Abstract
In survival analysis, nonparametric and parametric models are commonly used to estimate survival functions. An important limitation of nonparametric methods is that in the construction of survival function estimators, variables which can affect the duration of the period an individual remains in a given state are not taken into account. The use of parametric methods also has some limitations. First of all, problems with finding the analytical form of the distribution curve of survival times take place. The second limitation is the fact that time-dependent covariates cannot be included in a model. These restrictions do not apply to the Cox model considered in this study. In most studies, Cox models are used to evaluate the effects of explanatory variables on a hazard rate. The purpose of this paper is to indicate the possibility of using Cox regression model to determine direct adjusted probabilities of finding a job by the unemployed depending on their individual characteristics in the context of long-term unemployment risk. The study is based on LFS data from 2017 to 2018 for Poland.
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Grzenda, W. (2021). Direct Adjusted Survival Probabilities in the Analysis of Finding a Job by the Unemployed Depending on Their Individual Characteristics. In: Jajuga, K., Najman, K., Walesiak, M. (eds) Data Analysis and Classification. SKAD 2020. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-030-75190-6_14
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