This article presents a novel algorithm that efficiently computes L(1) penalized (lasso) estimates of parameters in high-dimensional models. The lasso has the property that it simultaneously performs variable selection and shrinkage, which makes it very useful for finding interpretable prediction rules in high-dimensional data. The new algorithm is based on a combination of gradient ascent opti...

Currently, for a variety of mechatronic systems and components, sufficient failure behaviour data are not available. Endurance tests at customer-specific operating conditions provide manufacturers with specific failure time data. However, they are timeconsuming and expensive. Findings gained through experiments are valid only for the applied test conditions and loads. On the other hand, develop...

results survival probabilities at different times were determined using the cox proportional hazards and a neural network with three nodes in the hidden layer; the ratios of standard errors with these two methods to the kaplan-meier method were 1.1593 and 1.0071, respectively, revealed a significant difference between cox and kaplan-meier (p < 0.05) and no significant difference between cox and...

The Cox proportional-hazards regression model has achieved widespread use in the analysis of time-to-event data with censoring and covariates. The covariates may change their values over time. This article discusses the use of such time-dependent covariates, which offer additional opportunities but must be used with caution. The interrelationships between the outcome and variable over time can ...

Some failure time data come from a population that consists of some subjects who are susceptible to and others who are nonsusceptible to the event of interest. The data typically have heavy censoring at the end of the follow-up period, and a standard survival analysis would not always be appropriate. In such situations where there is good scientific or empirical evidence of a nonsusceptible pop...

The Cox proportional hazards regression model has been widely used in the analysis of survival/duration data. It is semiparametric because the model includes a baseline hazard function that is completely unspecified. We study here the statistical inference of the Cox model where some information about the baseline hazard function is available, but it still remains as an infinite dimensional nui...