Takacs–Fiksel Method for Stationary Marked Gibbs Point Processes

Abstract. This article studies a method to estimate the parameters governing the distribution of a stationary marked Gibbs point process. This procedure, known as the Takacs–Fiksel method, is based on the estimation of the left and right hand sides of the Georgii–Nguyen–Zessin formula and leads to a family of estimators due to the possible choices of test functions. We propose several examples illustrating the interest and flexibility of this procedure. We also provide sufficient conditions based on the model and the test functions to derive asymptotic properties (consistency and asymptotic normality) of the resulting estimator. The different assumptions are discussed for exponential family models and for a large class of test functions. A short simulation study is proposed to assess the correctness of the methodology and the asymptotic results.     

Nonparametric Bayesian Methods for Benchmark Dose Estimation

The article proposes and investigates the performance of two Bayesian nonparametric estimation procedures in the context of benchmark dose estimation in toxicological animal experiments. The methodology is illustrated using several existing animal dose‐response data sets and is compared with traditional parametric methods available in standard benchmark dose estimation software (BMDS), as well as with a published model‐averaging approach and a frequentist nonparametric approach. These comparisons together with simulation studies suggest that the nonparametric methods provide a lot of flexibility in terms of model fit and can be a very useful tool in benchmark dose estimation studies, especially when standard parametric models fail to fit to the data adequately.     

Canonical expansions,correlation structure,and conditional distributions of bivariate distributions generated by mixtures

A simple result concerning the canonical expansions of mixed bivariate distributions is considered. This result is then applied to analyze the correlation structures of the Bates-Neyman accident proneness model and its generalization, to derive probability inequalities based on the concept of positive dependence, and to construct a bivariate beta distribution with positive correlation coefficient applicable in computer simulation experiments. The mixture formulation of the conditional distribution of this class of mixed bivariate distributions is used to define and generate first-order autoregressive gamma and negative binomial sequences.     

A Comparison of Unit-Root Test Criteria

During the past 15 years, the ordinary least squares estimator and the corresponding pivotal statistic have been widely used for testing the unit-root hypothesis in autoregressive processes. Recently, several new criteria, based on maximum likelihood estimators and weighted symmetric estimators, have been proposed. In this article, we describe several different test criteria. Results from a Monte Carlo study that compares the power of the different criteria indicate that the new tests are more powerful against the stationary alternative. Of the procedures studied, the weighted symmetric estimator and the unconditional maximum likelihood estimator provide the most powerful tests against the stationary alternative. As an illustration, the weekly series of one-month treasury-bill rates is analyzed.     

Identification,Estimation, and Control of Uncertain Dynamic Systems: A Nonparametric Approach

This article is devoted to a presentation of the author' practice of the non-parametric estimation theory for the estimation, filtering, and control of uncertain dynamic systems. The fundamental advantage of this approach is a weak dependency on prior modeling assumptions about uncertain dynamic components. This approach appears to be of great interest for the control of general discrete-time processes, and in particular, biotechnological processes, which are emblematic of nonlinear uncertain and partially observed systems.     

Asymptotic design and estimation using linear splines

The estimation of a regression function g using linear splines is considered. The integrated mean square error is minimized using choice of estimator, allocation of observations and displacement of knots.     

Infant feeding and post-neonatal mortality in Derbyshire,England, in the early twentieth century

This paper examines influences on post-neonatal mortality in Derbyshire (England) in the early twentieth century, by applying multivariate hazard analysis to a rare individual-level data set. The data allow detailed patterns of breastfeeding and weaning to be examined. The role of feeding is given special attention as a mediator between mortality and the other environmental, social, and demographic factors considered. Twins and illegitimate children were more likely to have been hand-fed, but this could explain only a small fraction of their increased vulnerability. Artificial feeding was associated with increased risks of death from diarrhoea, respiratory disease, and wasting diseases. It is suggested that the link with wasting diseases was predominantly the result of the greater likelihood of congenitally weak children being hand-fed. Most of the variation in post-neonatal mortality, particularly from respiratory disease, was explained by environmental influences - population density, altitude, and the presence of mining.     

A fourier integral density estimate: a Monte Carlo study

Davis (1977) proposed the use of a kernel density estimate which is the sample characteristic function integrated over (-A(n) , A(n)), where A(n) is chosen to minimize the mean integrated square error of the estimate. The scalar, A(n), is determined by the sample size and the population characteristic function. This paper investigates, in a Monte Carlo study, the mean integrated square error obtained under a procedure suggested by Davis (1977) for estimating A(n) when the population characteristic function is unknown.     

Computer integrated manufacturing: a complex information system

To be successful in today's competitive global environment, manufacturers must be able to respond quickly to changes in the manufacturing environment. Automation technology makes it possible for manufacturers to have this essential capability. A conceptualized information system that integrates the programmable design of a product and its manufacture with both planning activities and the manufacturing process is described. The information system integrates the programmable automation technologies used in computer integrated manufacturing with engineering and business databases. The computer integrated manufacturing concept is expended to the concept of a computer integrated economy. The technological difficulties that arise when implementing a computer integrated economy are identified.