A bias analysis of Weibull models under heaped data

Retrospectively collected duration data are often reported incorrectly. An important type of such an error is heaping—respondents tend to round-off or round-up the data according to some rule of thumb. For two special cases of the Weibull model we study the behaviour of the ‘naive estimators’, which simply ignore the measurement error due to heaping, and derive closed expressions for the asymptotic bias. These results give a formal justification of empirical evidence and simulation-based findings reported in the literature. Additionally, situations where a remarkable bias has to be expected can be identified, and an exact bias correction can be performed.     

Markov random-field models for estimating local labour markets

Summary. This work is motivated by data on daily travel-to-work flows observed between pairs of elemental territorial units of an Italian region. The data were collected during the 1991 population census. The aim of the analysis is to partition the region into local labour markets. We present a new method for this which is inspired by the Bayesian texture segmentation approach. We introduce a novel Markov random-field model for the distribution of the variables that label the local labour markets for each territorial unit. Inference is performed by means of Markov chain Monte Carlo methods. The issue of model hyperparameter estimation is also addressed. We compare the results with those obtained by applying a classical method. The methodology can be applied with minor modifications to other data sets.     

Graphical models for skew-normal variates

This paper explores the usefulness of the multivariate skew-normal distribution in the context of graphical models. A slight extension of the family recently discussed by Azzalini & Dalla Valle (1996 ) and Azzalini & Capitanio (1999 ) is described, the main motivation being the additional property of closure under conditioning. After considerations of the main probabilistic features, the focus of the paper is on the construction of conditional independence graphs for skew-normal variables. Necessary and sufficient conditions for conditional independence are stated, and the admissible structures of a graph under restriction on univariate marginal distribution are studied. Finally, parameter estimation is considered. It is shown how the factorization of the likelihood function according to a graph can be rearranged in order to obtain a parameter based factorization.     

On diagnostic checking of the autoregressive conditional intensity model

The autoregressive conditional intensity model proposed by Russell (1998) is a promising option for fitting multivariate high frequency irregularly spaced data. The authors acknowledge the validity of this model by showing the independence of its generalized residuals, a crucial assumption of the model formulation not readily recognized by researchers. The authors derive the large‐sample distribution of the autocorrelations of the generalized residual series and use it to construct a goodness‐of‐fit test for the model. Empirical results compare the performance of their test with other off‐the‐shelf tests such as the Ljung–Box test. They illustrate the use of their test with transaction records of the HSBC stock.     

Semiparametric Efficient Estimation of the Mean of a Time Series in the Presence of Conditional Heterogeneity of Unknown Form

We obtain semiparametric efficiency bounds for estimation of a location parameter in a time series model where the innovations are stationary and ergodic conditionally symmetric martingale differences but otherwise possess general dependence and distributions of unknown form. We then describe an iterative estimator that achieves this bound when the conditional density functions of the sample are known. Finally, we develop a “semi-adaptive” estimator that achieves the bound when these densities are unknown by the investigator. This estimator employs nonparametric kernel estimates of the densities. Monte Carlo results are reported.     

Are there causal relationships between the dimensions of the Maslach Burnout Inventory? A review and two longitudinal tests

Arising from interest concerning the possibility of causal relationships among the three components of the Maslach Burnout Inventory, several process models have been proposed for the development of burnout. The present paper first reviews the evidence in favour of the three most influential of these (Leiter and Maslach's model (1988); Golembiewski, Boudreau, Munzenrider, & Luo's (1996) phase model; and Lee and Ashforth's model (1993)). These three models, and our own model (which integrates of two of them, and includes feedback effects of depersonalization on emotional exhaustion) are then compared with each other using structural equation modelling, drawing on longitudinal data from two Dutch samples (total N=1185). The review revealed that none of the seven previous studies on this issue provided any convincing support for any particular causal order proposed so far. In contrast, our own study showed that high levels of exhaustion were associated with high levels of depersonalization over time across both samples. Further, higher levels of depersonalization led to higher levels of emotional exhaustion and lower levels of personal accomplishment. To our knowledge, the present research is the first to provide reliable longitudinal evidence for the conceptualization of burnout as a developmental process, although the effects are not large enough to be of practical use in the recognition of burnout.     

Best Spatial Two-Stage Least Squares Estimators for a Spatial Autoregressive Model with Autoregressive Disturbances

Estimation of a cross-sectional spatial model containing both a spatial lag of the dependent variable and spatially autoregressive disturbances are considered. [Kelejian and Prucha (1998)]described a generalized two-stage least squares procedure for estimating such a spatial model. Their estimator is, however, not asymptotically optimal. We propose best spatial 2SLS estimators that are asymptotically optimal instrumental variable (IV) estimators. An associated goodness-of-fit (or over identification) test is available. We suggest computationally simple and tractable numerical procedures for constructing the optimal instruments.     

Survival Analysis For A Sequenced Clinical Trial With Covariates

In this paper we discuss the survival analysis for a clinical trial in which treatment categories and general prognostic data are realised at different stages during a patient's survival time. In the light of possible strategies for the parsimonious modelling of such data, a corresponding sequence of illustrative analyses is presented. Detailed results are given for a weighted least squares analysis and these generally agree with those obtained by maximum likelihood.     

Adaptive Gaussian Markov random fields with applications in human brain mapping

Summary.  Functional magnetic resonance imaging has become a standard technology in human brain mapping. Analyses of the massive spatiotemporal functional magnetic resonance imaging data sets often focus on parametric or non-parametric modelling of the temporal component, whereas spatial smoothing is based on Gaussian kernels or random fields. A weakness of Gaussian spatial smoothing is underestimation of activation peaks or blurring of high curvature transitions between activated and non-activated regions of the brain. To improve spatial adaptivity, we introduce a class of inhomogeneous Markov random fields with stochastic interaction weights in a space-varying coefficient model. For given weights, the random field is conditionally Gaussian, but marginally it is non-Gaussian. Fully Bayesian inference, including estimation of weights and variance parameters, can be carried out through efficient Markov chain Monte Carlo simulation. Although motivated by the analysis of functional magnetic resonance imaging data, the methodological development is general and can also be used for spatial smoothing and regression analysis of areal data on irregular lattices. An application to stylized artificial data and to real functional magnetic resonance imaging data from a visual stimulation experiment demonstrates the performance of our approach in comparison with Gaussian and robustified non-Gaussian Markov random-field models.