An evaluation of the multivariate dispersion charts with estimated parameters under non-normality

2017 ◽  
Vol 33 (6) ◽  
pp. 694-716
Author(s):  
A. Mostajeran ◽  
N. Iranpanah ◽  
R. Noorossana
Keyword(s):  
Estimated Parameters ◽  
Multivariate Dispersion

Fast Inverse Tone Mapping Based on Reinhard's Global Operator with Estimated Parameters

2017 ◽  
Vol E100.A (11) ◽  
pp. 2248-2255 ◽  
Author(s):  
Yuma KINOSHITA ◽  
Sayaka SHIOTA ◽  
Hitoshi KIYA
Keyword(s):  
Tone Mapping ◽  
Estimated Parameters ◽  
Global Operator ◽  
Inverse Tone Mapping

scholarly journals Remote Sensing Methods for the Retrieval of Inventory and Bioproductivity Parameters of Forests Using High Resolution Satellite Images

2019 ◽  
Vol 75 ◽  
pp. 01003 ◽  
Author(s):  
Egor Dmitriev ◽  
Vladimir Kozoderov ◽  
Sergey Donskoy ◽  
Petr Melnik ◽  
Anton Sokolov
Keyword(s):  
Satellite Images ◽  
High Spatial Resolution ◽  
Texture Features ◽  
Forest Species ◽  
Effective Learning ◽  
Estimated Parameters ◽  
High Resolution Satellite Images ◽  
Automated Processing ◽  
Tver Region ◽  
Remote Sensing Methods

A method for automated processing high spatial resolution satellite images is proposed to retrieve inventory and bioproductivity parameters of forest stands. The method includes effective learning classifiers, inverse modeling, and regression modeling of the estimated parameters. Spectral and texture features are used to classify forest species. The results of test experiments for the selected area of Savvatievskoe forestry (Russia, Tver region) are presented. Accuracy estimates obtained using ground-based measurements demonstrate the effectiveness of using the proposed techniques to automate the process of updating information for the State Forest Inventory program of Russia.

Effects of incomplete parameterization on full-wavefield viscoelastic seismic data for petrophysical reservoir properties

Geophysics ◽  
2007 ◽  
Vol 72 (3) ◽  
pp. O9-O17 ◽  
Author(s):  
Upendra K. Tiwari ◽  
George A. McMechan
Keyword(s):  
Seismic Data ◽  
Input Data ◽  
Elastic Model ◽  
Quality Factors ◽  
Noise Levels ◽  
Reservoir Properties ◽  
Estimated Parameters ◽  
Model Parameterization ◽  
The Earth ◽  
Incomplete Model

In inversion of viscoelastic full-wavefield seismic data, the choice of model parameterization influences the uncertainties and biases in estimating seismic and petrophysical parameters. Using an incomplete model parameterization results in solutions in which the effects of missing parameters are attributed erroneously to the parameters that are included. Incompleteness in this context means assuming the earth is elastic rather than viscoelastic. The inclusion of compressional and shear-wave quality factors [Formula: see text] and [Formula: see text] in inversion gives better estimates of reservoir properties than the less complete (elastic) model parameterization. [Formula: see text] and [Formula: see text] are sensitive primarily to fluid types and saturations. The parameter correlations are sensitive also to the model parameterization. As noise increases in the viscoelastic input data, the resolution of the estimated parameters decreases, but the parameter correlations are relatively unaffected by modest noise levels.

scholarly journals Analysis of Genetic Structure and Dispersal Patterns in a Population of Sea Beet

Genetics ◽  
1998 ◽  
Vol 149 (4) ◽  
pp. 1975-1985
Author(s):  
Jarle Tufto ◽  
Alan F Raybould ◽  
Kjetil Hindar ◽  
Steinar Engen
Keyword(s):  
Equilibrium Distribution ◽  
Pollen Dispersal ◽  
Migration Pattern ◽  
Likelihood Ratio Tests ◽  
Frequency Data ◽  
Gene Frequencies ◽  
Dispersal Patterns ◽  
Parametric Bootstrapping ◽  
Estimated Parameters ◽  
Multivariate Stochastic Process

Abstract A model of the migration pattern in a metapopulation of sea beet (Beta vulgaris L. ssp. maritima), based on the continuous distributions of seed and pollen movements, is fitted to gene frequency data at 12 isozyme and RFLP loci by maximum likelihood by using an approximation of the simultaneous equilibrium distribution of the gene frequencies generated by the underlying multivariate stochastic process of genetic drift in the population. Several alternative restrictions of the general model are fitted to the data, including the island model, a model of complete isolation, and a model in which the seed and pollen dispersal variances are equal. Several likelihood ratio tests between these alternatives are performed, and median bias in the estimated parameters is corrected by using parametric bootstrapping. To assess the fit of the selected model, the predicted covariances are compared with covariances computed from the data directly. The dependency of estimated parameters on the ratio between effective and absolute subpopulation sizes, which is treated as a known parameter in the analysis, is also examined. Finally, we note that the data also appear to contain some information about this ratio.

scholarly journals Discussion on Competition for Spatial Statistics for Large Datasets

2021 ◽  
Author(s):  
Roman Flury ◽  
Reinhard Furrer
Keyword(s):  
Spatial Statistics ◽  
Covariance Function ◽  
Likelihood Function ◽  
Large Datasets ◽  
Missing Observations ◽  
Covariance Model ◽  
Compactly Supported ◽  
Covariance Tapering ◽  
Simple Kriging ◽  
Estimated Parameters

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.

scholarly journals Novel Optimization-Based Parameter Estimation Method for the Bass Diffusion Model

SAGE Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 215824402110269
Author(s):  
Lang Liang
Keyword(s):  
Parameter Estimation ◽  
Least Squares Method ◽  
Weighted Least Squares ◽  
Estimation Method ◽  
Reference Value ◽  
Diffusion Problem ◽  
Bass Model ◽  
Weighted Least Squares Method ◽  
Estimated Parameters ◽  
Bass Diffusion Model

The Bass model is the most popular model for forecasting the diffusion process of a new product. However, the controlling parameters in it are unknown in practice and need to be determined in advance. Currently, the estimation of the controlling parameters has been approached by various techniques. In this case, a novel optimization-based parameter estimation (OPE) method for the Bass model is proposed in the theoretical framework of system dynamics ( SD). To do this, the SD model of the Bass differential equation is first established and then the corresponding optimization mathematical model is formulated by introducing the controlling parameters as design variable and the discrepancy of the adopter function to the reference value as objective function. Using the VENSIM software, the present SD optimization model is solved, and its effectiveness and accuracy are demonstrated by two examples: one involves the exact solution and another is related to the actual user diffusion problem from Chinese Mobile. The results show that the present OPE method can produce higher predicting accuracy of the controlling parameters than the nonlinear weighted least squares method and the genetic algorithms. Moreover, the reliability interval of the estimated parameters and the goodness of fitting of the optimal results are given as well to further demonstrate the accuracy of the present OPE method.

Composite adaptive control based on historical data for electro-hydraulic system driven by dual servo motor-pump with unknown disturbance

2021 ◽  
pp. 095965182199860
Author(s):  
Xiaofu Zhang ◽  
Guanglin Shi
Keyword(s):  
Adaptive Control ◽  
Hydraulic System ◽  
Historical Data ◽  
Tracking Performance ◽  
Load Force ◽  
Position Tracking ◽  
Servo Motor ◽  
Estimated Parameters ◽  
Unknown Disturbance ◽  
Composite Adaptive Control

This article presents a composite adaptive control method to improve the position-tracking performance of an electro-hydraulic system driven by dual constant displacement pump and dual servo motor named as a novel electro-hydraulic system with unknown disturbance. A composite adaptive controller based on backstepping method is designed to estimate the uncertainties of electro-hydraulic control system, including the damping coefficient and elastic modulus. In order to release the persistent excitation condition of conventional adaptive control, which is often infeasible in practice, a prediction error based on the online historical data is used to update the estimated parameters. Furthermore, a disturbance observer is used to estimate the disturbance including the unmeasurable load force, friction and other unmodeled disturbance. The experiment results are provided and compared with other methods to verify the effectiveness of the proposed method, and the results have indicated that the proposed method has a better position-tracking performance with the convergent estimated parameters.

Two perspectives for designing a phase II control chart with estimated parameters: The case of the Shewhart X¯ Chart

2019 ◽  
Vol 52 (2) ◽  
pp. 198-217 ◽  
Author(s):  
Felipe S. Jardim ◽  
Subhabrata Chakraborti ◽  
Eugenio K. Epprecht
Keyword(s):  
Control Chart ◽  
Phase Ii ◽  
Estimated Parameters

scholarly journals Growth of the tambaqui Colossoma macropomum (Cuvier) (Characiformes: Characidae): which is the best model?

2005 ◽  
Vol 65 (1) ◽  
pp. 129-139 ◽  
Author(s):  
M. A. H Penna ◽  
M. A Villacorta-Corrêa ◽  
T. Walter ◽  
M. Petrere-JR
Keyword(s):  
Growth Model ◽  
Negative Correlation ◽  
The Other ◽  
Data Sources ◽  
Von Bertalanffy ◽  
Data Set ◽  
Colossoma Macropomum ◽  
Von Bertalanffy Model ◽  
Estimated Parameters ◽  
Schnute Model

In order to decide which is the best growth model for the tambaqui Colossoma macropomum Cuvier, 1818, we utilized 249 and 256 length-at-age ring readings in otholiths and scales respectively, for the same sample of individuals. The Schnute model was utilized and it is concluded that the Von Bertalanffy model is the most adequate for these data, because it proved highly stable for the data set, and only slightly sensitive to the initial values of the estimated parameters. The phi' values estimated from five different data sources presented a CV = 4.78%. The numerical discrepancies between these values are of not much concern due to the high negative correlation between k and L<FONT FACE=Symbol>¥</FONT> viz, so that when one of them increases, the other decreases and the final result in phi' remains nearly unchanged.

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