Predictive ability of the MOSCED and UNIFAC activity coefficient estimation methods

1987 ◽  
Vol 59 (21) ◽  
pp. 2596-2602 ◽  
Author(s):  
Jung Hag. Park ◽  
Peter W. Carr
Keyword(s):  
Activity Coefficient ◽  
Predictive Ability ◽  
Estimation Methods ◽  
Coefficient Estimation

Comparing genetic coefficient estimation methods using the CERES-Maize model

2000 ◽  
Vol 65 (1) ◽  
pp. 29-41 ◽  
Author(s):  
E Román-Paoli ◽  
S.M Welch ◽  
R.L Vanderlip
Keyword(s):  
Estimation Methods ◽  
Coefficient Estimation ◽  
Genetic Coefficient

Comparison of different methods for estimating activity coefficients in binary mixtures of hydrocarbons

1981 ◽  
Vol 46 (7) ◽  
pp. 1541-1548
Author(s):  
Vladimír Dohnal
Keyword(s):  
Activity Coefficient ◽  
Binary Mixtures ◽  
Activity Coefficients ◽  
Regular Solution Model ◽  
Pure Component ◽  
Group Contribution ◽  
Coefficient Estimation ◽  
The Mean ◽  
Group Contribution Methods ◽  
The One

The accuracy and reliability of a number of different methods for predicting activity coefficients in binary solutions of hydrocarbons was tested. Various modifications of the regular solution model and of the one-parameter Wilson equation and various group-contribution methods were applied to a set of 53 binary mixtures of hydrocarbons of different types. The agreement of the calculated and experimental dependence of activity coefficients on composition was considered. On using the best methods requiring the knowledge of pure component properties only, it is necessary to expect on the average an error of 7% in the value of activity coefficient. When using the group-contribution methods, which employ condensed information on related systems for the prediction, the mean error in the activity coefficient estimation lies about 4%.

The Impact of Missing and Error-Prone Auxiliary Information on Sparse-Matrix Sub-Population Parameter Estimates

Methodology ◽  
2015 ◽  
Vol 11 (3) ◽  
pp. 89-99 ◽  
Author(s):  
Leslie Rutkowski ◽  
Yan Zhou
Keyword(s):  
Sparse Matrix ◽  
Small Body ◽  
Auxiliary Information ◽  
Poor Quality ◽  
Quality Data ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Population Parameter ◽  
Conditioning Model ◽  
The Impact

Abstract. Given a consistent interest in comparing achievement across sub-populations in international assessments such as TIMSS, PIRLS, and PISA, it is critical that sub-population achievement is estimated reliably and with sufficient precision. As such, we systematically examine the limitations to current estimation methods used by these programs. Using a simulation study along with empirical results from the 2007 cycle of TIMSS, we show that a combination of missing and misclassified data in the conditioning model induces biases in sub-population achievement estimates, the magnitude and degree to which can be readily explained by data quality. Importantly, estimated biases in sub-population achievement are limited to the conditioning variable with poor-quality data while other sub-population achievement estimates are unaffected. Findings are generally in line with theory on missing and error-prone covariates. The current research adds to a small body of literature that has noted some of the limitations to sub-population estimation.

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