The Cox proportional hazards (PH) model with time-dependent covariates (referred to as the extended PH model) has been widely used in medical and health related studies to investigate the effects of time-varying risk factors on survival. Theories and practices regarding model estimation and...

Mathematical modeling of plant growth has gained increasing interest in recent years due to its potential applications. A general family of models, known as functional–structural plant models (FSPMs) and formalized as dynamic systems, serves as the basis for the current study. Modeling,...

Nonparametric comparison for panel count data is discussed. For the situation, most available approaches require that all subjects have the same observation process. However, such an assumption may not hold in reality. To address this, a new class of test procedures are proposed that allow...

A powerful and flexible method for fitting dynamic models to missing and censored data is to use the Bayesian paradigm via data-augmented Markov chain Monte Carlo (DA-MCMC). This samples from the joint posterior for the parameters and missing data, but requires high memory overheads for...

Variable selection techniques for the classical linear regression model have been widely investigated. Variable selection in fully nonparametric and additive regression models has been studied more recently. A Bayesian approach for nonparametric additive regression models is considered, where...

Given a finite population consisting of N elements, it is desired to obtain confidence intervals for (t/N)th quantile x(t) of the population based on the randomized nomination sampling (RNS) design. Three without replacement sampling protocols are described and procedures for constructing...

A robust estimation procedure for mixture linear regression models is proposed by assuming that the error terms follow a Laplace distribution. Using the fact that the Laplace distribution can be written as a scale mixture of a normal and a latent distribution, this procedure is implemented by an...

Extended stochastic volatility models are studied which use the daily returns as well as the volatility information in intraday price data summarised in terms of a number of realised measures. These extended models treat the logarithm of daily volatility as a latent process with autoregressive...

In non-parametric regression analysis the advantage of frames with respect to classical orthonormal bases is that they can furnish an efficient representation of a more broad class of functions. For example, fast oscillating functions as audio, speech, sonar, radar, EEG and stock market are much...

A semiparametric regression model of a q-dimensional multivariate response y on a p-dimensional covariate x is considered. A new approach is proposed based on sliced inverse regression (SIR) for estimating the effective dimension reduction (EDR) space without requiring a prespecified parametric...