The residential landscape: fluxes of elements and the role of household decisions

We assessed biogeochemical cycling of elements through residential household landscapes to evaluate the importance of annual to decadal household-level decisions for element fluxes that contribute to urban and regional pollution. We combined a mailed survey, vegetation measurements, and allometric and biogeochemical models to estimate fluxes and accumulation of carbon (C), nitrogen (N), and phosphorus (P) in landscapes of 360 single-family homes in the Minneapolis-Saint Paul, Minnesota metropolitan area. Carbon inputs and accumulation were strongly influenced by the presence of trees on the property. Nitrogen inputs to the landscape exceeded estimated ecosystem demand for N on average by 51% and were dominated by N fertilizer application. Because Minnesota state law restricts the use of P fertilizer, pet waste was responsible for 84% of P inputs to the landscape. The results have implications for understanding sources of urban pollution and the potential flexibility (i.e., the potential for change) in household behaviors such as tree planting, fertilization, and pet waste management that contribute to such pollution.     

Some Faults of the Bayes Factor in Nonparametric Model Selection

We compare different Bayesian strategies for testing a parametric model versus a nonparametric alternative on the ground of their ability to solve the inconsistency problems arising when using the Bayes factor under certain conditions. A preliminary critical discussion of such an inconsistency is provided.     

A family of power‐divergence diagnostics for goodness‐of‐fit

The author extends to the Bayesian nonparametric context the multinomial goodness‐of‐fit tests due to Cressie & Read (1984). Her approach is suitable when the model of interest is a discrete distribution. She provides an explicit form for the tests, which are based on power‐divergence measures between a prior Dirichlet process that is highly concentrated around the model of interest and the corresponding posterior Dirichlet process. In addition to providing interesting special cases and useful approximations, she discusses calibration and the choice of test through examples.     

Finite mixtures of matrix normal distributions for classifying three-way data

Matrix-variate distributions represent a natural way for modeling random matrices. Realizations from random matrices are generated by the simultaneous observation of variables in different situations or locations, and are commonly arranged in three-way data structures. Among the matrix-variate distributions, the matrix normal density plays the same pivotal role as the multivariate normal distribution in the family of multivariate distributions. In this work we define and explore finite mixtures of matrix normals. An EM algorithm for the model estimation is developed and some useful properties are demonstrated. We finally show that the proposed mixture model can be a powerful tool for classifying three-way data both in supervised and unsupervised problems. A simulation study and some real examples are presented.     

An improved statistical test for historical linguistics

Historical linguistics needs procedures to evaluate the similarity between languages through the comparison of specific word lists drawn from the whole vocabulary. The main issue is to evaluate a fair threshold for the number of similar items beyond which it is sensible to reject the hypothesis of chance similarity. After a short review of papers dealing with that problem, in this paper an extension of those methods is proposed which exploits available data in a more efficient way. In particular, the exact distribution of the new test statistics is calculated and the power of the new procedure is compared with the power of the existing method.     

Finding Relevant Linear Manifolds in Classification by Gaussian Mixtures

In this article, we present a strategy for producing low-dimensional projections that maximally separate the classes in Gaussian Mixture Model classification. The most revealing linear manifolds are those along which the classes are maximally separable. Here we consider a particular probability product kernel as a measure of similarity or affinity between the class-conditional distributions. It takes an appealing closed analytical form in the case of Gaussian mixture components. The performance of the proposed strategy has been evaluated on real data.