This paper presents a new class of time-deformation (or stochastic volatility) models for stock returns sampled in transaction time and directed by a generalized duration process. Stochastic volatility in this model is driven by an observed duration process and a latent autoregressive process....

A class of autoregressive moving-average (ARMA) models proposed by Jørgensen and Song [Journal of Applied Probability (1998), vol. 35, pp. 78-92] with exponential dispersion model margins are useful to deal with non-normal stationary time series with high-order autocorrelation. One property...

This article proposes stochastic conditional duration (SCD) models with "leverage effect" for financial transaction data, which extends both the autoregressive conditional duration (ACD) model (Engle and Russell, 1998, Econometrica, 66, 1127--1162) and the existing SCD model (Bauwens and...

In longitudinal data analysis with dropouts, despite its local efficiency in theory, the augmented inverse probability weighted (AIPW) estimator hardly achieves the semiparametric efficiency bound in practice, even if the variance–covariance of the longitudinal outcomes is correctly modeled....

This paper concerns the analysis of discrete-valued time series using a class of categorical ARMA models recently proposed by Biswas and Song (2009). Such ARMA processes are flexible to model discrete-valued time series, allowing a wide range of marginal distributions such as binomial,...

The additive hazard regression (AHR) model is known for its convenience in interpretation, as hazard is modeled as a linear function of covariates. One outstanding issue in the application of such a model in the analysis of current status data is that there lacks an efficient and computationally...