Complaining Our Way to Quality: Complaints, Contracts and the Voluntary Sector

The research on which this paper is based is part of a wider study of quality assurance and the voluntary sector. The focus of this paper is on complaints procedures, as part of quality assurance mechanisms, where voluntary agencies are the contracted service providers. The authors argue that, with the introduction of contracting and particularly with its further development, the way in which complaints mechanisms currently operate will need to be revised if they are to be an effective part of the quality assurance process.     

加涅累积学习理论与幼儿科学教育

作为将行为主义学习论与认知主义学习论相结合的代表,加涅的累积学习理论日益受到教育者的关注.其累积学习的模式,以及关于学习的条件、学习的要素、学习的过程等理论为幼儿科学教育内容的选择和安排以及方法的选择和运用提供了有力的理论依据.     

Hypotheses testing for a multidimensional parameter of inhomogeneous Poisson processes

We observe a realization of an inhomogeneous Poisson process whose intensity function depends on an unknown multidimensional parameter. We consider the asymptotic behaviour of the Rao score test for a simple null hypothesis against the multilateral alternative. By using the Edgeworth type expansion (under the null hypothesis) for a vector of stochastic integrals with respect to the Poisson process, we refine the (classic) threshold of the test (obtained by the central limit theorem), which improves the first type probability of error. The expansion allows us to describe the power of the test under the local alternative, i.e. a sequence of alternatives, which converge to the null hypothesis with a certain rate. The rates can be different for components of the parameter.     

企业创新的执行过程研究

创新不是时髦的口号,创新重在切实地执行,而执行重在目标分解与层层落实。目前,从理论层面来讨论创新者居多,结合企业实际,从创新的执行层面讨论则相对较少。从企业创新执行角度,将创新的理念作为一个目标,分解为可执行的流程:企业分析—创新分析—创新需求评估—确定创新需求—企业创新制度设计—控制文档—企业创新制度执行—企业创新制度监控、反馈与有效性评估—识别和修正缺陷—流程和结果固化—持续执行。通过以上流程将创新融入企业,执行、固化并持续。     

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.