The contribution deals with the application of statistical survival analysis with the intensity described by a generalized version of Cox regression model with time dependent parameters. A\nmethod of model components non-parametric estimation is recalled, the flexibility of result is assessed with a goodness-of-fit test based on martingale residuals. The application\nconcerns to the real data representing the job opportunities development and reduction, during a given period. The risk of leaving the company is changing in time and depends also on the age of employees and their time with company. Both these covariates are considered and their impact to the risk analyzed
The Cox proportional-hazards regression model has achieved widespread use in the analysis of time-to...
Cox proportional hazards model assumes that independent variables remain constant throughout the ob...
Survival analysis is one of the statistical methods used to analyze data relating to time, starting...
The present contribution deals with models and statistical analysis of sequences of random events. T...
The most widely used model in multivariate analysis of survival data is proportional hazards model ...
Survival analysis, Cox regression model, Unemployment duration, C14, C24, J60, J64,
This vignette covers 3 different but interrelated concepts: An introduction to time dependent covar...
Survival models for life-time data and other time-to-event data are widely used in many fields, incl...
The Cox regression model is a cornerstone of modern survival analysis and is widely used in many oth...
This paper applies the semi-parametric Cox regression approach to model unemployment duration in fiv...
The longitudinal studies has increased the importance of statistical methods for time-to event data ...
Time-varying covariance occurs when a covariate changes over time during the follow-up period. Such ...
Statistical modeling of lifetime data, or survival analysis, is studied in many fields, including me...
The Cox regression model ~s a standard tool m survival analysis for studying the dependence of a haz...
Survival analysis examines and models the time it takes for events to occur, termed survival time. T...
The Cox proportional-hazards regression model has achieved widespread use in the analysis of time-to...
Cox proportional hazards model assumes that independent variables remain constant throughout the ob...
Survival analysis is one of the statistical methods used to analyze data relating to time, starting...
The present contribution deals with models and statistical analysis of sequences of random events. T...
The most widely used model in multivariate analysis of survival data is proportional hazards model ...
Survival analysis, Cox regression model, Unemployment duration, C14, C24, J60, J64,
This vignette covers 3 different but interrelated concepts: An introduction to time dependent covar...
Survival models for life-time data and other time-to-event data are widely used in many fields, incl...
The Cox regression model is a cornerstone of modern survival analysis and is widely used in many oth...
This paper applies the semi-parametric Cox regression approach to model unemployment duration in fiv...
The longitudinal studies has increased the importance of statistical methods for time-to event data ...
Time-varying covariance occurs when a covariate changes over time during the follow-up period. Such ...
Statistical modeling of lifetime data, or survival analysis, is studied in many fields, including me...
The Cox regression model ~s a standard tool m survival analysis for studying the dependence of a haz...
Survival analysis examines and models the time it takes for events to occur, termed survival time. T...
The Cox proportional-hazards regression model has achieved widespread use in the analysis of time-to...
Cox proportional hazards model assumes that independent variables remain constant throughout the ob...
Survival analysis is one of the statistical methods used to analyze data relating to time, starting...