Semiparametric estimation of mean and variance functions for non-Gaussian data

2006 ◽  
Vol 21 (3-4) ◽  
pp. 603-620 ◽  
Author(s):  
David Nott
Keyword(s):  
Semiparametric Estimation ◽  
Variance Functions ◽  
Mean And Variance ◽  
Non Gaussian ◽  
Gaussian Data

scholarly journals A Moment Matching Ensemble Filter for Nonlinear Non-Gaussian Data Assimilation

2011 ◽  
Vol 139 (12) ◽  
pp. 3964-3973 ◽  
Author(s):  
Jing Lei ◽  
Peter Bickel
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Ensemble Forecasting ◽  
Moment Matching ◽  
State Variables ◽  
Mean And Variance ◽  
Large Systems ◽  
Non Gaussian ◽  
Lorenz 96 ◽  
Gaussian Data

Abstract The ensemble Kalman filter is now an important component of ensemble forecasting. While using the linear relationship between the observation and state variables makes it applicable for large systems, relying on linearity introduces nonnegligible bias since the true distribution will never be Gaussian. This paper analyzes the bias of the ensemble Kalman filter from a statistical perspective and proposes a debiasing method called the nonlinear ensemble adjustment filter. This new filter transforms the forecast ensemble in a statistically principled manner so that the updated ensemble has the desired mean and variance. It is also easily localizable and, hence, potentially useful for large systems. Its performance is demonstrated and compared with other Kalman filter and particle filter variants through various experiments on the Lorenz-63 and Lorenz-96 systems. The results show that the new filter is stable and accurate for challenging situations such as nonlinear, high-dimensional systems with sparse observations.

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
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