Semiparametric Multivariate Accelerated Failure Time Model with Generalized Estimating Equations

The semiparametric accelerated failure time model is not as widely used as the Cox relative risk model mainly due to computational difficulties. Recent developments in least squares estimation and induced smoothing estimating equations provide promising tools to make the accelerate failure time models more attractive in practice. For semiparametric multivariate accelerated failure time models, we propose a generalized estimating equation approach to account for the multivariate dependence through working correlation structures. The marginal error distributions can be either identical as in sequential event settings or different as in parallel event settings. Some regression coefficients can be shared across margins as needed. The initial estimator is a rank-based estimator with Gehan's weight, but obtained from an induced smoothing approach with computation ease. The resulting estimator is consistent and asymptotically normal, with a variance estimated through a multiplier resampling method. In a simulation study, our estimator was up to three times as efficient as the initial estimator, especially with stronger multivariate dependence and heavier censoring percentage. Two real examples demonstrate the utility of the proposed method

Identification of a competing risks model with unknown transformations of latent failure times

This paper is concerned with identification of a competing risks model with unknown transformations of latent failure times. The model in this paper includes, as special cases, competing risks versions of proportional hazards, mixed proportional hazards, and accelerated failure time models. It is shown that covariate effects on latent failure times, cause-specific link functions, and the joint survivor function of the disturbance terms can be identified without relying on modelling the dependence between latent failure times parametrically nor using an exclusion restriction among covariates. As a result, the paper provides an identification result on the joint survivor function of the latent failure times conditional on covariates

Identification of a competing risks model with unknown transformations of latent failure times

This paper is concerned with identification of a competing risks model with unknown transformations of latent failure times. The model in this paper includes, as special cases, competing risks versions of proportional hazards, mixed proportional hazards, and accelerated failure time models. It is shown that covariate effects on latent failure times, cause-specific link functions, and the joint survivor function of the disturbance terms can be identified without relying on modelling the dependence between latent failure times parametrically nor using an exclusion restriction among covariates. As a result, the paper provides an identification result on the joint survivor function of the latent failure times conditional on covariates

Modeling dynamic effects of promotion on interpurchase times

In this paper we put forward a duration model to analyze the dynamic effects of marketing-mix variables on interpurchase times. We extend the accelerated failure-time model with an autoregressive structure. An important feature of our model is that it allows for different long-run and short-run effects of marketing-mix variables on interpurchase times. As marketing efforts usually change during the spells, we explicitly deal with time-varying covariates. Our empirical analysis of purchases in three different categories reveals that, for some segments of households, the short-run effects of marketing-mix variables are significantly different from the long-run effects.Dynamic duration model;Error-correction model;Time-varying covariates;Unobserved heterogeneity