Properties of Spatial Cox Process Models

Smoothness Properties and Gradient Analysis Under Spatial Dirichlet Process Models

When analyzing point-referenced spatial data, interest will be in the first order or global behavior of associated surfaces. However, in order to better understand these surfaces, we may also be interested in second order or local behavior, e.g., in the rate of change of a spatial surface at a given location in a given direction. In a Bayesian parametric setting, such smoothness analysis has be...

Multivariate Spatial Process Models

Generalized Spatial Dirichlet Process Models

By JASON A. DUAN Institute of Statistics and Decision Sciences at Duke University, Durham, North Carolina, 27708-0251, U.S.A. [email protected] MICHELE GUINDANI Istituto di Metodi Quantitativi, Università Bocconi, 20136, Milano, Italy [email protected] and ALAN E. GELFAND Institute of Statistics and Decision Sciences at Duke University, Durham, North Carolina, 27708-0251, U.S.A. ...

Gaussian Process Models of Spatial Aggregation Algorithms

Multi-level spatial aggregates are important for data mining in a variety of scientific and engineering applications, from analysis of weather data (aggregating temperature and pressure data into ridges and fronts) to performance analysis of wireless systems (aggregating simulation results into configuration space regions exhibiting particular performance characteristics). In many of these appl...

Cox and Frailty Models for Analysis of Esophageal Cancer Data‎

‎By existing censor and skewness in survival data‎, ‎some models such as weibull are used to analyzing survival data‎. ‎In addition, parametric and semiparametric models can be obtained from baseline hazard function of Cox model to fit to survival data‎. ‎However these models are popular because of their simple usage but do not consider unknown risk factors‎, ‎that's why cannot introduce the be...

Applying Spatial Geostatistical Analysis Models for Evaluating Variability of Soil Properties in Eastern Shiraz, Iran

ABSTRACT- The information on the spatial properties of soil is vital to improve soil management and to increase the crop productivity. Geostatistical analysis technique is one of the most important methods for determining the spatial properties of soil. The aim of this study was to investigate spatial variability of soil chemical and physical attributes for field management in eastern Shiraz, I...