The Experts below are selected from a list of 66 Experts worldwide ranked by ideXlab platform

Oisin Ryan - One of the best experts on this subject based on the ideXlab platform.

  • a Squared Standard Error is not a measure of individual differences
    Proceedings of the National Academy of Sciences of the United States of America, 2019
    Co-Authors: Ellen L Hamaker, Oisin Ryan
    Abstract:

    Fisher et al. (1) investigate the congruence between intraindividual correlations and cross-sectional correlations using six empirical datasets. While others have emphasized that there is no mathematical law dictating that these correlations should be the same (2⇓–4), empirical studies are imperative to determine whether or not they differ in practice. Therefore, we greatly appreciate this timely and valuable contribution. However, Fisher et al. (1) also raise a concern regarding the variability in correlations, which we believe is ill informed. Specifically, they state, “the variance around the expected value was two to four times larger within individuals than within groups. This suggests that literatures in social and medical sciences may overestimate the accuracy of aggregated statistical estimates.” We argue that this comparison is fundamentally flawed, because the two variances they compare are designed to represent inherently different phenomena. The first variance they consider is based on estimating a correlation per person (over time), and then taking the variance of … [↵][1]1To whom correspondence should be addressed. Email: e.l.hamaker{at}uu.nl. [1]: #xref-corresp-1-1

Ellen L Hamaker - One of the best experts on this subject based on the ideXlab platform.

  • a Squared Standard Error is not a measure of individual differences
    Proceedings of the National Academy of Sciences of the United States of America, 2019
    Co-Authors: Ellen L Hamaker, Oisin Ryan
    Abstract:

    Fisher et al. (1) investigate the congruence between intraindividual correlations and cross-sectional correlations using six empirical datasets. While others have emphasized that there is no mathematical law dictating that these correlations should be the same (2⇓–4), empirical studies are imperative to determine whether or not they differ in practice. Therefore, we greatly appreciate this timely and valuable contribution. However, Fisher et al. (1) also raise a concern regarding the variability in correlations, which we believe is ill informed. Specifically, they state, “the variance around the expected value was two to four times larger within individuals than within groups. This suggests that literatures in social and medical sciences may overestimate the accuracy of aggregated statistical estimates.” We argue that this comparison is fundamentally flawed, because the two variances they compare are designed to represent inherently different phenomena. The first variance they consider is based on estimating a correlation per person (over time), and then taking the variance of … [↵][1]1To whom correspondence should be addressed. Email: e.l.hamaker{at}uu.nl. [1]: #xref-corresp-1-1

Jeremy N. V. Miles - One of the best experts on this subject based on the ideXlab platform.

William C. Bridges - One of the best experts on this subject based on the ideXlab platform.

  • The Impact of Correlated and/or Interacting Predictor Omission on Estimated Regression Coefficients in Linear Regression
    Journal of Statistical Theory and Practice, 2019
    Co-Authors: Emily Nystrom, Julia L. Sharp, William C. Bridges
    Abstract:

    We examine cases of predictor omission defined by the relationship between the set of omitted predictor(s) and a set of remaining predictor(s), both of which are included in the full model. We consider a wider range of omitted predictors than previously studied by systematically accounting for both interaction and correlation between the included and the omitted predictors. Our study highlights the impact of predictor omission on the resulting estimated regression coefficients and their Squared Standard Errors. Theoretical and simulated results are presented to illustrate the impact of predictor omission among cases of interaction and correlation. In our simulated results, bias diverged as correlation increased from zero to one. On its own, interaction amplified bias, but the impact of interaction was worse when combined with correlation. Overall, our discussions surround the known problem of predictor omission with a rigorous framework to quantify bias in the included predictor’s estimated regression coefficient and Squared Standard Error.

Zhide Hu - One of the best experts on this subject based on the ideXlab platform.

  • Study of Human Dopamine Sulfotransferases Based on Gene Expression Programming
    Chemical Biology & Drug Design, 2011
    Co-Authors: Hongzong Si, Jiangang Zhao, Ning Lian, Hanlin Feng, Yunbo Duan, Zhide Hu
    Abstract:

    A quantitative model is developed to predict the Km of 47 human dopamine sulfotransferases by gene expression programming. Each kind of compound is represented by several calculated structural descriptors of moment of inertia A, average electrophilic reactivity index for a C atom, relative number of triple bonds, RNCG relative negative charge, HA-dependent HDSA-1, and HBCA H-bonding charged surface area. Eight fitness functions of the gene expression programming method are used to find the best nonlinear model. The best quantitative model with Squared Standard Error and square of correlation coefficient are 0.096 and 0.91 for training data set, and 0.102 and 0.88 for test set, respectively. It is shown that the gene expression programming-predicted results with fitness function are in good agreement with experimental ones.

  • Prediction of gas-phase reduced ion mobility constants (K0) based on the multiple linear regression and projection pursuit regression
    Talanta, 2006
    Co-Authors: Zhide Hu
    Abstract:

    Multiple linear regression and projection pursuit regression were used to develop the linear and nonlinear models for predicting the gas-phase reduced ion mobility constant (KO) of 159 diverse compounds. The six descriptors selected by heuristic method were used as the inputs of the linear and nonlinear models. The linear and nonlinear models gave very satisfactory results; the square of correlation coefficient was 0.9082 and 0.9379, the Squared Standard Error was 0.0043 and 0.0030, respectively for the whole data set. The proposed models can identify and provide some insight into what structural features are related to the K-0 of compounds. They can also help to understand the separation mechanism in ion mobility spectrometry. Additionally, this paper provided two simple, practical and effective methods for analytical chemists to predict the KO of compounds in ion mobility spectrometry. (c) 2006 Elsevier B.V. All rights reserved.