A Casebook for Spatial Statistical Data Analysis
نویسنده
چکیده
منابع مشابه
Determination of Optimal Sampling Design for Spatial Data Analysis
Extended Abstract. Inferences for spatial data are affected substantially by the spatial configuration of the network of sites where measurements are taken. Consider the following standard data-model framework for spatial data. Suppose a continuous, spatially-varying quantity, Z, is to be observed at a predetermined number, n, of points ....[ To Countinue Click here]
متن کاملSensitivity Analysis of Spatial Sampling Designs for Optimal Prediction
In spatial statistic, the data analyzed which is correlated and this correlation is due to their locations in the studied region. Such correlation that is related to distance between observations is called spatial correlation. Usually in spatial data analysis, the prediction of the amount of uncertain quantity in arbitrary 4locations of the area is considered according to attained observations ...
متن کاملAnalysis of Spatial Imbalance Associated with Rural Settlements in Iran
Spatial distributions of rural settlements in Iran represent an imbalanced nature. The major objective of this study is to investigate the spatial patterns of Iranian rural settlements using certain indicators and indices .It further tries to propose a model regarding the analysis of spatial imbalances. This study further supported by application of modifiable areal unit problem(MAUP) suitable ...
متن کاملBayesian Analysis of Censored Spatial Data Based on a Non-Gaussian Model
Abstract: In this paper, we suggest using a skew Gaussian-log Gaussian model for the analysis of spatial censored data from a Bayesian point of view. This approach furnishes an extension of the skew log Gaussian model to accommodate to both skewness and heavy tails and also censored data. All of the characteristics mentioned are three pervasive features of spatial data. We utilize data augme...
متن کاملSpatial Design for Knot Selection in Knot-Based Low-Rank Models
Analysis of large geostatistical data sets, usually, entail the expensive matrix computations. This problem creates challenges in implementing statistical inferences of traditional Bayesian models. In addition,researchers often face with multiple spatial data sets with complex spatial dependence structures that their analysis is difficult. This is a problem for MCMC sampling algorith...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Technometrics
دوره 43 شماره
صفحات -
تاریخ انتشار 2001