1,720,969 research outputs found
Regression Models for Lifetime Data: An Overview
No full textTwo methods dominate the regression analysis of time-to-event data: the accelerated failure time model and the proportional hazards model. Broadly speaking, these predominate in reliability modelling and biomedical applications, respectively. However, many other methods have been proposed, including proportional odds, proportional mean residual life and several other “proportional” models. This paper presents an overview of the field and the concept behind each of these ideas. Multi-parameter modelling is also discussed, in which (in contrast to, say, the proportional hazards model) more than one parameter of the lifetime distribution may depend on covariates. This includes first hitting time (or threshold) regression based on an underlying latent stochastic process. Many of the methods that have been proposed have seen little or no practical use. Lack of user-friendly software is certainly a factor in this. Diagnostic methods are also lacking for most methods
Modeling the Reliability of Ball Bearings
No full textThe data, created by Chrys Caroni of the National Technical University of Athens, presented in this article refer to the reliability of ball bearings in manufacturing. The data was originally published by "Lieblein and Zelen" and contains 210 observations. Rather than exploring the data to obtain a multiple linear regression solution, a theoretically derived equation is given and the data is used to test it. Some of the key concepts includes: failure times, percentiles and weighted least squares
Analysing the Risk of Bankruptcy of Firms: Survival Analysis, Competing Risks and Multistate Models
No full textBankruptcy prediction by survival models based on current and lagged values of time-varying financial data
No full textPeriods of economic crisis arouse interest in exploring the causes of firms closure, for preventive and predictive purposes. Failure prediction models are useful tools for bankers to measure the risk of lending and minimise losses, for firms wishing to evaluate their market position, and, also for investors, asset managers and rating agencies. Quantitative methods to assess the performance of firms and to predict the bankruptcyevent based on balance sheet indicators are widely used in the credit risk context. Logistic regression and survival analysis techniques based on hazard models are among the methods often employed. AlargedatasetoncapitalcompaniesinItalyfrom2008to2013,including Business Registerdata supplying a complete picture of their legal situation, was used to develop survival models. Training (n = 27286) and holdout (n = 7124) samples were constructed for developing and testing models, respectively. Fixed and time-varying covariates were taken into account and macro-economic variables were included besides the firms individual financial indicators. Furthermore, we considered one- and two-year lagged values of each time-varying covariate. ROC curves that vary as a function of time and AUC up to a given time were used to compare models and obtain global concordance measure
Bankruptcy prediction by survival models based on current and lagged values of time-varying financial data
No full textRobust Detection of Multiple Outliers in Grouped Multivariate Data
No full textMany methods have been developed for detecting multiple outliers in a single multivariate sample, but very few for the case where there may be groups in the data. We propose a method of simultaneously determining groups (as in cluster analysis) and detecting outliers, which are points that are distant from every group. Our method is an adaptation of the BACON algorithm proposed by Billor, Hadi and Velleman for the robust detection of multiple outliers in a single group of multivariate data. There are two versions of our method, depending on whether or not the groups can be assumed to have equal covariance matrices. The effectiveness of the method is illustrated by its application to two real data sets and further shown by a simulation study for different sample sizes and dimensions for 2 and 3 groups, with and without planted outliers in the data. When the number of groups is not known in advance, the algorithm could be used as a robust method of cluster analysis, by running it for various numbers of groups and choosing the best solution.Multivariate data, outliers, robust methods, BACON, cluster analysis,
Testing for the Marshall–Olkin extended form of the Weibull distribution
No full textWeibull distribution, Marshall–Olkin extension, Proportional odds, Likelihood,
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