Using a computer simulation game to teach family time use concepts

A computer simulation game designed to give students insights into family time use concepts has been used in an introductory family resource management class at Ohio State University and other universities. The game allows for planning for time use changes over the family life cycle. The game can give students insights into economic and sociological models of time use. It is possible that future versions of the game could be used in family counseling.His research interests include derivation of optimal decision-making rules for families and family financial management.Her research interests include financial management over the life course including financial ratios, baby boomers, and retirement. She received her Ph.D. in 1993 from The Ohio State University in Family Resource Management.His research interests include the time trade-offs made between couples, financial planning over the life cycle, and the interaction of time and money between families and their home-based businesses. He received his Ph.D. in 1993 from The Ohio State University in Family Resource Management.     

ASSESSING THE RELATIONSHIP BETWEEN SYMPTOMS OF ALLERGIC RHINOCONJUNCTIVITIS AND POLLEN COUNTS

The relationship between daily pollen counts during the peak pollen season and hay fever symptoms in known sufferers of pollen allergy was investigated in a Sydney hospital‐based study. This paper develops statistical models for both the short term (day to day) associations and the longer term relationships between these time series. Possible effects of asthma status are investigated. The analyses illustrate how different relationships between time series may be explored in a simple way by working on different time scales with suitably transformed variables.     

Parameter Estimation for a Discretely Observed Integrated Diffusion Process

Abstract.  We consider the estimation of unknown parameters in the drift and diffusion coefficients of a one-dimensional ergodic diffusion X when the observation is a discrete sampling of the integral of X at times i Δ , i  =  1 ,…, n . Assuming that the sampling interval tends to 0 while the total length time interval tends to infinity, we first prove limit theorems for functionals associated with our observations. We apply these results to obtain a contrast function. The associated minimum contrast estimators are shown to be consistent and asymptotically Gaussian with different rates for drift and diffusion coefficient parameters.     

Bayesian palaeoclimate reconstruction

Summary.  We consider the problem of reconstructing prehistoric climates by using fossil data that have been extracted from lake sediment cores. Such reconstructions promise to provide one of the few ways to validate modern models of climate change. A hierarchical Bayesian modelling approach is presented and its use, inversely, is demonstrated in a relatively small but statistically challenging exercise: the reconstruction of prehistoric climate at Glendalough in Ireland from fossil pollen. This computationally intensive method extends current approaches by explicitly modelling uncertainty and reconstructing entire climate histories. The statistical issues that are raised relate to the use of compositional data (pollen) with covariates (climate) which are available at many modern sites but are missing for the fossil data. The compositional data arise as mixtures and the missing covariates have a temporal structure. Novel aspects of the analysis include a spatial process model for compositional data, local modelling of lattice data, the use, as a prior, of a random walk with long-tailed increments, a two-stage implementation of the Markov chain Monte Carlo approach and a fast approximate procedure for cross-validation in inverse problems. We present some details, contrasting its reconstructions with those which have been generated by a method in use in the palaeoclimatology literature. We suggest that the method provides a basis for resolving important challenging issues in palaeoclimate research. We draw attention to several challenging statistical issues that need to be overcome.     

On Non-parametric Estimation of the Survival Function with Competing Risks

The non-parametric maximum likelihood estimators (MLEs) are derived for survival functions associated with individual risks or system components in a reliability framework. Lifetimes are observed for systems that contain one or more of those components. Analogous to a competing risks model, the system is assumed to fail upon the first instance of any component failure; i.e. the system is configured in series. For any given risk or component type, the asymptotic distribution is shown to depend explicitly on the unknown survival function of the other risks, as well as the censoring distribution. Survival functions with increasing failure rate are investigated as a special case. The order restricted MLE is shown to be consistent under mild assumptions of the underlying component lifetime distributions.     

Smoothing for discrete-valued time series

We deal with smoothed estimators for conditional probability functions of discrete-valued time series { Y_t } under two different settings. When the conditional distribution of Y_t given its lagged values falls in a parametric family and depends on exogenous random variables, a smoothed maximum (partial) likelihood estimator for the unknown parameter is proposed. While there is no prior information on the distribution, various nonparametric estimation methods have been compared and the adjusted Nadaraya–Watson estimator stands out as it shares the advantages of both Nadaraya–Watson and local linear regression estimators. The asymptotic normality of the estimators proposed has been established in the manner of sparse asymptotics, which shows that the smoothed methods proposed outperform their conventional, unsmoothed, parametric counterparts under very mild conditions. Simulation results lend further support to this assertion. Finally, the new method is illustrated via a real data set concerning the relationship between the number of daily hospital admissions and the levels of pollutants in Hong Kong in 1994–1995. An ad hoc model selection procedure based on a local Akaike information criterion is proposed to select the significant pollutant indices.     

Spatiotemporal prediction for log-Gaussian Cox processes

Space–time point pattern data have become more widely available as a result of technological developments in areas such as geographic information systems. We describe a flexible class of space–time point processes. Our models are Cox processes whose stochastic intensity is a space–time Ornstein–Uhlenbeck process. We develop moment-based methods of parameter estimation, show how to predict the underlying intensity by using a Markov chain Monte Carlo approach and illustrate the performance of our methods on a synthetic data set.     

International Business Cycle Asymmetry and Time Irreversible Nonlinearities

Using tests of time reversibility, this paper provides further statistical evidence on the long-standing conjecture in economics concerning the potentially asymmetric behaviour of output over the expansionary and contractionary phases of the business cycle. A particular advantage of this approach is that it provides a discriminating test that is instructive as to whether any asymmetries detected are due to asymmetric shocks to a linear model, or an underlying non-linear model with symmetric shocks, and in the latter case is informative as to the potential form of that nonlinear model. Using a long span of international per capita output growth data, the asymmetry detected is overwhelmingly consistent with the long standing perception that the output business cycle is characterized by steeper recessions and longer more gentle expansions, but the evidence for this form of business cycle asymmetry is weaker in the data adjusted for the influence of outliers associated with wars and other extreme events. Statistically significant time irreversibility is reported for the output growth rates of almost all of the countries considered in the full sample data, and there is evidence that this time irreversibility is of a form implying an underlying nonlinear model with symmetrically distributed innovations for 15 of the 22 countries considered. However, the time irreversibility test results for the outlier-trimmed full sample data reveal significant time irreversibility in output growth for around one half of the countries considered, predominantly in Northern Europe and North America, and of a form implying a nonlinear underlying model in only a further half of those cases.