Evaluation of trace evidence in the form of multivariate data

Summary.  The evaluation of measurements on characteristics of trace evidence found at a crime scene and on a suspect is an important part of forensic science. Five methods of assessment for the value of the evidence for multivariate data are described. Two are based on significance tests and three on the evaluation of likelihood ratios. The likelihood ratio which compares the probability of the measurements on the evidence assuming a common source for the crime scene and suspect evidence with the probability of the measurements on the evidence assuming different sources for the crime scene and suspect evidence is a well-documented measure of the value of the evidence. One of the likelihood ratio approaches transforms the data to a univariate projection based on the first principal component. The other two versions of the likelihood ratio for multivariate data account for correlation among the variables and for two levels of variation: that between sources and that within sources. One version assumes that between-source variability is modelled by a multivariate normal distribution; the other version models the variability with a multivariate kernel density estimate. Results are compared from the analysis of measurements on the elemental composition of glass.     

A cointegration analysis of crime,economic activity,and police performance in São Paulo city

The main objective of this paper is to investigate possible causes for the significant reduction observed in crime rates in São Paulo city. By applying a cointegration analysis, we observed long-run relationships between crime, economic activity, and police performance. The results indicate that the lethal crime rate is positively related to unemployment and negatively related to real wages and to the results of law-enforcement activities, specifically arrests and seizure of firearms. Moreover, the hypothesis that the Disarmament Statute led to a reduction in the lethal crime rate is not rejected.     

Fitting Gaussian Markov Random Fields to Gaussian Fields

This paper discusses the following task often encountered in building Bayesian spatial models: construct a homogeneous Gaussian Markov random field (GMRF) on a lattice with correlation properties either as present in some observed data, or consistent with prior knowledge. The Markov property is essential in designing computationally efficient Markov chain Monte Carlo algorithms to analyse such models. We argue that we can restate both tasks as that of fitting a GMRF to a prescribed stationary Gaussian field on a lattice when both local and global properties are important. We demonstrate that using the KullbackLeibler discrepancy often fails for this task, giving severely undesirable behaviour of the correlation function for lags outside the neighbourhood. We propose a new criterion that resolves this difficulty, and demonstrate that GMRFs with small neighbourhoods can approximate Gaussian fields surprisingly well even with long correlation lengths. Finally, we discuss implications of our findings for likelihood based inference for general Markov random fields when global properties are also important.     

A framework for progressively improving small area population estimates

Summary.  The paper presents a framework for small area population estimation that enables users to select a method that is fit for the purpose. The adjustments to input data that are needed before use are outlined, with emphasis on developing consistent time series of inputs. We show how geographical harmonization of small areas, which is crucial to comparisons over time, can be achieved. For two study regions, the East of England and Yorkshire and the Humber, the differences in output and consequences of adopting different methods are illustrated. The paper concludes with a discussion of how data, on stream since 1998, might be included in future small area estimates.     

Semiparametric Spatial Autoregressive Models With Endogenous Regressors: With an Application to Crime Data

This study considers semiparametric spatial autoregressive models that allow for endogenous regressors, as well as the heterogenous effects of these regressors across spatial units. For the model estimation, we propose a semiparametric series generalized method of moments estimator. We establish that the proposed estimator is both consistent and asymptotically normal. As an empirical illustration, we apply the proposed model and method to Tokyo crime data to estimate how the existence of a neighborhood police substation (NPS) affects the household burglary rate. The results indicate that the presence of an NPS helps reduce household burglaries, and that the effects of some variables are heterogenous with respect to residential distribution patterns. Furthermore, we show that using a model that does not adjust for the endogeneity of NPS does not allow us to observe the significant relationship between NPS and the household burglary rate. Supplementary materials for this article are available online.     

Crime victimization: its extent and communication

Summary. The criminal justice contexts are identified in which understanding and communicating risks are important. The paper is thereafter exclusively concerned with the probability of crime victimization classified by person and location. Examples of crime risks derived from the British Crime Survey are provided, with instances of where concern about crime diverges from risks of crime. Caution is advocated about seeking to reduce the fear of crime by reassurance. The high levels of concentration of crime by location are noted, together with the fact that particular individuals and households are repeatedly victimized, enabling the prioritization of crime-reductive resources after a crime has taken place. The paper concludes that an emphasis on how the supply of criminal opportunities may be regulated heralds a more systematic approach to the distribution of unavoidable hazard among citizens.     

Identifying Different Areas of Inhomogenous Mineral Subsoil: Spatial Fluctuation Approaches

We use a recently proposed fluctuation-type procedure for detecting breaks in spatial regions to distinguish between hard and soft areas of inhomogeneous mineral subsoil like additives, air pockets, and adhesion. For a proper application, some refinements of the procedure are necessary. Both simulation evidence of the refinement and the application on the subsoil yield favorable results.     

地理约束、合作与劳动力知识分配空间偏好

经济高质量发展阶段,知识资源的空间再分配有着重要的现实意义。然而,在当前地理条件的约束下,对运输成本的改善将如何影响不同区位间技能劳动力跨空间知识合作行为,以及其在不同区位合作对象间进行知识分配的空间偏好及其倾向与策略变化,却鲜有研究。为此,本文首先构建了 一个纳入不同地理约束条件、运输成本变化以及基于异质性劳动力知识合作的区位选择模型;其中,城市间交通网络设施的改善降低了技能劳动力面对面交流的时间成本,提高了知识合作的匹配质量,从而促进了更远距离的知识合作及其绩效,同时伴随着知识配置从区域内部向外部的转变。另外,一个城市知识资源配置的空间偏好会随着该城市所达可交流市场量的增加而先递减再递增,并呈现知识资源集中分配趋势。进一步,本文利用2006-2015年的我国论文合作数据,基于多期双重差分法,估计了由高铁线路连通带来的劳动力运输成本的降低对知识资源空间配置行为的影响。本文的实证结果与理论模型相一致。     

Testing for Spatial Isotropy Under General Designs

Spatial modeling is typically composed of a specification of a mean function and a model for the correlation structure. A common assumption on the spatial correlation is that it is isotropic. This means that the correlation between any two observations depends only on the distance between those sites and not on their relative orientation. The assumption of isotropy is often made due to a simpler interpretation of correlation behavior and to an easier estimation problem under an assumed isotropy. The assumption of isotropy, however, can have serious deleterious effects when not appropriate. In this paper we formulate a test of isotropy for spatial observations located according to a general class of stochastic designs. Distribution theory of our test statistic is derived and we carry out extensive simulations which verify the efficacy of our approach. We apply our methodology to a data set on longleaf pine trees from an oldgrowth forest in the southern United States.     

The complexity of school and neighbourhood effects and movements of pupils on school differences in models of educational achievement

Summary.  Traditional studies of school differences in educational achievement use multilevel modelling techniques to take into account the nesting of pupils within schools. However, educational data are known to have more complex non-hierarchical structures. The potential importance of such structures is apparent when considering the effect of pupil mobility during secondary schooling on educational achievement. Movements of pupils between schools suggest that we should model pupils as belonging to the series of schools that are attended and not just their final school. Since these school moves are strongly linked to residential moves, it is important to explore additionally whether achievement is also affected by the history of neighbourhoods that are lived in. Using the national pupil database, this paper combines multiple membership and cross-classified multilevel models to explore simultaneously the relationships between secondary school, primary school, neighbourhood and educational achievement. The results show a negative relationship between pupil mobility and achievement, the strength of which depends greatly on the nature and timing of these moves. Accounting for pupil mobility also reveals that schools and neighbourhoods are more important than shown by previous analysis. A strong primary school effect appears to last long after a child has left that phase of schooling. The additional effect of neighbourhoods, in contrast, is small. Crucially, the rank order of school effects across all types of pupil is sensitive to whether we account for the complexity of the multilevel data structure.     

房地产价格区域间联动与泡沫的空间扩散 ——基于2000－2005年中国35个大中城市面板数据的实证检验

 利用中国35个大中城市2000－2005年间的面板数据，本文首先对房地产价格中的泡沫成分进行测度；然后构建泡沫自回归模型，并对其残差进行CSD检验，结果显示不同城市间房地产泡沫的演化过程相互影响。与前人研究不同，以房地产泡沫为研究对象，不仅可以确认中国不同城市间房地产价格存在联动性，而且可以证明消费者的适应性预期是其中重要的传导机制之一。     

A dimensionality reduction method of continuous dependent variables based supervised Laplacian eigenmaps

Dimensionality reduction is one of the important preprocessing steps in high-dimensional data analysis. In this paper we propose a supervised manifold learning method, it makes use of the information of continuous dependent variables to distinguish intrinsic neighbourhood and extrinsic neighbourhood of data samples, and construct two graphs according to these two kinds of neighbourhoods. Following the idea of Laplacian eigenmaps, we reveal that on the low-dimensional manifold the neighbourhood structure can be preserved or even improved. Our approach has two important characteristics: (i) it uses dependent variables to find an informative low-dimensional projection which is robust to noisy independent variables and (ii) the objective function simultaneously enlarges the distance between dissimilar samples and pushes similar samples close to each other according to the graph constructed with the help of continuous dependent variables. Our experiments demonstrate that the effectiveness of our method is over their traditional rivals.     

On Estimators of Scale and Dispersion in Neighbourhoods ofParametric Families

This paper investigates two estimators under the non-parametric neighbourhoods of an exponential scale parametric family. It uses the relative efficiency approach and shows that the tighter lower bounds on the relative efficiency of the upper trimmed mean to mean can be obtained under a sufficient condition. This condition gives the relationship between the possible positive lower bound and the degree of asymmetry of some related distributions. Similar arguments can be applied to the comparison of dispersion estimators under the neighbourhoods of a normal distribution.     

The influence of load on the lifetime distribution

This paper derives lifetime distributions for components that fail due to overloading. We introduce the notion of energy similarity. This makes it possible to use knowledge about how components behave under different loads in order to establish a probability model. To obtain parametric families, we consider two physically distinct cases: (1) components that function individually and (2) components that function as part of a system. The former leads to univariate and the latter to multivariate models. The results are particularly useful when little or no data is available.     

Bayesian Analysis of DNA Profiling Data in Forensic Identification Applications

The utilization of DNA evidence in cases of forensic identification has become widespread over the last few years. The strength of this evidence against an individual standing trial is typically presented in court in the form of a likelihood ratio (LR) or its reciprocal (the profile match probability). The value of this LR will vary according to the nature of the genetic relationship between the accused and other possible perpetrators of the crime in the population. This paper develops ideas and methods for analysing data and evaluating LRs when the evidence is based on short tandem repeat profiles, with special emphasis placed on a Bayesian approach. These are then applied in the context of a particular quadruplex profiling system used for routine case-work by the UK Forensic Science Service.     

On the asymptotic independence of numbers of observations near order statistics

In this paper, we consider the numbers of observations in two-sided neighbourhoods of the kth and (n?r)th order statistics from a sample of size n and show that they are asymptotically independent as n→∞. We also establish a result that generalizes all the existing results regarding the asymptotic independence of numbers of observations in the left and right neighbourhoods of order statistics. Finally, we consider the limiting joint behaviour of numbers of observations in the neighbourhoods of s central order statistics and establish that they are asymptotically independent.     

MIDAS Regressions: Further Results and New Directions

We explore mixed data sampling (henceforth MIDAS) regression models. The regressions involve time series data sampled at different frequencies. Volatility and related processes are our prime focus, though the regression method has wider applications in macroeconomics and finance, among other areas. The regressions combine recent developments regarding estimation of volatility and a not-so-recent literature on distributed lag models. We study various lag structures to parameterize parsimoniously the regressions and relate them to existing models. We also propose several new extensions of the MIDAS framework. The paper concludes with an empirical section where we provide further evidence and new results on the risk-return trade-off. We also report empirical evidence on microstructure noise and volatility forecasting.     

Analysis of the Covariance Structure in Manufactured Parts

We discuss the use of variograms for covariance modeling under the Kriging model to assess tolerances on manufactured parts. The variogram is very informative about the spatial dependence and it is favored by researchers in the choice of a correlation function. It may give evidence of anisotropy and of nugget effect too. In this paper, various variogram estimators from literature are used for comparing and evaluating their properties in assessing the surface error of three workpieces manufactured with different machining precisions. Contrary to the common engineering belief, the variograms give evidence of both technological signatures and systematic errors of the measurement machines.     

A model for estimating point sources in spatial data

We introduce a point source model which may be useful for estimating point sources in spatial data. It may also be useful for modelling general spatial data, and providing a simple explanatory model for some data, whilst in other cases it may give a parsimonious representation. The model assumes that there are point sources (or sinks), usually at unknown positions, and that the mean value at a site depends on the distance from these sources. We discuss the general form of the model, and some methods for estimating the sources and the regression parameters. We demonstrate the methodology using a simulation study, and apply the model to two real data sets. Some possibilities for further research are outlined.     

The relationship between crime, punishment and economic conditions: is reliable inference possible when crimes are under-recorded?

We investigate the estimation of dynamic models of criminal activity, when there is significant under-recording of crime. We give a theoretical analysis and use simulation techniques to investigate the resulting biases in conventional regression estimates. We find the biases to be of little practical significance. We develop and apply a new simulated maximum likelihood procedure that estimates simultaneously the measurement error and crime processes, using extraneous survey data. This also confirms that measurement error biases are small. Our estimation results for data from England and Wales imply a significant response of crime to both the economic and the enforcement environment.