Relatively there is little empirical research that has been taken to understand how the underlying economy affects customers’ subsequent financial product purchase behaviours. A better understanding of this influence and being able to predict the probability of purchasing are important for financial service industries. This paper undertakes an examination of the impacts of social-demographic an...

The Cox proportional hazards model has become the standard in biomedical studies, particularly for settings in which the estimation covariate effects (as opposed to prediction) is the primary objective. In spite of the obvious flexibility of this approach and its wide applicability, the model is not usually chosen for its fit to the data, but by convention and for reasons of convenience. It is ...

In this paper, we develop a Bayesian approach to estimate a Cox proportional hazards model that allows a threshold in the regression coefficient, when some fraction of subjects are not susceptible to the event of interest. A data augmentation scheme with latent binary cure indicators is adopted to simplify the Markov chain Monte Carlo implementation. Given the binary cure indicators, the Cox cu...

The research on biomarkers has been limited in its effectiveness because biomarker levels can only be measured within the thresholds of assays and laboratory instruments, a challenge referred to as a detection limit (DL) problem. In this paper, we propose a Bayesian approach to the Cox proportional hazards model with explanatory variables subject to lower, upper, or interval DLs. We demonstrate...

The Cox proportional hazards model to explore the effect of explanatory variables on survival is by far the most popular and powerful statistical technique. It is used throughout a wide variety of types of clinical studies. However, special techniques are required when multiple interaction terms are introduced into the Cox model. This paper provides an in-depth analysis, with some explanation o...

Proportional hazards regression model assumes that the covariates affect the hazard function through a link function and an index which is a linear function of the covariates. Traditional approaches, such as the Cox proportional hazards model, focus on estimating the unknown index by assuming a known link function between the log-hazard function and covariates. A linear link function is often e...

Since the pioneering work of Thomas and Grunkemeier (1975) and Owen (1988), the empirical likelihood has been developed as a powerful nonparametric inference approach and become popular in statistical literature. There are many applications of empirical likelihood in survival analysis. In this paper, we present an overview of some recent developments of the empirical likelihood for survival dat...

This paper derives a formula to calculate the number of deaths required for a proportional hazards regression model with a nonbinary covariate. The method does not require assumptions about the distributions of survival time and predictor variables other than proportional hazards. Simulations show that the censored observations do not contribute to the power of the test in the proportional haza...