Statistical analysis of the sea surface backscattered field from Monte Carlo simulations

2012 ◽  
Author(s):  
N. Pinel ◽  
C. Bourlier ◽  
B. Chapron ◽  
N. de Beaucoudrey ◽  
A. Ghaleb ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Statistical Analysis ◽  
Monte Carlo Simulations ◽  
Sea Surface

scholarly journals Monte Carlo simulations of the microwave emissivity of the sea surface

1998 ◽  
Vol 103 (C11) ◽  
pp. 24983-24989 ◽  
Author(s):  
Quanhua Liu ◽  
Clemens Simmer ◽  
Eberhard Ruprecht
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Sea Surface ◽  
Microwave Emissivity

Statistical Analysis of Real Aperture Radar Field Backscattered From Sea Surfaces Under Moderate Winds by Monte Carlo Simulations

2014 ◽  
Vol 52 (10) ◽  
pp. 6459-6470 ◽  
Author(s):  
Nicolas Pinel ◽  
Bertrand Chapron ◽  
Christophe Bourlier ◽  
Nicole de Beaucoudrey ◽  
Rene Garello ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Statistical Analysis ◽  
Monte Carlo Simulations ◽  
Real Aperture Radar ◽  
Aperture Radar

scholarly journals An analysis of a presumed major outbreak of pseudorabies virus in a vaccinated sow herd

2001 ◽  
Vol 126 (1) ◽  
pp. 119-128
Author(s):  
A. VAN NES ◽  
M. C. M. DE JONG ◽  
A. J. KERSTEN ◽  
T. G. KIMMAN ◽  
J. H. M. VERHEIJDEN
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Statistical Analysis ◽  
Monte Carlo Simulations ◽  
Mathematical Analysis ◽  
Generalized Linear Model ◽  
Pseudorabies Virus ◽  
Reproduction Ratio ◽  
Multiple Introductions ◽  
Major Outbreak

We describe a major outbreak of pseudorabies virus (PRV) in a sow herd in which the sows were vaccinated simultaneously three times a year with a vaccine containing Bartha strain. Also in the associated rearing herd in which the gilts were vaccinated twice an outbreak of PRV occurred. The outbreak was analysed with mathematical models, statistical methods and Monte-Carlo simulation. Under the assumption that the outbreak started with one introduction of virus the reproduction ratio Rind – as a measure of transmission of PRV between individuals – in the sow herd was estimated with a Generalized Linear Model to be 1·6. Also under the assumption of one introduction of virus Rind in the rearing herd was estimated with a martingale estimator to be 1·7. Both estimates were significantly larger than 1. Mathematical analysis showed that heterogeneity in the sow herd, because of the presence of not-optimally immunized replacement sows could not be the only cause of the observed outbreak in the sow herd. With Monte-Carlo simulations, the duration of an outbreak after a single introduction of virus and Rind = 1·6 did not mimic the data and thus the hypothesis of a single introduction with Rind = 1·6 could also be rejected and Rind is thus, not necessarily above 1. Moreover, with statistical analysis, endemicity in the combination of herds as a cause for the observed outbreak could be rejected. Endemicity in the rearing herd alone could not be excluded. Therefore, multiple introductions from outside and most probably from the rearing herd were possibly the cause of the observed outbreak(s). The implications for eradication of pseudorabies virus were discussed.

scholarly journals Quick-MESS: A fast statistical tool for Exoplanet Imaging Surveys

2013 ◽  
Vol 8 (S299) ◽  
pp. 28-29
Author(s):  
Mariangela Bonavita ◽  
Ernst De Mooij ◽  
Ray Jayawardhana ◽  
Raffaele Gratton
Keyword(s):  
Monte Carlo ◽  
Statistical Analysis ◽  
Monte Carlo Simulations ◽  
Bayesian Approach ◽  
Direct Imaging ◽  
Statistical Tool ◽  
Novel Approach

AbstractSeveral tools have been developed for the analysis of the results of direct imaging exoplanet surveys, mostly using a combination of Monte-Carlo simulations or a Bayesian approach. Here we present a novel approach to the statistical analysis of Direct Imaging surveys, called Quick-MESS, which allows for a much faster and flexible analysis.

scholarly journals Logistic regression applied to natural hazards: rare event logistic regression with replications

2012 ◽  
Vol 12 (6) ◽  
pp. 1937-1947 ◽  
Author(s):  
M. Guns ◽  
V. Vanacker
Keyword(s):  
Monte Carlo ◽  
Logistic Regression ◽  
Statistical Analysis ◽  
Variable Selection ◽  
Monte Carlo Simulations ◽  
Natural Hazards ◽  
Statistical Methods ◽  
Rare Event ◽  
Controlling Factors ◽  
Robust Detection

Abstract. Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.

Statistical Analysis and Yield Enhancement of MEMS Devices by Considering Multi-Process Variations

2015 ◽  
Vol 645-646 ◽  
pp. 589-594
Author(s):  
Li Li Gao ◽  
Wei Hua Li ◽  
Qing An Huang
Keyword(s):  
Monte Carlo ◽  
Statistical Analysis ◽  
Monte Carlo Simulations ◽  
Process Variations ◽  
Yield Enhancement ◽  
Mems Devices ◽  
Comb Drive ◽  
Performance Error

A methodology is developed statistically to make MEMS devices robust to process variations to improve manufacturability and yield. Two approaches are applied to discuss the effects of multi-process variations. Comparisons have been made between the proposed method and Monte Carlo simulations, which confirm the robustness of the proposed one with performance error less than 4%. Experiments on beams and comb-drive resonator verified the effectiveness of the methodology and it is useful for practical device designs to be more robust to process variations and yield enhancement realization.

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