Chapter 16: Analysis of Non-Gaussian Data

2018 ◽  
Author(s):  
Walter W. Stroup ◽  
Glaz Barry ◽  
Kathleen M. Yeater
Keyword(s):  
Non Gaussian ◽  
Gaussian Data

scholarly journals Cluster Sampling Filters for Non-Gaussian Data Assimilation

Atmosphere ◽  
2018 ◽  
Vol 9 (6) ◽  
pp. 213 ◽  
Author(s):  
Ahmed Attia ◽  
Azam Moosavi ◽  
Adrian Sandu
Keyword(s):  
Data Assimilation ◽  
Cluster Sampling ◽  
Non Gaussian ◽  
Gaussian Data

scholarly journals A Robust Non-Gaussian Data Assimilation Method for Highly Non-Linear Models

Atmosphere ◽  
2018 ◽  
Vol 9 (4) ◽  
pp. 126 ◽  
Author(s):  
Elias Nino-Ruiz ◽  
Haiyan Cheng ◽  
Rolando Beltran
Keyword(s):  
Data Assimilation ◽  
Linear Models ◽  
Non Linear ◽  
Non Gaussian ◽  
Gaussian Data

scholarly journals PCA Consistency for Non-Gaussian Data in High Dimension, Low Sample Size Context

2009 ◽  
Vol 38 (16-17) ◽  
pp. 2634-2652 ◽  
Author(s):  
Kazuyoshi Yata ◽  
Makoto Aoshima
Keyword(s):  
Sample Size ◽  
High Dimension ◽  
Non Gaussian ◽  
Gaussian Data

scholarly journals Predictive Inference for Big, Spatial, Non-Gaussian Data: MODIS Cloud Data and its Change-of-Support

2016 ◽  
Vol 58 (1) ◽  
pp. 15-45 ◽  
Author(s):  
Aritra Sengupta ◽  
Noel Cressie ◽  
Brian H. Kahn ◽  
Richard Frey
Keyword(s):  
Predictive Inference ◽  
Change Of Support ◽  
Cloud Data ◽  
Non Gaussian ◽  
Gaussian Data

scholarly journals Ising Model for Interpolation of Spatial Data on Regular Grids

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1270
Author(s):  
Milan Žukovič ◽  
Dionissios T. Hristopulos
Keyword(s):  
Ising Model ◽  
Spatial Data ◽  
Nearest Neighbor ◽  
Correlated Data ◽  
Continuous Variables ◽  
Regression Problem ◽  
Spatially Correlated ◽  
Regular Grids ◽  
Non Gaussian ◽  
Gaussian Data

We apply the Ising model with nearest-neighbor correlations (INNC) in the problem of interpolation of spatially correlated data on regular grids. The correlations are captured by short-range interactions between “Ising spins”. The INNC algorithm can be used with label data (classification) as well as discrete and continuous real-valued data (regression). In the regression problem, INNC approximates continuous variables by means of a user-specified number of classes. INNC predicts the class identity at unmeasured points by using the Monte Carlo simulation conditioned on the observed data (partial sample). The algorithm locally respects the sample values and globally aims to minimize the deviation between an energy measure of the partial sample and that of the entire grid. INNC is non-parametric and, thus, is suitable for non-Gaussian data. The method is found to be very competitive with respect to interpolation accuracy and computational efficiency compared to some standard methods. Thus, this method provides a useful tool for filling gaps in gridded data such as satellite images.

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