Urban tree cover: an ecological perspective

Analysis of urban tree cover is generally limited to inventories of tree structure and composition on public lands. This approach provided valuable information for resource management. However, it does not account for all tree cover within an urban landscape, thus providing insufficient information on ecological patterns and processes. We propose evaluating tree cover for an entire urban area that is based on patch dynamics. Treed patches are classified by their origin, structure, and management intensity. A patch approach enables ecologists to evaluate ecological patterns and processes for the entire urban landscape and to examine how social patterns influence these ecological patterns and processes.     

A Spatio-Temporal Model for Functional Magnetic Resonance Imaging Data – with a View to Resting State Networks

Abstract.  Functional magnetic resonance imaging (fMRI) is a technique for studying the active human brain. During the fMRI experiment, a sequence of MR images is obtained, where the brain is represented as a set of voxels. The data obtained are a realization of a complex spatio-temporal process with many sources of variation, both biological and technical. We present a spatio-temporal point process model approach for fMRI data where the temporal and spatial activation are modelled simultaneously. It is possible to analyse other characteristics of the data than just the locations of active brain regions, such as the interaction between the active regions. We discuss both classical statistical inference and Bayesian inference in the model. We analyse simulated data without repeated stimuli both for location of the activated regions and for interactions between the activated regions. An example of analysis of fMRI data, using this approach, is presented.     

Bayesian semiparametric inference for the accelerated failure-time model

Bayesian semiparametric inference is considered for a loglinear model. This model consists of a parametric component for the regression coefficients and a nonparametric component for the unknown error distribution. Bayesian analysis is studied for the case of a parametric prior on the regression coefficients and a mixture-of-Dirichlet-processes prior on the unknown error distribution. A Markov-chain Monte Carlo (MCMC) method is developed to compute the features of the posterior distribution. A model selection method for obtaining a more parsimonious set of predictors is studied. The method adds indicator variables to the regression equation. The set of indicator variables represents all the possible subsets to be considered. A MCMC method is developed to search stochastically for the best subset. These procedures are applied to two examples, one with censored data.     

Statistical methodology for assessing homology of intronic regions of genes

We consider the problem of statistically evaluating the similarity of DNA intronic regions of genes. Present algorithms are based on matching a sequence of interest with known DNA sequences in a gene bank and are designed primarily to assess homology among exonic regions of genes. Most research focuses on exonic regions because they have a clear biological significance, coding for proteins, and therefore tend to be more conserved in evolution than intronic regions. To investigate whether the intronic features of genes whose expression is highly sensitive to environmental perturbations differ from genes that have a more constant expression, a collection of oncogenes, tumor suppressor genes, and nonregulatory genes involved in energy metabolism are compared. An analysis of the features of these genes' intronic regions result in clustering by regulatory group. In addition, Billingsley's test for Markov structure (1961) suggests that 67% of the intronic regions in this collection of genes show evidence of nonrandom structure, indicating the possibility of a biological function for these regions. The result of Billingsley's test for homology is used as input to a clustering algorithm. The biological significance of this methodology lies in the identification of groups based on the intronic regions from genes of unknown function. With the advent of rapid sequencing techniques, there is a great need for statistical techniques to help identify the purpose of poorly understood portions of genes. These methods can be utilized to assess the functional group to which such a gene might possibly belong.     

Normalité asymptotique d'estimateurs convergents du mode conditionnel

We state sufficient conditions for asymptotic normality of convergent estimates of the conditional mode, irrespective of data dependence, and give an application to α-mixing stationary processes.     

Theory & Methods: Krige,Smooth, Both or Neither?

Both kriging and non-parametric regression smoothing can model a non-stationary regression function with spatially correlated errors. However comparisons have mainly been based on ordinary kriging and smoothing with uncorrelated errors. Ordinary kriging attributes smoothness of the response to spatial autocorrelation whereas non-parametric regression attributes trends to a smooth regression function. For spatial processes it is reasonable to suppose that the response is due to both trend and autocorrelation. This paper reviews methodology for non-parametric regression with autocorrelated errors which is a natural compromise between the two methods. Re-analysis of the one-dimensional stationary spatial data of Laslett (1994) and a clearly non-stationary time series demonstrates the rather surprising result that for these data, ordinary kriging outperforms more computationally intensive models including both universal kriging and correlated splines for spatial prediction. For estimating the regression function, non-parametric regression provides adaptive estimation, but the autocorrelation must be accounted for in selecting the smoothing parameter.     

Graphical Models for Composable Finite Markov Processes

Abstract.  Composable Markov processes were introduced by Schweder (1970) in order to capture the idea that a process can be composed of different components where some of these only depend on a subset of the other components. Here we propose a graphical representation of this kind of dependence which has been called 'local dependence'. It is shown that the graph allows to read off further independencies characterizing the underlying Markov process. Also, some standard methods for inference are adapted to exploit the graphical representation, e.g. for testing local independence.     

Fundamental references from the Western culture

This article presents theoretical elements concerning the ‘nature versus culture’ confrontation as a particular aspect of the worldwide expansion of Western civilization. Several speculations are made concerning the philosophical foundations which have provided the base for the current political and economic mega-scenarios in the contemporary world. We turn our attention to the articulations of the concept of nature with the processes of mondialization of Western culture and with the history of universal culture. Finally, several considerations are made on the dynamics of economic processes on a world scale and projections on the impact of science and technology in the social and political frames that are taking place in the contemporary world.     

从爆破音变体的语流环境看音系结构特征

爆破音的种种变体,不仅仅是一种单纯的发音问题,还在一定的程度上从一个侧面反映了英语音系结构的特征;英语爆破音变体发生的语流环境是一个外显的语音现象,在深层次上受着英语特有的音系结构的支持,而且,多变的语音受着固定不变的音系结构支持。这些特征决定了音系结构是非线性的。     

基层政府社会风险治理流程优化研究

社会风险治理是社会治理的重要组成部分,增强基层政府社会风险治理能力是推进国家治理体系和治理能力现代化的一大关键。随着我国社会的进一步转型,社会风险呈现出虚拟化、网络化、联动化等新态势,它使得基层政府社会风险治理面临着新的挑战。因此,需要从流程优化的角度,对基层政府社会风险治理的机制与方式进行创新,从而提高基层政府社会风险治理的水平。