Clustering Spatial Functional Data: A Method Based on a Nonparametric Variogram Estimation
نویسندگان
چکیده
In this paper we propose an extended version of a model-based strategy for clustering spatial functional data. The strategy, we refer, aims simultaneously to classify spatially dependent curves and to obtain a spatial functional model prototype for each cluster. The fit of these models implies to estimate a variogram function, the trace variogram function. Our proposal is to introduce an alternative estimator for the trace-variogram function: a kernel variogram estimator. This works better to adapt spatial varying features of the functional data pattern. Experimental comparisons show this approach has some advantages over the previous one.
منابع مشابه
Nonparametric Estimation of Spatial Risk for a Mean Nonstationary Random Field}
The common methods for spatial risk estimation are investigated for a stationary random field. Because of simplifying, lets distribution is known, and parametric variogram for the random field are considered. In this paper, we study a nonparametric spatial method for spatial risk. In this method, we model the random field trend by a local linear estimator, and through bias-corrected residuals, ...
متن کاملVariogram Model Selection via Nonparametric Derivative Estimation
Before optimal linear prediction can be performed on spatial data sets, the variogram is usually estimated at various lags and a parametric model is fitted to those estimates. Apart from possible a priori knowledge about the process and the user’s subjectivity, there is no standard methodology for choosing among valid variogram models like the spherical or the exponential ones. This paper discu...
متن کاملGrade estimation of Zu2 Jajarm deposit by considering imprecise variogram model parameters based on the extension principle
Nowadays, kriging has been accepted as the most common method of grade estimation in mineral resource evaluation stage. Access to the crisp assay data and a variogram model are the necessary means for the utilization of this method. Since fitting a crisp variogram model is generally difficult, if not impossible, the fitted theoretical model is usually tainted with uncertainty due to various rea...
متن کاملAnalysis of Rainfall Data by Robust Spatial Statistics using S+SPATIALSTATS
This paper discusses the use of robust geostatistical methods on a data set of rainfall measurements for Switzerland. The variables are detrended via nonparametric estimation penalized with a smoothing parameter. The optimal trend is computed with a smoothing parameter based on cross-validation. The variogram is then estimated by a highly robust estimator of scale. The parametric variogram mode...
متن کاملGender-based Differences in Associations between Attitude and Self-esteem with Smoking Behavior among Adolescents: A Secondary Analysis Applying Bayesian Nonparametric Functional Latent Variable Model
Background: Different patterns of gender-based relationships between attitude toward smoking and self-esteem with smoking behavior have reported. However, such associations may be much more complex than a simply supposed linear relationship. We aimed to propose a method of providing hand details on the total and gender-based scenarios of the relationships between attitude toward smoking and sel...
متن کامل