Baseline biodiversity surveys of the soil macrofauna of London’s green spaces

A serious barrier to our understanding of urban ecosystems is a lack of information on the ecology of soils organisms of green spaces within large cities. This study addresses this gap by providing baseline survey data on the biodiversity of soil macrofauna in urban parks and domestic gardens of London, UK. In April and June 2004, the soil macrofauna were handsorted from soil cores in eleven parks and gardens of various sizes in central London. Five taxa were identified to species (Lumbricidae, Isopoda, Diplopoda, Chilopoda and Formicidae). The biodiversity value of the two main habitats (horticultural borders and mown grass lawns) was assessed and the influence of a range of environmental factors on species density (number of species per unit area) examined. The species densities of the studied soil invertebrates in the urban gardens were comparable with those found in natural ecosystems, although plant borders contained significantly more species than lawns. Borders had higher levels of plant nutrients, higher floristic diversity and lower levels of micronutrients and heavy metals than lawns. Significant predictor variables of species densities in the plant borders were the percentage of leaf litter cover, sampling month and soil pH. Species densities in the lawns were significantly correlated with the distance of the samples from the edge of the lawn.     

Modeling Earnings Dynamics

In this paper, we use indirect inference to estimate a joint model of earnings, employment, job changes, wage rates, and work hours over a career. We use the model to address a number of important questions in labor economics, including the source of the experience profile of wages, the response of job changes to outside wage offers, and the effects of seniority on job changes. We also study the dynamic response of wage rates, hours, and earnings to various shocks, and measure the relative contributions of the shocks to the variance of earnings in a given year and over a lifetime. We find that human capital accounts for most of the growth of earnings over a career, although job seniority and job mobility also play significant roles. Unemployment shocks have a large impact on earnings in the short run, as well as a substantial long‐term effect that operates through the wage rate. Shocks associated with job changes and unemployment make a large contribution to the variance of career earnings and operate mostly through the job‐specific error components of wages and hours.     

Exact Tests of Significance in Higher Dimensional Tables

We describe methods used to provide an exact test of significance of the hypothesis that all factors are mutually independent of each other in 2³ and 2⁴ contingency tables. Several numerical examples demonstrate the advantages of exact tests over approximate significance levels. We give bounds on the number of tables needed to perform this exact significance test. In four or more dimensions the number of tables in this enumeration becomes astronomical with even modest sample sizes. Inverting the characteristic function of the exact distribution has proved useful in these situations.     

Retirement and Productive Activity in Later Life

It is commonly believed that as people age and leave the labor force, they disengage from productive activity and become dependent. While consistent with the conventional economic view of what constitutes productive activity, this belief is inconsistent with a more contemporary view that unpaid work (domestic, volunteer, and caring work) is equally economically valuable (i.e., productive). We investigate differences in how people allocate time among productive and other activities pre- and post-retirement. Results indicate that people remain engaged in productive activities even as they move out of the labor force, substituting unpaid for paid work. Only respondents who have exceeded their normal life expectancy greatly reduce their productive activity. Evidence suggests a need to revisit assumptions underlying policy debate on population aging.

Goodness‐of‐fit testing based on a weighted bootstrap: A fast large‐sample alternative to the parametric bootstrap

The process comparing the empirical cumulative distribution function of the sample with a parametric estimate of the cumulative distribution function is known as the empirical process with estimated parameters and has been extensively employed in the literature for goodness‐of‐fit testing. The simplest way to carry out such goodness‐of‐fit tests, especially in a multivariate setting, is to use a parametric bootstrap. Although very easy to implement, the parametric bootstrap can become very computationally expensive as the sample size, the number of parameters, or the dimension of the data increase. An alternative resampling technique based on a fast weighted bootstrap is proposed in this paper, and is studied both theoretically and empirically. The outcome of this work is a generic and computationally efficient multiplier goodness‐of‐fit procedure that can be used as a large‐sample alternative to the parametric bootstrap. In order to approximately determine how large the sample size needs to be for the parametric and weighted bootstraps to have roughly equivalent powers, extensive Monte Carlo experiments are carried out in dimension one, two and three, and for models containing up to nine parameters. The computational gains resulting from the use of the proposed multiplier goodness‐of‐fit procedure are illustrated on trivariate financial data. A by‐product of this work is a fast large‐sample goodness‐of‐fit procedure for the bivariate and trivariate t distribution whose degrees of freedom are fixed. The Canadian Journal of Statistics 40: 480–500; 2012 © 2012 Statistical Society of Canada     

Penalized triograms: total variation regularization for bivariate smoothing

Summary.  Hansen, Kooperberg and Sardy introduced a family of continuous, piecewise linear functions defined over adaptively selected triangulations of the plane as a general approach to statistical modelling of bivariate densities and regression and hazard functions. These triograms enjoy a natural affine equivariance that offers distinct advantages over competing tensor product methods that are more commonly used in statistical applications. Triograms employ basis functions consisting of linear 'tent functions' defined with respect to a triangulation of a given planar domain. As in knot selection for univariate splines, Hansen and colleagues adopted the regression spline approach of Stone. Vertices of the triangulation are introduced or removed sequentially in an effort to balance fidelity to the data and parsimony. We explore a smoothing spline variant of the triogram model based on a roughness penalty adapted to the piecewise linear structure of the triogram model. We show that the roughness penalty proposed may be interpreted as a total variation penalty on the gradient of the fitted function. The methods are illustrated with real and artificial examples, including an application to estimated quantile surfaces of land value in the Chicago metropolitan area.