Modelling map positional error to infer true feature location

The authors consider the issue of map positional error, or the difference between location as represented in a spatial database (i.e., a map) and the corresponding unobservable true location. They propose a fully model‐based approach that incorporates aspects of the map registration process commonly performed by users of geographic informations systems, including rubber‐sheeting. They explain how estimates of positional error can be obtained, hence estimates of true location. They show that with multiple maps of varying accuracy along with ground truthing data, suitable model averaging offers a strategy for using all of the maps to learn about true location.     

Bivariate deconvolution with SIMEX: an application to mapping Alaska earthquake density

Constructing spatial density maps of seismic events, such as earthquake hypocentres, is complicated by the fact that events are not located precisely. In this paper, we present a method for estimating density maps from event locations that are measured with error. The estimator is based on the simulation–extrapolation method of estimation and is appropriate for location errors that are either homoscedastic or heteroscedastic. A simulation study shows that the estimator outperforms the standard estimator of density that ignores location errors in the data, even when location errors are spatially dependent. We apply our method to construct an estimated density map of earthquake hypocenters using data from the Alaska earthquake catalogue.     

Curved Chaotic Map Time Series Models and Their Stochastic Reversals

This paper considers two types of chaotic map time series models, including the well-known tent, logistic and binary-shift maps as special cases; these are called curved tent and curved binary families. Deterministic behaviour is investigated by invariant distributions, Lyapunov exponents, and by serial dependency. Stochastic time reversal of the families is shown to produce models which have a broader range of stochastic and chaotic properties than their deterministic counterparts. The marginal distributions may have concentrations and restricted supports and are shown to be a non-standard class of invariant distribution. Dependenc y is generally weaker with the reversed stochastic models. The work gives a broad statistical account of deterministic and stochastically reversed map models, such as are emerging in random number generation, communica tion systems and cryptography     

A Short Note on the Dependence Structure of a Stochastic Process Generated by the Asymmetric Cusp Map

Intermittency maps are well-known to be capable of generating stochastic processes with slowly decaying cross covariances. The present paper considers the asymmetric cusp map and derives an asymptotic lower bound of the autocovariance function of a stochastic process generated by this map. As a consequence, such a stochastic process is shown to belong to the class of long memory processes.     

Spatial smoothing of low birth weight rate in Bangladesh using Bayesian hierarchical model

The term low birth weight refers an event where a newborn baby has a weight that is less than 2500?g. This is an essential indicator while the interest is in public health issues such as infant mortality, maternal complications, and antenatal care, etc. of a country, particularly, for a developing country like Bangladesh. The regional development programs are in the current priority list of Bangladesh government and other policy makers. Many of such regional development programs may need the spatial distribution of relative risk for low birth weight that can be obtained by mapping the risks over small area domains like the districts of Bangladesh. This study aims to find whether is there any spatial dependence among the relative risks of low birth weight for the districts of Bangladesh. This has been investigated using Moran's I statistic and a significant spatial dependence in the relative risks was found. Then, attempt has been made to rediscover the spatial distribution based on the idea of spatial smoothing. A Bayesian hierarchical model is used considering percent received antenatal care and female labor force participation as covariates to smooth the observed relative risks of low birth weight in 64 districts of Bangladesh. Revised spatial distribution taking the spatial dependence under consideration through intrinsic conditional autoregressive model is derived and showed in choropleth map along with its different behaviors.     

Various Approaches for Predicting Land Cover in Mountain Areas

Using former maps, geographers intend to study the evolution of the land cover in order to have a prospective approach on the future landscape; predictions of the future land cover, by the use of older maps and environmental variables, are usually done through the GIS (Geographic Information System). We propose here to confront this classical geographical approach with statistical approaches: a linear parametric model (polychotomous regression modeling) and a nonparametric one (multilayer perceptron). These methodologies have been tested on two real areas on which the land cover is known at various dates; this allows us to emphasize the benefit of these two statistical approaches compared to GIS and to discuss the way GIS could be improved by the use of statistical models.     

统计数据质量研究新思路——误差研究

统计数据质量问题一直困扰着我国统计界 ,也为社会各界所关注。统计数据真正的重要性在于影响人们对社会经济现象的认识和判断。对当前我国统计数据质量的状况 ,据权威的判断是“宏观统计数据基本可靠 ,从总体看 ,宏观统计数据客观地反映了经济运行的趋势和变化 ,未发生趋势性差错” ;“有些原始数据质量问题较多 ,一些地方原始数据①　国家统计局局长刘洪在全国统计工作会议上的讲话 ,1998 1 7。质量差 ,甚至有下降趋势。”①我国的统计数据质量问题一直存在 ,统计界的行政领导也一直非常重视 ,理论上的研究探讨也始终保持热点 ,但研究的重…     

Fisher's Hypergeometric Test for a Comparison in a Finite Population

Graphical methods have played a central role in the development of statistical theory and practice. This presentation briefly reviews some of the highlights in the historical development of statistical graphics and gives a simple taxonomy that can be used to characterize the current use of graphical methods. This taxonomy is used to describe the evolution of the use of graphics in some major statistical and related scientific journals.

Some recent advances in the use of graphical methods for statistical analysis are reviewed, and several graphical methods for the statistical presentation of data are illustrated, including the use of multicolor maps.     

A New Method for Statistical Multidimensional Unfolding

ABSTRACT

Consider the problem of estimating the positions of a set of targets in a multidimensional Euclidean space from distances reported by a number of observers when the observers do not know their own positions in the space. Each observer reports the distance from the observer to each target plus a random error. This statistical problem is the basic model for the various forms of what is called multidimensional unfolding in the psychometric literature. Multidimensional unfolding methodology as developed in the field of cognitive psychology is basically a statistical estimation problem where the data structure is a set of measures that are monotonic functions of Euclidean distances between a number of observers and targets in a multidimensional space. The new method presented in this article deals with estimating the target locations and the observer positions when the observations are functions of the squared distances between observers and targets observed with an additive random error in a two-dimensional space. The method provides robust estimates of the target locations in a multidimensional space for the parametric structure of the data generating model presented in the article. The method also yields estimates of the orientation of the coordinate system and the mean and variances of the observer locations. The mean and the variances are not estimated by standard unfolding methods which yield targets maps that are invariant to a rotation of the coordinate system. The data is transformed so that the nonlinearity due to the squared observer locations is removed. The sampling properties of the estimates are derived from the asymptotic variances of the additive errors of a maximum likelihood factor analysis of the sample covariance matrix of the transformed data augmented with bootstrapping. The robustness of the new method is tested using artificial data. The method is applied to a 2001 survey data set from Turkey to provide a real data example.     

Spatial discrimination and classification maps

A method of constructing maps through spatial discrimination is given. The discrimination depends basically on the assumption of local spatial continuity, and a factorized covariance matrix. Given an autocovariance function, this formulation in particular, leads to a deeper insight into the pioneering work of Switzer (1980). Certain windows for the maps are examined, and choice of window size is discussed in relation to the classification error when the variables are dependent versus independent. When a training data is given, we give a method of estimating the parameters in the model. Some numerical examples are also given.     

Spatial Modeling of Habitat Preferences of Biological Species using Markov Random Fields

Spatial modeling has gained interest in ecology during the past two decades, especially in the area of biodiversity, where reliable distribution maps are required. Several methods have been proposed to construct distribution maps, most of them acknowledging the presence of spatial interactions. In many cases, a key problem is the lack of true absence data. We present here a model suitable for use when true absence data are missing. The quality of the estimates obtained from the model is evaluated using ROC curve analysis as well as a quadratic cost function, computed from the false positive and false negative error rates. The model is also tested under random and clustered scattering of the presence records. We also present an application of the model to the construction of distribution maps of two endemic bird species in México.     

On the bias and variance of some proportion estimators

Providing certain parameters are known, almost any linear map from R^P to R¹ can be adjusted to yield a consistent and unbiased estimator in the context of estimating the mixing proportion θ on the basis of an unclassified sample of observations taken from a mixture of two p-dimensional distributions in proportions θ and 1-θ. Attention is focused on an estimator proposed recently, θ, which has minimum variance over all such linear maps. Unfortunately, the form of θ depends on the means of the component distributions and the covariance matrix of the mixture distribution. The effect of using appropriate sample estimates for these unknown parameters in forming θ is investigated by deriving the asymptotic mean and variance of the resulting estimator. The relative efficiency of this estimator under normality is derived. Also, a study is undertaken of the performance of a similar type of estimator appropriate in the context where an observed data vector is not an observation from either one or the other onent distributions, but is recorded as an integrated measurement over a surface area which is a mixture of two categories whose characteristics have different statistical distributions.The asymptotic bias in this case is compared with some available practical results.     

Quality control procedures at statistics sweden

This paper treats quality control in a wide sense which is imperative for a statistical agencyto do. First, we discuss the meaning of total survey error and different quality characteris-

tics. Secondly, some common quality problems in statistical agencies are presented as a prelude to the bulk of this paper, namely the case of Sweden. Quality control efforts at Statistics Sweden are reviewed regarding the handling of specific error sources, error estimation, and error control. Different tools such as preventive control, production control, and evaluation are dealt with. As for QC-procedures proper we provide some applications from clerical coding.     

Effect of length-biased sampling on the modeling error

In statistical data analysis, the choice of an appropriate model is a very important factor. An inappropriate model leads to a different kind of error in the analysis. This error has been called by C. R. Rao as type III error or modeling error as opposed to type I and type II errors in statistical inference.In This paper we Study the relative errors in Incurred by Erroneously Assuming the Distribution of the Family Size N as P(n) While in fact it is the Length-biased (Weighted) Version of P(n).An Analytical Expression for the Relative Error,When the Distribution of N Belongs to the Class of Modified Power Series Distributions, is Derived. More Specifically, the Effect of length-biasing on the Relative Error is Investigated, When N Follows a Generalized Poisson Distribution. These Results are Compared With the Case When N Follows a Poisson Distribution.     

Geostatistical analysis of disease data: a case study of tuberculosis incidence in Iran

The main objective of this study is to introduce two advanced statistical methods and to consider geographical distribution of tuberculosis incidence in Iran. With the knowledge that environmental and climatic conditions in each region are affective for the incidence and spread of the disease, the study has been taken into consideration. The disease incidences in different counties are realizations of spatial data, therefore we apply the Poisson kriging and ordinary kriging for prediction of tuberculosis incidence rates map in Iran. To identify high risk areas using statistical map of disease, our results show that tuberculosis incidences are not uniformly distributed in whole of the country and estimated risk is high in the eastern parts. Assessing geographical distribution of a disease is essential for health officials to recognize high-risk areas, and improve case management and resource allocation.     

统计调查中回答误差的计量

统计调查中回答误差的计量赵俊康ＡＢＳＴＲＡＣＴＩｔｉｓａｖｅｒｙｉｍｐｏｒｔａｎｔｐｒｏｂｌｅｍｄｅｍａｎｄｉｎｇｐｒｏｍｐｔｓｏｌｕｔｉｏｎｔｈａｔｈｏｗｔｏｅｓｔｉｍａｔｅｒｅｓｐｏｎｓｅｅｒｏｒｉｎｓｔａｔｉｓｔｉｃａｌｓｕｒｖｅｙｓ．Ｔｈｉｓ...     

Establishing an Office of Scientific and Public Affairs

Procedures for high-interaction two-variable color mapping are described, whereby the user interacts with a graphic display to produce a color statistical map in a few seconds. The approach emphasizes the methodological benefits derived from the ability to examine the nature of the linkage between the statistical and spatial distributions of bivariate data. A series of examples illustrate this method.     

CMB data analysis and sparsity

The statistical analysis of the soon to come Planck satellite CMB data will help set tighter bounds on major cosmological parameters. On the way, a number of practical difficulties need to be tackled, notably that several other astrophysical sources emit radiation in the frequency range of CMB observations. Some level of residual contributions, most significantly in the galactic region and at the locations of strong radio point sources will unavoidably contaminate the estimated spherical CMB map. Masking out these regions is common practice but the gaps in the data need proper handling. In order to restore the stationarity of a partly incomplete CMB map and thus lower the impact of the gaps on non-local statistical tests, we developed an inpainting algorithm on the sphere based on a sparse representation of the data, to fill in and interpolate across the masked regions.