Survival analysis as a tool for company failure prediction

Luoma, Mikko

doi:10.1016/0305-0483(91)90015-l

Cited by 134 publications

(81 citation statements)

References 15 publications

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“…discriminant analysis, logit and probit regressions, survival analysis) (for further details see Balcaen and Ooghe (2006), Gepp and Kumar (2012)). When survival analysis is applied in this context (see for example Gepp and Kumar (2008), Luoma and Laitinen (1991)), right censoring and truncation have been considered in only few papers (Bhattacharjee et al 2009) in the univariate case.…”

Section: Discussionmentioning

confidence: 99%

A class of nonparametric bivariate survival function estimators for randomly censored and truncated data

Dai

Restaino

Wang

2016

Journal of Nonparametric Statistics

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This paper proposes a class of nonparametric estimators for the bivariate survival function estimation under both random truncation and random censoring. In practice, the pair of random variables under consideration may have certain parametric relationship. The proposed class of nonparametric estimators uses such parametric information via a data transformation approach and thus provides more accurate estimates than existing methods without using such information. The large sample properties of the new class of estimators and a general guidance of how to find a good data transformation are given. The proposed method is also justified via a simulation study and an application on an economic data set.

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Section: Discussionmentioning

confidence: 99%

A class of nonparametric bivariate survival function estimators for randomly censored and truncated data

Dai

Restaino

Wang

2016

Journal of Nonparametric Statistics

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“…The survival analysis comes from the medical research (as the name indicated) but is also used in other studies [13], [14], [15], [16]. Characteristic for this analysis are the so-called censored data.…”

Section: The Comparison Of the Analysis Of Survival For Simulated Supmentioning

confidence: 99%

THE COMPLEXITY OF SUPPLY CHAIN vs. SURVIVAL OF THIS SUPPLY CHAIN

Hoffa-Dąbrowska¹,

Grzybowska²

2018

dtetr

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The article presents issue of survival of simulated supply chains depends on the complexity of this chain. Authors analyzed the supply chain with mechanism of coordination of actions. In this article, authors decided to compare three supply chain on a different levels of complexity. For each of this supply chain the simulation model was built, and then the analysis of survival was made. In every model authors analyzed the decision part and transportation part. The first model is the basic simulation model with using the Electronic Bulletin Board, in which one contractor was considered. In second model authors extended the numbers of entities (contractors) in analyzed supply chain to four. In third model the transportation part was extended to be more complexitydifferent contractors can realized the transportation tasks. For each case authors focus on aspects of durability of supply chain as non-renewable systems.

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“…Portioning, while Gupta et al [20] use mathematical programming as an alternative to multivariate discriminate analysis for system failure prediction problem. Other methods used were survival analysis by Luoma, Laitinenl [21] which is a tool for company failure prediction, expert systems by Messier and Hansen [22] , neural network by Altman.…”

Section: Introductionmentioning

confidence: 99%