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The software reliability models to describe the reliability growth phenomenon are formulated by any stochastic point process with state‐dependent or time‐dependent intensity function. On the other hand, to deal with the environmental data, which consists of covariates influencing times to software failure, it may be useful to apply the Cox’s proportional hazards model for assessing the software reliability. In this paper, we review the proportional hazards software reliability models and discuss the problem to determine the optimal software release time under the expected total software cost criterion. Numerical examples are devoted to examine the dependence of the covariate structure in both the software reliability prediction and the optimal software release decision.

To a customer, higher quality is synonymous to higher expected life. Therefore, the purpose of this paper is to determine the existing life of the competing brands in a product field and suggest an improvement plan, under cost constraints, so that all the brands can be placed on a comparable scale.

For this, we consider Cox proportional hazard model for estimation of the mean life and suggest an optimization procedure for improving mean life under cost constraint. As the cost of redesigning the product is mostly known, the authors propose to take corresponding repairing cost as their surrogates and optimize the expected life for each brand subject to a fixed level of cost.

From Cox's model one can identify the causes of failure for the brands under consideration. Further, under the optimization techniques proposed herein one can order the brands for comparison purpose.

We have applied the proposed optimization techniques for ordering mobile handsets. In fact, based on the result obtained by our proposed method, the design engineers or the brand planners can take necessary actions to increase the product life, correct product design and improve the product performance.

The cost minimization approach under Cox's cause‐wise setup can provide a tool for comparing different brands of different prices and order them to know the best performer.

The purpose of this research is to undertake an examination of the impacts of socio‐demographic and economic variables on the probability of purchasing financial products. There is relatively little empirical research that has been taken to understand how the underlying economy affects customers' subsequent financial product purchase behaviours. Understanding this influence would improve prediction of when purchases will occur and hence is important for the Customer lifetime value models of financial service organisations.

Two proportional hazard modelling approaches – Cox and Weibull – are compared in terms of predictive ability on a data set from a major insurance company. The risk factors for purchase are both economic and socio‐demographic.

The results show that the external economic environment is an extremely important influence in driving customers' financial products purchasing behaviours. Furthermore, the results also indicate that Cox's proportional hazard models are superior to Weibull proportional hazard models in this case because of an annual purchase effect.

Financial organisations need to consider the current economic conditions before determining how much marketing effort to undertake.

The originality of this paper is that it considers economic conditions and socio‐demographic variables in modelling the long run purchase behaviour of customers for insurance and savings products. It has a large data set from a major insurance company. It is also one of the first papers to make a detailed comparison between the semi‐parametric and parametric proportional hazard models in the bank marketing area.

Reviews previous research on predicting financial distress and the effects of US Chapter 11 bankruptcy (C11B); and explains how survival analysis and Cox’s (1972) proportional hazards model can be used to estimate the financial outcome for the shareholders of C11B. Reduces a previous data set (Russel et al 1999) of 154 companies entering C11B between 1984 and 1993 to 59 (54 of which gave no value to shareholders) and estimates two models to predict this: one based on firm‐specific covariates only and the other adding market‐wide covariates. Explains the methodology, presents the results and uses receiver operating characteristic curves to compare the predictive accuracy of the two. Finds little difference between the and suggests using the simpler model. Briefly summarizes the variables which are most useful in predicting the value outcomes of C11B for shareholders and recognizes the limitations of the study.

Research in the area of financial distress often uses a proportional hazards model to determine the influence of covariates on the duration of time that precedes financial distress. Acritical issue in the use of a proportional hazards model is the use of time‐invariant and time‐dependent covariates. Time‐invariant covariates remain fixed while time‐dependent covariates change during the estimation of the model. Although the choice of covariates might substantially affect the estimation of the proportional hazards model, existing literature often fails to consider the potential effect of this choice on model estimation. This paper reviews the distinction between time‐invariant and time‐dependent covariates and the effect of covariate selection on the estimation of a proportional hazards model. Using a sample of financially distressed and non‐financially distressed firms, this paper suggests the choice of time dependence substantially influences model estimation and that covariate selection should be given more serious consideration in financial distress research.

Survival (default) data are frequently encountered in financial (especially credit risk), medical, educational, and other fields, where the “default” can be interpreted as the failure to fulfill debt payments of a specific company or the death of a patient in a medical study or the inability to pass some educational tests.

This paper introduces the basic ideas of Cox's original proportional model for the hazard rates and extends the model within a general framework of statistical data mining procedures. By employing regularization, basis expansion, boosting, bagging, Markov chain Monte Carlo (MCMC) and many other tools, we effectively calibrate a large and flexible class of proportional hazard models.

The proposed methods have important applications in the setting of credit risk. For example, the model for the default correlation through regularization can be used to price credit basket products, and the frailty factor models can explain the contagion effects in the defaults of multiple firms in the credit market.

The objective of this paper is to determine whether international entry‐order strategies by Korean chaebols affect the exit of their foreign subsidiaries.

The sample consists of a set of 61 parent firms and their 500 foreign subsidiaries. The sample includes 27 Korean business groups, called chaebols, and spans 51 markets, during the period from 1999 to 2004. The study employs resource‐ and knowledge‐based views, and is based on the Cox's proportional hazard model.

This study leads to two main findings: in the context of Korean business groups, latecomers in international markets have greater survival rates than pioneers do because latecomers have stronger resource commitments; and, nonetheless, if chaebol pioneers have greater competitive advantages than chaebol latecomers, the pioneers' subsidiaries have better survival rates than do those of latecomers.

The analysis advances order‐of‐entry research by exploring the international order‐of‐entry strategies of chaebol multinationals and their impact on international exit and the interrelationship between the order‐of‐entry and core competencies of chaebol multinationals.

Cox’s proportional hazards model (PHM) has been widely applied to the analysis of lifetime data. It involves covariates influencing the failure of products. Regarding the covariates as discrete random variables, the probability model is reduced to a mixture of PHM. This article presents a statistical procedure to estimate model parameters in the mixture of PHM. The estimation procedure is developed in a parametric framework when not only complete sets of field data but also incomplete ones are given. The expectation‐maximization algorithm is employed to handle the incomplete data problem. Simulation results are presented to illustrate the accuracy and some properties of the estimation results.

The purpose of this paper is to propose a novel condition-based maintenance (CBM) policy with two sampling intervals for a system subject to stochastic deterioration described by the Cox’s proportional hazards model (PHM).

In this paper, the new or renewed system is monitored using a longer sampling interval. When the estimated hazard function of the system exceeds a warning limit, the observations are taken more frequently, i.e., the sampling interval changes to a shorter one. Preventive maintenance is performed when either the hazard function exceeds a maintenance threshold or the system age exceeds a pre-determined age. A more expensive corrective maintenance is performed upon system failure. The proposed model is formulated in the semi-Markov decision process (SMDP) framework.

The optimal maintenance policy is found and a computational algorithm based on policy iteration for SMDP is developed to obtain the control thresholds as well as the sampling intervals minimizing the long-run expected average cost per unit time.

A numerical example is presented to illustrate the whole procedure. The newly proposed maintenance policy with two sampling intervals outperforms previously developed maintenance policies using PHM. The paper compares the proposed model with a single sampling interval CBM model and well-known age-based model. Formulas for the conditional reliability function and the mean residual life are also derived for the proposed model. Sensitivity analysis has been performed to study the effect of the changes in the Weibull parameters on the average cost.

The results show that considerable cost savings can be obtained by implementing the maintenance policy developed in this paper.

Unlike the previous CBM policies widely discussed in the literature which use sequential or periodic monitoring, the authors propose a new sampling strategy based on two sampling intervals. From the economic point of view, when the sampling is costly, it is advantageous to monitor the system less frequently when it is in a healthy state and more frequently when it deteriorates and enters the unhealthy state.

This paper aims to investigate the divestment behavior of emerging market multinationals from Latin America – multilatinas – by examining how their foreign market entry decision impacts the likelihood of subsidiary divestment.

The hypotheses are tested using Cox’s proportional hazard rate model in a longitudinal database of Brazilian multinational companies established in 43 countries.

Results indicate that these subsidiaries can thrive in environments that bear similarities to their home country, being less likely to divest in institutionally weak countries. Contrary to developed country multinationals, these firms benefit from foreign entry decisions that entail handling partnerships abroad; thus, wholly-owned greenfield (WOGF) investments have a higher likelihood of being divested.

To the best of the authors’ knowledge, this paper is the first to analyze foreign divestment from multilatinas, accounting for how entry mode strategy and host country institutions may impact these firms’ de-internationalization.