Variational Bayes survival analysis for unemployment modelling

  title={Variational Bayes survival analysis for unemployment modelling},
  author={Pavle Bo{\vs}koski and Matija Perne and Martina Ramesa and Biljana Mileva-Boshkoska},
  journal={Knowl. Based Syst.},

SurvSHAP(t): Time-dependent explanations of machine learning survival models

Experiments on synthetic and medical data show that SurvSHAP(t) can detect variables with a time-dependent effect, and its aggregation is a better determinant of the importance of variables for a prediction than SurvLIME.

Community analysis in Slovenian labour network 2010-2020

ABSTRACT There is little evidence on the right approach on how to delineate the sub-networks in a labour market. The subject of research in this paper is computational influence identification of the

Structuring the scattered literature on algorithmic profiling in the case of unemployment through a systematic literature review

PurposeThis article examines the overlooked literature on algorithmic profiling in public employment services (APPES) in the field of public administration. More specifically, it aims to provide an




Following the unemployment hysteresis of the 1980s, discussions of methods for reducing the natural rate of unemployment tend to focus on long-term unemployment (LTU). A broad consensus exists among

Semiparametric likelihood inference for left-truncated and right-censored data.

A new estimation procedure for the survival time distribution with left-truncated and right-censored data, where the distribution of the truncation time is known up to a finite-dimensional parameter vector.

Modeling Discrete Time-To-Event Data

This book covers well-established methods like life-table analysis and discrete hazard regression models, but also introduces state-of-the art techniques for model evaluation, nonparametric estimation and variable selection.

Statistical profiling in public employment services

An overview on profiling tools currently used throughout the OECD is presented, considerations for the development of such tools, and some insights into the latest developments such as using “click data” on job searches and advanced machine learning techniques are presented.

A Danish Profiling System

We describe the statistical model used for profiling new unemployed workers in Denmark. When a worker - during his or her first six months in unemployment - enters the employment office for the first

WTTE-RNN : Weibull Time To Event Recurrent Neural Network A model for sequential prediction of time-to-event in the case of discrete or continuous censored data, recurrent events or time-varying covariates

The WTTE-RNN is described using a general framework for censored data which can easily be extended with other distributions and adapted for multivariate prediction and is found to have many advantages and comparable performance to binary fixed-window RNNs without the need to specify window size and the ability to train on more data.

Statistical Profiling of the Unemployed

Abstract Labour market policy in Canada has undergone profound reforms over the past several decades. Successive federal and provincial governments have sought to “activate” the unemployed through

Is Unemployment Really Scarring? Effects of Unemployment Experiences on Wages

This paper looks at the effects of unemployment on re-employment wage for men using the first seven waves of the British Household Panel Survey (BHPS) conducted over the period 1991- 1997. In