maintenance on the survival of medical equipment is a beneficial tool that is not exploited in the medical equipment industry. It will provide CE practitioners with scientific tool to analyse the effect of PM on the survival of medical equipment.

This paper presents a mathematical approach to analyse the effect of maintenance on the survival of medical equipment, which is crucial in the assessment of maintenance strategies implemented in the medical equipment industry.

The purpose of this paper is to identify the critical components of a complex system by using survival signature. First, a complex system is abstracted with varying scales and generates a multi-levels model. Then reliability evaluations can be conducted by survival signature from rough to fine for tracing and identifying them. Finally, the feasibility of the proposed approach is demonstrated by an actual production system.

The paper mainly applies a multi-level evaluating strategy for the reliability analysis of complex systems with components of multiple types. In addition, a multi-levels model of a complex system is constructed and survival signature also used for evaluation.

The proposed approach was demonstrated to be the feasibility by an actual production system that is used in the case study.

The case study was performed on a system with simple network structure, but the proposed approach could be applied to systems with complex ones. However, the approach to generate the digraphs of abstraction levels for complex system has to be developed.

So far the approach has been used for the reliability analysis of a machining system. The approach that is proposed for the identification of critical components also can be applied to make maintenance decision.

The multi-level evaluating strategy that was proposed for reliability analysis and the identification of critical components of complex systems was a novel method, and it also can be applied as index to make maintenance planning.

The purpose of this paper is to discuss issues related to customer churn behavior in digital libraries (DLs) and demonstrate the successful application of Survival Analysis for understanding customer churn status and relationship duration distribution between customers and libraries.

The study applies non-parametric methods of Survival Analysis to analyze churn behaviors of 8,054 customers from a famous Chinese digital library, and a cluster method to make customer segmentation according to customer behavioral features.

The customer churn rate of the given library is very high, so as to the churn hazard in early three months after customer's registration on the web site of the library. There is clear difference in both customer survival time and churn hazard among customer groups. It is necessary to strengthen customer churn analysis and customer relationship management (CRM) for DLs.

The studied samples are mainly based on customers from one digital library and some hypotheses have not been strictly proven due to the absence of relevant empirical researches.

This study provides a reasonable basis for decision making about CRM in DLs.

Most previous researches about information behavior concentrate on information seeking behavior in DLs, seldom discuss customer switching behavior. The paper discusses issues related to customer churn analysis and illustrates the adaptation of Survival Analysis to understand customer churn status and relationship duration distribution in DLs.

Banks in India have started opening their branches in different areas to make sure that their customers get a high-touch experience and they see them as a premier brand. This could be ensured only if the banks show a stable physical presence in the market as well as provide the recent high-tech services to their customers as per the population group. The purpose of this study is to examine the survival rates of the commercial banks in India across the four population groups along with the differences that exist in their survival rates in all the population groups.

The analysis is based on the quarterly data of the number of functioning offices of the commercial banks in India as per the four population groups from March 2006 to December 2019. The survival is estimated using the Kaplan–Meier estimator.

From the analysis, it is revealed that survival of the banks changes as per the population group. In addition to this, it is found that the survival time of each category of the bank varies in each population group.

This study focuses only on the commercial banks of India; a similar research could be done for other categories of Indian banks. Also, the results would have been different if the variables such as the size of the bank, bank risk, etc. are included and studied. Moreover, this study is done using the Kaplan–Meier estimator, i.e. time-to-event. Further, an advance study could be done after considering the financial parameters of banks using the Cox’s regression model, which explores the relationship between various predictors and the time-to-event.

Due to the changes in the preferences of societies, the banks should also adopt different strategies to ensure that their products are understood and accepted by their customers. This will eventually increase the survival rate of the banks.

The work made in this study is completely new.

Over the past several years, growth in sponsorship spending has surpassed that of traditional marketing and promotional approaches, as it has become an indispensable part of the marketing mix. Yet, despite considerable advances in the application of analytics across the sport industry, sponsorship revenue forecasting still largely relies on a decades-old methodology. The paper aims to discuss this issue.

This research seeks to assist sport organizations by applying more advanced survival analysis methodologies to the study of shirt sponsorships of football clubs, utilizing more than 300 sponsorships of every team that has competed in the English Premier League (EPL) over the past 25 years.

The analysis of the lifetimes of shirt sponsorships provides several insights for those employed by European football clubs and tasked with managing these increasingly lucrative sponsorships. Notably, tests confirmed that survivor functions of EPL shirt sponsorships are significantly different than those that appeared solely in English Football League (EFL) Championship play. In addition, results found that the median lifetimes of shirt sponsorships of EPL clubs were more than one year longer, when compared to EFL clubs.

This research marks the first attempt in the literature to apply survival analysis methods to describe the lifetimes of European football shirt sponsorships. The results provide empirical evidence that the potential effects of promotion or relegation could have consequences for football clubs in the tens of millions of dollars, and illustrate the importance of providing those tasked with managing such partnerships with more advanced methodologies to assist in the organization’s sponsorship revenue forecasting activities.

The purpose of this paper is to investigate hazards for minor users while they are exposed to social networks. In particular, it provides the statistical relationship of these hazards with the exposure time as well as the amount of published personal information.

An experiment was conducted that has revealed a huge number of personal information exposed by users of social network applications. Moreover, a significant amount of suspicious activity against minors has been recorded. Experimental data led to the hypothesis that online hazards can be modeled with known statistical distributions. In order to examine this hypothesis, survival analysis techniques, which involve the estimation of certain functions that reflect the relation of a disastrous event with time, were applied.

The results show that the incoming hazards for minor female profiles follow the Logistic distribution, while the corresponding hazards for minor male profiles follow the Normal distribution.

The findings of this work are crucial for developing an effective system for automated grooming recognition in real time by optimizing the detection threshold as a function of time. Thus, the threshold sensitivity can be appropriately adjusted such that lower frequencies of occurrence lead to lower threshold sensitivities, and higher frequencies of occurrence lead to higher threshold sensitivities.