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Kai-Tai Fang

Publications and source records attributed to Kai-Tai Fang.

2 recordsLinked to original sources

Two-step multivariate adaptive regression splines for modeling a quantitative relationship between gas chromatography retention indices and molecular descriptors.

The relationship between retention indices and molecular descriptors of alkanes is established by two-step multivariate adaptive regression splines (TMARS). TMARS combines linear regression with multivariate adaptive regression splines (MARS). It is demonstrated for the present data set that using linear regression or MARS modeling alone causes lack of fit. TMARS avoids lack of fit and appreciably improves the prediction ability for the model. The use of this combined approach permits the development of additional understanding of the adaptive nature in MARS modeling.

Chromatography, Gas↗

External factor variable connectivity index.

A new variable index, external factor variable connectivity index (EFVCI), is proposed, in which the atomic attribute is divided into two parts. The innate part is denoted as outer-shell electrons and external part or perturbation by other atoms is represented as summation, multiplied by a variable x, of squared reciprocal matrix of i row (corresponds to atom A(i)). The division of atomic attribute in EFVCI is interpreted by using topological structure. In the correlation of boiling point of 149 acyclic alkanes, the optimal values will approach to a constant at -0.29 by using the zero to higher order indices of the same series. The new index, with high regression quality (R = 0.9986, s = 2.26, and F = 7088.4), is compared favorably with variable connectivity index and molecular connectivity index.

Journal Article↗