Statistical models with unobservable random variables such as random-effect models have
been recently studied for analyzing data of complex types (eg longitudinal and time-to-event …

Survival or time-to-event data arise in various research areas such as medicine,
epidemiology, genetics, engineering, econometrics, and sociology. Survival data have …

This book provides a groundbreaking introduction to the likelihood inference for correlated
survival data via the hierarchical (or h-) likelihood in order to obtain the (marginal) likelihood …

For inferences from random-effect models Lee and Nelder (1996) proposed to use
hierarchical likelihood (h-likelihood). It allows inference from models that may include both …

Since the pioneer work of Thomas & Grunkemeier (1975) and Owen (1988), empirical
likelihood has been developed as a powerful nonparametric inference approach and …

Frailty models are useful for measuring unobserved heterogeneity in risk of failures across
clusters, providing cluster-specific risk prediction. In a frailty model, the latent frailties shared …

Mixed linear models describe the dependence via random effects in multivariate normal
survival data. Recently they have received considerable attention in the biomedical …

Fisher's classical likelihood has become the standard procedure to make inference for fixed
unknown parameters. Recently, inferences of unobservable random variables, such as …

Empirical Likelihood Method in Survival Analysis explains how to use the empirical
likelihood method for right censored survival data. The author uses R for calculating …

There has been much recent work on Bayesian approaches to survival analysis,
incorporating features such as flexible baseline hazards, time-dependent covariate effects …