Discussion on Competition for Spatial Statistics for Large Datasets

Author(s):  
Yasumasa Matsuda
Author(s):  
Roman Flury ◽  
Reinhard Furrer

AbstractWe discuss the experiences and results of the AppStatUZH team’s participation in the comprehensive and unbiased comparison of different spatial approximations conducted in the Competition for Spatial Statistics for Large Datasets. In each of the different sub-competitions, we estimated parameters of the covariance model based on a likelihood function and predicted missing observations with simple kriging. We approximated the covariance model either with covariance tapering or a compactly supported Wendland covariance function.


Author(s):  
Huang Huang ◽  
Sameh Abdulah ◽  
Ying Sun ◽  
Hatem Ltaief ◽  
David E. Keyes ◽  
...  

Author(s):  
Lewis R. Blake ◽  
Olga Khaliukova ◽  
Alexander Pinard ◽  
Douglas Nychka ◽  
Dorit Hammerling ◽  
...  

Author(s):  
Quan Vu ◽  
Yi Cao ◽  
Josh Jacobson ◽  
Alan R. Pearse ◽  
Andrew Zammit-Mangion

2017 ◽  
Vol 25 (2) ◽  
pp. 927-960
Author(s):  
Jarod Jacobs

In this article, I discuss three statistical tools that have proven pivotal in linguistic research, particularly those studies that seek to evaluate large datasets. These tools are the Gaussian Curve, significance tests, and hierarchical clustering. I present a brief description of these tools and their general uses. Then, I apply them to an analysis of the variations between the “biblical” DSS and our other witnesses, focusing upon variations involving particles. Finally, I engage the recent debate surrounding the diachronic study of Biblical Hebrew. This article serves a dual function. First, it presents statistical tools that are useful for many linguistic studies. Second, it develops an analysis of the he-locale, as it is used in the “biblical” Dead Sea Scrolls, Masoretic Text, and Samaritan Pentateuch. Through that analysis, this article highlights the value of inferential statistical tools as we attempt to better understand the Hebrew of our ancient witnesses.


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