Designing and integrating composite networks for monitoring multivariate gaussian pollution fields

Networks of ambient monitoring stations are used to monitor environmental pollution fields such as those for acid rain and air pollution. Such stations provide regular measurements of pollutant concentrations. The networks are established for a variety of purposes at various times so often several stations measuring different subsets of pollutant concentrations can be found in compact geographical regions. The problem of statistically combining these disparate information sources into a single 'network' then arises. Capitalizing on the efficiencies so achieved can then lead to the secondary problem of extending this network. The subject of this paper is a set of 31 air pollution monitoring stations in southern Ontario. Each of these regularly measures a particular subset of ionic sulphate, sulphite, nitrite and ozone. However, this subset varies from station to station. For example only two stations measure all four. Some measure just one. We describe a Bayesian framework for integrating the measurements of these stations to yield a spatial predictive distribution for unmonitored sites and unmeasured concentrations at existing stations. Furthermore we show how this network can be extended by using an entropy maximization criterion. The methods assume that the multivariate response field being measured has a joint Gaussian distribution conditional on its mean and covariance function. A conjugate prior is used for these parameters, some of its hyperparameters being fitted empirically.     

Factors influencing Soay sheep survival

We present a survival analysis of Soay sheep mark recapture and recovery data. Unlike previous conditional analyses, it is not necessary to assume equality of recovery and recapture probabilities; instead these are estimated by maximum likelihood. Male and female sheep are treated separately, with the higher numbers and survival probabilities of the females resulting in a more complex model than that used for the males. In both cases, however, age and time aspects need to be included and there is a strong indication of a reduction in survival for sheep aged 7 years or more. Time variation in survival is related to the size of the population and selected weather variables, by using logistic regression. The size of the population significantly affects the survival probabilities of male and female lambs, and of female sheep aged 7 or more years. March rainfall and a measure of the North Atlantic oscillation are found to influence survival significantly for all age groups considered, for both males and females. Either of these weather variables can be used in a model. Several phenotypic and genotypic individual covariates are also fitted. The only covariate which is found to influence survival significantly is the type of horn of first-year female sheep. There is a substantial variation in the recovery probabilities over time, reflecting in part the increased effort when a population crash was expected. The goodness of fit of the model is checked by using graphical procedures.     

Binary probability maps using a hidden conditional autoregressive Gaussian process with an application to Finnish common toad data

The Finnish common toad data of Heikkinen and Hogmander are reanalysed using an alternative fully Bayesian model that does not require a pseudolikelihood approximation and an alternative prior distribution for the true presence or absence status of toads in each 10 km×10 km square. Markov chain Monte Carlo methods are used to obtain posterior probability estimates of the square-specific presences of the common toad and these are presented as a map. The results are different from those of Heikkinen and Hogmander and we offer an explanation in terms of the prior used for square-specific presence of the toads. We suggest that our approach is more faithful to the data and avoids unnecessary confounding of effects. We demonstrate how to extend our model efficiently with square-specific covariates and illustrate this by introducing deterministic spatial changes.     

Dynamic models and attainment in the workplace

The two most marked trends in recent stratification studies are: (1) the shift away from a statical approach to attainment and toward a dynamical representation of achievement, and (2) the shift away from the assumption that achievement is largely a matter of individual characteristics, and toward the view that achievement is the outcome of an employer-employee exchange of productive resources for earnings and status. This paper forges a link between these parallel trends by elaborating on previous formulations of dynamic models of achievement and applying the results to the analysis of earnings attainment in an internal labor market. The modeling section of the paper joins within a single framework the growing interest in ascertaining how a given structure of opportunity shapes achievement and in determining the different points in the career line at which individual background and resource variables impact attainment. Special attention is devoted to the problems facing researchers who wish to bring a dynamic conceptualization of achievement to cross-sectional or otherwise deficient data. Although the empirical application of the various models is largely meant to be illustrative, it is of interest in its own right because it goes substantially beyond previous efforts in this area.     

Elimination ofSitotroga cerealella (Olivier) bySitophilus zeamais Motschulsky from mixed laboratory cultures on maize

Summary This paper describes a series of experiments conducted to determine whySitophilus zeamais Mots. andSitotroga cerealella (Oliv.) could not survive together in maize cultures in the laboratory. The effect ofS. zeamais on different developmental stages ofS. cerealella was investigated. The presence of adultS. zeamais slightly affected moth copulation, egg laying and moth eggs in a mixed culture, but large numbers of developing moths inside maize grains were killed by the adult weevil through feeding on the grains. The major cause of elimination ofS. cerealella byS. zeamais from mixed cultures was therefore found to be damage to the immature moths in grain and such moth mortality increased as the developing moths became bigger in the grains. A weevil: grain ratio of approximately 1.4∶1 was found to be the critical weevil density at which the moth disappeared from the mixed cultures.     

Seasonal forcing on the dynamics ofClethrionomys rufocanus: Modeling geographic gradients in population dynamics

We interpret gradients in population dynamics of the gray-sided vole from the southwestern part of the island of Hokkaido to its northeastern part within the framework of a phenomenological model involving the relative length of summer and winter. In Hokkaido, as in other northern regions, both spring and fall is considered as short transition periods between the two main seasons — summer (the primary breeding season) and winter (the non-reproductive or secondary breeding season). We show that the geographic transition in dynamics may be understood as the combined consequence of different patterns of density-dependence during summer and winter, and geographically varying season lengths. Differences are shown to exist between summer and winter with respect to strength of density-dependence. Direct density-dependence, in particular, is stronger during winter than during summer. A model is presented to show how relative lengths of seasons can induce both stable and periodically fluctuating population dynamics. The results are compared and contrasted with what is otherwise known about the gradient in rodent dynamics in Fennoscandia.     

Collinearity, Bias, and Effect Size: Modeling “the” Effect of Track on Achievement

The estimated effect of any factor can be highly dependent on both the model and the data used for the analyses. This article presents an example of the estimated effect of one factor in two different data sets under three different forms of the standard linear model using the effect of track placement on achievement as an example. Some relative advantages and disadvantages of each model are considered. The analyses demonstrate that, given collinearity among the predictor variables, a model with a poorer statistical fit may be useful for some interpretive purposes.     

Effects of Group Practice of the Transcendental Meditation Program on Preventing Violent Crime in Washington,D.C.: Results of the National Demonstration Project,June--July 1993

This paper reports the results of a prospective experiment in which a group of approximately 4,000 participants in the Transcendental Meditation and TM-Sidhi programs of Maharishi Mahesh Yogi assembled in Washington, D.C., from June 7 to July 30, 1993. It was hypothesized that levels of violent crime in the District of Columbia would fall substantially during the Demonstration Project, as a result of the group's effect of increasing coherence and reducing stress in the collective consciousness of the District. A 27-member Project Review Board comprising independent scientists and leading citizens approved the research protocol and monitored the research process. Weekly crime data was derived from database records provided by the District of Columbia Metropolitan Police Department (DCMPD), which are used in the FBI Uniform Crime Reports. Statistical analysis considered the effect of weather variables, daylight, historical crime trends and annual patterns in the District of Columbia, as well as trends in neighboring cities. Consistent with previous research, levels of homicides, rapes and assaults (HRA crimes) correlated with average weekly temperature. Robberies approximately followed an annually recurring cycle. Time series analysis of 1993 data, controlling for temperature, showed that HRA crimes dropped significantly during the Demonstration Project, corresponding with increases in the size of the group; the maximum decrease was 23.3% (p < 2 × 10–9) [24.6% using a longer baseline, with 1988--1993 data (p < 3 × 10–5)], coincident with the peak number of participants in the group during the final week of the assembly. When the same period in each of the five previous years was examined, no significant decreases in HRA crimes were found. Robberies did not decrease significantly. However, a model that jointly estimated the effect of the Demonstration Project on both HRA crimes and robberies showed a significant reduction in violent crimes overall of 15.6% (p = 0.0008). Further analysis showed that the effect of the coherence-creating group on reducing HRA crimes could not be accounted for by additional police staffing. The time series analysis for HRA crimes gave results that are highly robust to alternative model specifications, and showed that the effect of the group size was cumulative and persisted after the Demonstration Project ended. Also, calculation of the steady state gain based on the time series model predicted that a permanent group of 4,000 coherence-creating experts in the District would have a long-term effect of reducing HRA crimes by 48%.     

Estimation of the parameters of symmetric stable ARMA and ARMA–GARCH models

In this article, we first propose the modified Hannan–Rissanen Method for estimating the parameters of autoregressive moving average (ARMA) process with symmetric stable noise and symmetric stable generalized autoregressive conditional heteroskedastic (GARCH) noise. Next, we propose the modified empirical characteristic function method for the estimation of GARCH parameters with symmetric stable noise. Further, we show the efficiency, accuracy and simplicity of our methods with Monte-Carlo simulation. Finally, we apply our proposed methods to model the financial data.