Home About us Contact
|
Home About us Contact | ||
|
|
||
Percentage Deviation (percentage + deviation)
Selected AbstractsA Three-step Method for Choosing the Number of Bootstrap RepetitionsECONOMETRICA, Issue 1 2000Donald W. K. Andrews This paper considers the problem of choosing the number of bootstrap repetitions B for bootstrap standard errors, confidence intervals, confidence regions, hypothesis tests, p -values, and bias correction. For each of these problems, the paper provides a three-step method for choosing B to achieve a desired level of accuracy. Accuracy is measured by the percentage deviation of the bootstrap standard error estimate, confidence interval length, test's critical value, test's p -value, or bias-corrected estimate based on B bootstrap simulations from the corresponding ideal bootstrap quantities for which B=,. The results apply quite generally to parametric, semiparametric, and nonparametric models with independent and dependent data. The results apply to the standard nonparametric iid bootstrap, moving block bootstraps for time series data, parametric and semiparametric bootstraps, and bootstraps for regression models based on bootstrapping residuals. Monte Carlo simulations show that the proposed methods work very well. [source] Comparison of different algorithms to calculate electrophoretic mobility of analytes as a function of binary solvent compositionELECTROPHORESIS, Issue 10 2003Abolghasem Jouyban Abstract Ten different mathematical models representing the electrophoretic mobility of analytes in capillary electrophoresis in mixed solvents of different composition have been compared using 32 experimental data sets. The solvents are binary mixtures of water-methanol, water-ethanol and methanol-ethanol, respectively. Mean percentage deviation (MPD), overall MPD (OMPD) and individual percentage deviation (IPD) have been considered as comparison criteria. The results showed that a reorganized solution model, namely the combined nearly ideal binary solvent/Redlich-Kister equation, is the most accurate model among other similar models concerning both correlation ability and prediction capability. [source] Nonlinear quantitative structure-property relationship modeling of skin permeation coefficientJOURNAL OF PHARMACEUTICAL SCIENCES, Issue 11 2009Brian J. Neely Abstract The permeation coefficient characterizes the ability of a chemical to penetrate the dermis, and the current study describes our efforts to develop structure-based models for the permeation coefficient. Specifically, we have integrated nonlinear, quantitative structure-property relationship (QSPR) models, genetic algorithms (GAs), and neural networks to develop a reliable model. Case studies were conducted to investigate the effects of structural attributes on permeation using a carefully characterized database. Upon careful evaluation, a permeation coefficient data set consisting of 333 data points for 258 molecules was identified, and these data were added to our extensive thermophysical database. Of these data, permeation values for 160 molecular structures were deemed suitable for our modeling efforts. We employed established descriptors and constructed new descriptors to aid the development of a reliable QSPR model for the permeation coefficient. Overall, our new nonlinear QSPR model had an absolute-average percentage deviation, root-mean-square error, and correlation coefficient of 8.0%, 0.34, and 0.93, respectively. Cause-and-effect analysis of the structural descriptors obtained in this study indicates that that three size/shape and two polarity descriptors accounted for ,70% of the permeation information conveyed by the descriptors. © 2009 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 98:4069,4084, 2009 [source] New probe for the measurement of dynamic changes in the rectumNEUROGASTROENTEROLOGY & MOTILITY, Issue 1 2004I. S. Andersen Abstract, Conventional mano-volumetric techniques cannot measure changes in circumferential dimensions at several axial positions within a bowel segment. Our aims were to validate a new impedance planimetry probe for simultaneously measuring the cross-sectional area (CSA) at five axial positions in vitro and in vivo in 10 anesthetized pigs. The day-to-day coefficient of variation (CV) for CSA measured by the probe in cylindrical tubes of known diameter was 0.8,9.5%. The mean from actual diameter deviation ranged from 2.3 to 6.7%. In a conical tube the day-to-day CV was 2.3,8% and mean percentage deviation ,2.8 to 1.0. Interposed narrowing sections caused a total CV of 7,13%. In vivo studies revealed variations in CSA, associated with expulsion of flatus. It is concluded that impedance planimetry allows simultaneous measurement of CSA at several levels within the rectum. In vitro validity was acceptable and alterations in lumen diameter were identified in vivo. [source] Application of PC-ANN to Acidity Constant Prediction of Various Phenols and Benzoic Acids in WaterCHINESE JOURNAL OF CHEMISTRY, Issue 5 2008Aziz HABIBI-YANGJEH Abstract Principal component regression (PCR) and principal component-artificial neural network (PC-ANN) models were applied to prediction of the acidity constant for various benzoic acids and phenols (242 compounds) in water at 25 °C. A large number of theoretical descriptors were calculated for each molecule. The first fifty principal components (PC) were found to explain more than 95% of variances in the original data matrix. From the pool of these PC's, the eigenvalue ranking method was employed to select the best set of PC for PCR and PC-ANN models. The PC-ANN model with architecture 47-20-1 was generated using 47 principal components as inputs and its output is pKa. For evaluation of the predictive power of the PCR and PC-ANN models, pKa values of 37 compounds in the prediction set were calculated. Mean percentage deviation (MPD) for PCR and PC-ANN models are 18.45 and 0.6448, respectively. These improvements are due to the fact that the pKa of the compounds demonstrate non-linear correlations with the principal components. Comparison of the results obtained by the models reveals superiority of the PC-ANN model relative to the PCR model. [source] | ||||||||||