scholarly journals Comparison of computer-based and manual coal resource estimation methods for the Cache coal bed, Recluse Geologic Model Area, Wyoming

Circular ◽  
1984 ◽  
Author(s):  
Gary B. Schneider ◽  
Sharon S. Crowley ◽  
Mary Alice Carey
Keyword(s):  
Estimation Methods ◽  
Coal Bed ◽  
Resource Estimation ◽  
Computer Based ◽  
Geologic Model ◽  
Coal Resource

A Comparison of Regression Prediction Accuracy on Several Types of Factor Scores

1979 ◽  
Vol 16 (1) ◽  
pp. 17-24 ◽  
Author(s):  
John D. Morris
Keyword(s):  
Prediction Accuracy ◽  
Predictive Accuracy ◽  
Factor Score ◽  
Regression Equation ◽  
Estimation Methods ◽  
Multiple Regression Equation ◽  
Factor Scores ◽  
Variable Sampling ◽  
Computer Based ◽  
Weighted Factor

Several advantages to the use of factor scores as independent variables in a multiple regression equation have been advocated in the literature. To provide guidance for selecting the most desirable type of factor score upon which to calculate a regression equation, computer-based Monte Carlo methods were used to compare the predictive accuracy upon replication of regression on five “complete” and four “incomplete” factor score estimation methods. For several levels of multiple correlation (R2 = .30, .50, and. 70), and for several subject-to-variable sampling ratios (3:1, 5:1, and 10:1), prediction on incomplete factor scores showed better double cross-validated prediction accuracy than on complete factor scores. Moreover, the unique unit-weighted factor score was superior among the incomplete methods.

Coal resource estimation using Gaussian copula

2017 ◽  
Vol 175 ◽  
pp. 1-9 ◽  
Author(s):  
Fırat Atalay ◽  
A. Erhan Tercan
Keyword(s):  
Gaussian Copula ◽  
Resource Estimation ◽  
Coal Resource

The Impact of Filtering on Sea and Air Operations

1983 ◽  
Vol 36 (1) ◽  
pp. 74-80
Author(s):  
M. G. Pearson
Keyword(s):  
Real Time ◽  
Kalman Filtering ◽  
The Other ◽  
Estimation Methods ◽  
Weighted Mean ◽  
Modern Approach ◽  
On Line ◽  
Computer Based ◽  
And Control ◽  
The Impact

Estimation methods and filtering techniques are nowadays an integral part of any computer-based navigation system. The purpose of these techniques is to provide an estimate of required variables which is sufficiently accurate for real-time command and control purposes. Repeatability, which is important for so many applications, is deemed to be a by-product of the estimation process. For this requirement it is not strictly necessary for the process to be accurate, it is sufficient if it is only consistent; these are closely linked but one does not imply the other. The modern approach is to minimize the variance of the noisy observations or the sum of the squares of the residuals, and the methods available for doing this are increasingly refined. The impression given in the literature (and it is extensive) is that data processing can somehow compensate for the shortcomings of the basic sensors with respect to the operation being considered. Within certain limits this is true, but the real reason for the sudden surge of Kalman filtering for real-time on-line applications was the relative simplicity of the computational process. In a way, Kalman filtering has done for estimation theory what the Fast Fourier Transform has done for spectral analysis.The concept is simple enough to state. It consists of combining two independent estimates of a variable to form a weighted mean. One of these estimates is a forecast and the other is the current measurement.

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