Marginal and association regression models for longitudinal binary data with drop‐outs: A likelihood‐based approach

Longitudinal data often contain missing observations, and it is in general difficult to justify particular missing data mechanisms, whether random or not, that may be hard to distinguish. The authors describe a likelihood‐based approach to estimating both the mean response and association parameters for longitudinal binary data with drop‐outs. They specify marginal and dependence structures as regression models which link the responses to the covariates. They illustrate their approach using a data set from the Waterloo Smoking Prevention Project They also report the results of simulation studies carried out to assess the performance of their technique under various circumstances.     

Discrete time modelling of disease incidence time series by using Markov chain Monte Carlo methods

Summary.  A stochastic discrete time version of the susceptible–infected–recovered model for infectious diseases is developed. Disease is transmitted within and between communities when infected and susceptible individuals interact. Markov chain Monte Carlo methods are used to make inference about these unobserved populations and the unknown parameters of interest. The algorithm is designed specifically for modelling time series of reported measles cases although it can be adapted for other infectious diseases with permanent immunity. The application to observed measles incidence series motivates extensions to incorporate age structure as well as spatial epidemic coupling between communities.     

Proportional reversed hazard rate model and its applications

The purpose of this paper is to study the structure and properties of the proportional reversed hazard rate model (PRHRM) in contrast to the celebrated proportional hazard model (PHM). The monotonicity of the hazard rate and the reversed hazard rate of the model is investigated. Some criteria of aging are presented and the inheritance of the aging notions (of the base distribution) by the PRHRM is studied. Characterizations of the model involving Fisher information are presented and the statistical inference of the parameters is discussed. Finally, it is shown that several members of the proportional reversed hazard rate class have been found to be useful and flexible in real data analysis.     

The Measurement of Multidimensional Poverty

Many authors have insisted on the necessity of defining poverty as a multidimensional concept rather than relying on income or consumption expenditures per capita. Yet, not much has actually been done to include the various dimensions of deprivation into the practical definition and measurement of poverty. Existing attempts along that direction consist of aggregating various attributes into a single index through some arbitrary function and defining a poverty line and associated poverty measures on the basis of that index. This is merely redefining more generally the concept of poverty, which then essentially remains a one dimensional concept. The present paper suggests that an alternative way to take into account the multi-dimensionality of poverty is to specify a poverty line for each dimension of poverty and to consider that a person is poor if he/she falls below at least one of these various lines. The paper then explores how to combine these various poverty lines and associated one-dimensional gaps into multidimensional poverty measures. An application of these measures to the rural population in Brazil is also given with poverty defined on income and education.     

Parenting Behaviors Among Mainland Puerto Rican Adolescent Mothers: The Role of Grandmother and Partner Involvement

Relations between grandmother and partner involvement (coresidence, social support, child care) and Puerto Rican adolescent mothers' parenting behaviors were examined. Few associations between grandmother involvement and behavior emerged, and these were moderated by coresidence. In contrast, partner involvement showed a number of significant (positive) direct and moderated relations with behavior. For both providers, the direction of the relations between social support and behavior during teaching was the opposite depending on coresidence: Greater social support was related to the display of more positive behavior among coresiding mothers but to less competent behavior among non‐coresiding mothers. The relations between child care support and behavior differed across providers. For grandmothers, greater child care was related to less positive behavior during teaching among coresiding mothers but to more competent behavior among non‐coresiding mothers. For partners, greater child care was more strongly related to positive behavior during play among non‐coresiding than coresiding mothers. Results are discussed in light of Latino cultural values, developmental issues, and implications for intervention.     

Silphids in urban forests: Diversity and function

Many ecologists have examined the process of how urbanization reduces biological diversity but rarely have its ecological consequences been assessed. We studied forest-dwelling burying beetles (Coleoptera: Silphidae)—a guild of insects that requires carrion to complete their life cycles—along an urban-rural gradient of land use in Maryland. Our objective was to determine how forest fragmentation associated with urbanization affects (1) beetle community diversity and structure and (2) the ecological function provided by these insects, that is, decomposition of vertebrate carcasses. Forest fragmentation strongly reduced burying beetle diversity and abundance, and did so far more pervasively than urbanization of the surrounding landscape. The likelihood that beetles interred experimental baits was a direct, positive function of burying beetle diversity. We conclude that loss of burying beetle diversity resulting from forest fragmentation could have important ecological consequences in urban forests.     

HEAVY-TAILED-DISTRIBUTED THRESHOLD STOCHASTIC VOLATILITY MODELS IN FINANCIAL TIME SERIES

To capture mean and variance asymmetries and time‐varying volatility in financial time series, we generalize the threshold stochastic volatility (THSV) model and incorporate a heavy‐tailed error distribution. Unlike existing stochastic volatility models, this model simultaneously accounts for uncertainty in the unobserved threshold value and in the time‐delay parameter. Self‐exciting and exogenous threshold variables are considered to investigate the impact of a number of market news variables on volatility changes. Adopting a Bayesian approach, we use Markov chain Monte Carlo methods to estimate all unknown parameters and latent variables. A simulation experiment demonstrates good estimation performance for reasonable sample sizes. In a study of two international financial market indices, we consider two variants of the generalized THSV model, with US market news as the threshold variable. Finally, we compare models using Bayesian forecasting in a value‐at‐risk (VaR) study. The results show that our proposed model can generate more accurate VaR forecasts than can standard models.