Spatial variation in soil inorganic nitrogen across an arid urban ecosystem

We explored variations in inorganic soil nitrogen (N) concentrations across metropolitan Phoenix, Arizona, and the surrounding desert using a probability-based synoptic survey. Data were examined using spatial statistics on the entire region, as well as for the desert and urban sites separately. Concentrations of both NO₃-N and NH₄-N were markedly higher and more heterogeneous amongst urban compared to desert soils. Regional variation in soil NO₃-N concentration was best explained by latitude, land use history, population density, along with percent cover of impervious surfaces and lawn, whereas soil NH₄-N concentrations were related to only latitude and population density. Within the urban area, patterns in both soil NO₃-N and NH₄-N were best predicted by elevation, population density and type of irrigation in the surrounding neighborhood. Spatial autocorrelation of soil NO₃-N concentrations explained 49% of variation among desert sites but was absent between urban sites. We suggest that inorganic soil N concentrations are controlled by a number of ‘local’ or ‘neighborhood’ human-related drivers in the city, rather than factors related to an urban-rural gradient.     

Bayesian analysis of discrete time warranty data

Summary.  The analysis of warranty claim data, and their use for prediction, has been a topic of active research in recent years. Field data comprising numbers of units returned under guarantee are examined, covering both situations in which the ages of the failed units are known and in which they are not. The latter case poses particular computational problems for likelihood-based methods because of the large number of feasible failure patterns that must be included as contributions to the likelihood function. For prediction of future warranty exposure, which is of central concern to the manufacturer, the Bayesian approach is adopted. For this, Markov chain Monte Carlo methodology is developed.     

Efficient designs based on orthogonal arrays of type I and type II for experiments using units ordered over time or space

For a wide variety of applications, experiments are based on units ordered over time or space. Models for these experiments generally may include one or more of: correlations, systematic trends, carryover effects and interference effects. Since the standard optimal block designs may not be efficient in these situations, orthogonal arrays of type I and type II, which were introduced in 1961 by C.R. Rao [Combinatorial arrangements analogous to orthogonal arrays, Sankhya A 23 (1961) 283–286], have been recently used to construct optimal and efficient designs for many of these experiments. Results in this area are unified and the salient features are outlined.     

A semi-parametric approach to robust parameter design

Parameter design or robust parameter design (RPD) is an engineering methodology intended as a cost-effective approach for improving the quality of products and processes. The goal of parameter design is to choose the levels of the control variables that optimize a defined quality characteristic. An essential component of RPD involves the assumption of well estimated models for the process mean and variance. Traditionally, the modeling of the mean and variance has been done parametrically. It is often the case, particularly when modeling the variance, that nonparametric techniques are more appropriate due to the nature of the curvature in the underlying function. Most response surface experiments involve sparse data. In sparse data situations with unusual curvature in the underlying function, nonparametric techniques often result in estimates with problematic variation whereas their parametric counterparts may result in estimates with problematic bias. We propose the use of semi-parametric modeling within the robust design setting, combining parametric and nonparametric functions to improve the quality of both mean and variance model estimation. The proposed method will be illustrated with an example and simulations.     

Refined Inference on Long Memory in Realized Volatility

There is an emerging consensus in empirical finance that realized volatility series typically display long range dependence with a memory parameter (d) around 0.4 (Andersen et al., 2001; Martens et al., 2004). The present article provides some illustrative analysis of how long memory may arise from the accumulative process underlying realized volatility. The article also uses results in Lieberman and Phillips (2004, 2005) to refine statistical inference about d by higher order theory. Standard asymptotic theory has an O(n^-1/2) error rate for error rejection probabilities, and the theory used here refines the approximation to an error rate of o(n^-1/2). The new formula is independent of unknown parameters, is simple to calculate and user-friendly. The method is applied to test whether the reported long memory parameter estimates of Andersen et al. (2001) and Martens et al. (2004) differ significantly from the lower boundary (d = 0.5) of nonstationary long memory, and generally confirms earlier findings.     

On the Evolution of Competitive Balance: The Impact of an Increasing Global Search

The Coase theorem maintains that where free-market precepts exist, the allocation of property rights does not impact the distribution of resources. An application to Major League Baseball suggests that institutions such as free agency and the reverse-order amateur draft would not impact player distributions and therefore would not impact competitive balance. The present study finds that the distribution of wins is generally consistent with the precepts of the Coase theorem and therefore suggests a course for those who wish to alter the level of competitive balance: Major League Baseball should increase its focus on expanding the size of its labor pool. (JEL O15 , L83 , C22 )     

UNDERSTANDING THE DECISION TO PARTICIPATE IN A SURVEY

The lack of full participation in sample surveys threatens theinferential value of the survey method. We review a set of conceptualdevelopments and experimental findings that appear to be informativeabout causes of survey participation; offer an integration ofthat work with findings from the more traditional statisticaland survey methodological literature on nonresponse; and, giventhe theoretical structure, deduce potentially promising pathsof research toward the understanding of survey participation.     

Libertarian collective decision-making: A new framework

A model of collective decision-making is presented which enables us to analyze the allocation of individual rights and liberties. The model broadens the traditional social choice framework. In social choice theory it is usually assumed that individuals have preferences over a set of feasible alternatives and that society reaches a decision on the basis of those preferences only. In the model presented in this paper, decision procedures do not only take information about individual preferences as input, but also information about individual freedom. The specific decision procedures we present and analyze can be characterized as libertarian.This paper was written while I was a visitor at the University of California, Riverside. The visit was made possible by financial support of the Netherlands Organization of Scientific Research (NWO). I am extremely grateful to Prasanta Pattanaik for many stimulating discussions on the subject. For helpful comments I also thank Ad van Deemen, Grahame Lock, Huib Pellikaan and Marcel Wissenburg. Finally, I express my thanks to Wulf Gaertner and two anonymous referees.     

Screening for patients with alcohol problems: severity of patients identified by the CAGE.

Primary care physicians are well situated to identify patients with substance abuse problems and motivate them to seek appropriate assistance, but active programs are the exception. A study in a community setting was undertaken to assess the CAGE (the first letters of key words in a series of four questions about drinking: cut down; annoyed; guilty; and eye-opener), instrument in the routine screening for alcohol problems in both new and established patients. The screening process identified subjects for a pilot evaluation of a motivational interview designed to encourage problem-solving behavior. This article focuses on the screening results and the use of the CAGE instrument. During June and July of 1990, 687 patients of two primary care physicians belonging to a large group practice were asked to complete a health questionnaire that included the CAGE. Those who responded affirmatively to at least two of the four CAGE questions were requested to participate in follow-up assessment of problems associated with alcohol and health. The type and severity of alcohol problems experienced by patients who scored positive on the CAGE are described. Prevalence of a positive score on the CAGE was 8.6 percent with males, smokers, and blue collar and unemployed persons being more likely to score positive. The positive predictive value was .68. Primarily, persons with moderate alcohol problems were identified. Results show that the CAGE instrument is a useful screening device for identifying those with mild to moderate substance abuse problems, increasing the opportunity for intervention prior to serious medical complications. The instrument is easily administered, and has demonstrated relatively high levels of sensitivity and specificity. When combined with assessment and motivational interviews, the CAGE shows promise in the secondary prevention of substance abuse and related health problems.