𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Semiparametric inference for survival models with step process covariates

✍ Scribed by Timothy Hanson; Wesley Johnson; Purushottam Laud

Publisher
John Wiley and Sons
Year
2009
Tongue
French
Weight
222 KB
Volume
37
Category
Article
ISSN
0319-5724

No coin nor oath required. For personal study only.

πŸ“œ SIMILAR VOLUMES

Semiparametric Bayesian inference for dy
Semiparametric Bayesian inference for dynamic Tobit panel data models with unobserved heterogeneity
✍ Tong Li; Xiaoyong Zheng πŸ“‚ Article πŸ“… 2008 πŸ› John Wiley and Sons 🌐 English βš– 242 KB

## Abstract This paper develops semiparametric Bayesian methods for inference of dynamic Tobit panel data models. Our approach requires that the conditional mean dependence of the unobserved heterogeneity on the initial conditions and the strictly exogenous variables be specified. Important quantit

Mixture models for cancer survival analy
Mixture models for cancer survival analysis: application to population-based data with covariates
✍ R. De Angelis; R. Capocaccia; T. Hakulinen; B. Soderman; A. Verdecchia πŸ“‚ Article πŸ“… 1999 πŸ› John Wiley and Sons 🌐 English βš– 140 KB πŸ‘ 2 views

The interest in estimating the probability of cure has been increasing in cancer survival analysis as the curability of many cancer diseases is becoming a reality. Mixture survival models provide a way of modelling time to death when cure is possible, simultaneously estimating death hazard of fatal

On inference for a semiparametric partia
On inference for a semiparametric partially linear regression model with serially correlated errors
✍ Jinhong You; Gemai Chen πŸ“‚ Article πŸ“… 2007 πŸ› John Wiley and Sons 🌐 French βš– 980 KB

## Abstract The authors consider a semiparametric partially linear regression model with serially correlated errors. They propose a new way of estimating the error structure which has the advantage that it does not involve any nonparametric estimation. This allows them to develop an inference proce

Inference for regression models with err
Inference for regression models with errors from a non-invertible MA(1) process
✍ Mei-Ching Chen; Richard A. Davis; Li Song πŸ“‚ Article πŸ“… 2010 πŸ› John Wiley and Sons 🌐 English βš– 312 KB

## Abstract This paper considers maximum likelihood estimation in a regression model when the errors follow a first‐order moving average model which is non‐invertible or nearly non‐invertible. The latter corresponds to a moving average parameter ΞΈ that is equal to or close to 1. The joint limiting