scholarly journals A statistical model for microarrays, optimal estimation algorithms, and limits of performance

2006 ◽  
Vol 54 (6) ◽  
pp. 2444-2455 ◽  
Author(s):  
H. Vikalo ◽  
B. Hassibi ◽  
A. Hassibi
Keyword(s):  
Statistical Model ◽  
Optimal Estimation ◽  
Estimation Algorithms

scholarly journals Round-robin evaluation of nadir ozone profile retrievals: methodology and application to MetOp-A GOME-2

2014 ◽  
Vol 7 (11) ◽  
pp. 11481-11546 ◽  
Author(s):  
A. Keppens ◽  
J.-C. Lambert ◽  
J. Granville ◽  
G. Miles ◽  
R. Siddans ◽  
...  
Keyword(s):  
Information Content ◽  
Optimal Estimation ◽  
Round Robin ◽  
Atmospheric Composition ◽  
User Requirements ◽  
Satellite Measurements ◽  
Estimation Algorithms ◽  
Ozone Profile ◽  
Reference Measurements ◽  
Resolution Estimation

Abstract. A methodology for the round-robin evaluation and geophysical validation of ozone profile data retrieved from nadir UV backscatter satellite measurements is detailed and discussed, consisting of dataset content studies, information content studies, co-location studies, and comparisons with reference measurements. Within ESA's Climate Change Initiative on ozone (Ozone_cci project), the proposed round-robin procedure is applied to two nadir ozone profile datasets retrieved at KNMI and RAL, using their respective OPERA v1.26 and RAL v2.1 optimal estimation algorithms, from MetOp-A GOME-2 measurements taken in 2008. The ground-based comparisons use ozonesonde and lidar profiles as reference data, acquired by the Network for the Detection of Atmospheric Composition Change (NDACC), Southern Hemisphere Additional Ozonesonde programme (SHADOZ), and other stations of WMO's Global Atmosphere Watch. This direct illustration highlights practical issues that inevitably emerge from discrepancies in e.g. profile representation and vertical smoothing, for which different recipes are investigated and discussed. Several approaches for information content quantification, vertical resolution estimation, and reference profile resampling are compared and applied as well. The paper concludes with compliance estimates of the two GOME-2 ozone profile datasets with user requirements from GCOS and from climate modellers.

scholarly journals Self-Tuning Distributed Fusion Filter for Multi-Sensor Networked Systems with Unknown Packet Receiving Rates, Noise Variances, and Model Parameters

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4436 ◽  
Author(s):  
Wang ◽  
Sun
Keyword(s):  
Optimal Estimation ◽  
Original System ◽  
Identification Problem ◽  
Sensor Data ◽  
Model Parameters ◽  
Packet Dropouts ◽  
Estimation Algorithms ◽  
Self Tuning ◽  
Time Systems ◽  
Distributed Fusion

In this study, we researched the problem of self-tuning (ST) distributed fusion state estimation for multi-sensor networked stochastic linear discrete-time systems with unknown packet receiving rates, noise variances (NVs), and model parameters (MPs). Packet dropouts may occur when sensor data are sent to a local processor. A Bernoulli distributed stochastic variable is adopted to depict phenomena of packet dropouts. By model transformation, the identification problem of packet receiving rates is transformed into that of unknown MPs for a new augmented system. The recursive extended least squares (RELS) algorithm is used to simultaneously identify packet receiving rates and MPs in the original system. Then, a correlation function method is used to identify unknown NVs. Further, a ST distributed fusion state filter is achieved by applying identified packet receiving rates, NVs, and MPs to the corresponding optimal estimation algorithms. It is strictly proven that ST algorithms converge to optimal algorithms under the condition that the identifiers for parameters are consistent. Two examples verify the effectiveness of the proposed algorithms.

scholarly journals Statistical Model of Motor Evoked Potentials for Simulation of Transcranial Magnetic and Electric Stimulation

2018 ◽  
Author(s):  
Stefan M. Goetz ◽  
S. M. Madhi Alavi ◽  
Zhi-De Deng ◽  
Angel V. Peterchev
Keyword(s):  
Statistical Model ◽  
Evoked Potentials ◽  
Motor Evoked Potentials ◽  
Transcranial Stimulation ◽  
Large Variability ◽  
Dose Individualization ◽  
Estimation Algorithms ◽  
Amplitude Data ◽  
Virtual Population ◽  
Characteristic Features

AbstractMotor evoked potentials (MEPs) are widely used for biomarkers and dose individualization in transcranial stimulation. The large variability of MEPs requires sophisticated methods of analysis to extract information fast and correctly. However, models of MEPs that represent their characteristic features are lacking. This work presents a statistical model that can simulate long sequences of individualized MEP amplitude data with properties matching experimental observations. The MEP model includes three sources of trial-to-trial variability to mimic excitability fluctuations, variability in the neural and muscular pathways, and physiological and measurement noise. It also generates virtual human subject data from statistics of population variability. All parameters are extracted as statistical distributions from experimental data from the literature. The model exhibits previously described features, such as stimulusintensity-dependent MEP amplitude distributions, including bimodal ones. The model can generate long sequences of test data for individual subjects with specified parameters or for subjects from a virtual population. The presented MEP model is the most detailed to date and can be used for the development and implementation of dosing and biomarker estimation algorithms for transcranial stimulation.

On-Line Performance Monitoring With Signal Validation and Uncertainty Estimation

1989 ◽  
Vol 111 (4) ◽  
pp. 694-696
Author(s):  
Geun-Sun Auh
Keyword(s):  
Performance Monitoring ◽  
Detection Method ◽  
Measurement Data ◽  
Optimal Estimation ◽  
Uncertainty Estimation ◽  
Estimation Algorithms ◽  
Signal Validation ◽  
New Information ◽  
On Line ◽  
Plant Components

Methods are developed for performance monitoring of power plant components. On-line uncertainty estimation algorithms are developed. Since this can give new information about the validity of the measurements, proper use of performance monitoring can be achieved. A sequential fault detection method is introduced for the detection of small faults. This signal validation program gives an additional check for good inputs to the performance monitoring. Low-order models are solved using optimal estimation theory to get analytic measurements. The above algorithms are applied to heat transfer loops with real measurement data.

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