Journal of Data Science

Traditionally z-scores specified from the WHO population growth curves have been used to describe a child’s growth in relation to his age- and sex-matched population distribution. We propose a new regression approach that offers a straightforward interpretation of the relative growth in terms of the original anthropometric variable. We create a hybrid data set consisting of the observations from the study of interest and counterpart pseudo-population observations imputed from the WHO population growth curves matched to each study participant. We then fit linear and quantile regression models to the hybrid data incorporating demographic variables (usually age and biological sex) corresponding to the growth curves of demographically-similar individuals, a study versus population indicator, and its interactions with demographic variables. We further control for confounding variables from the study by adding their interactions with the study indicator variable. The interaction terms between the study indicator and the demographic variables age and biologic sex can be interpreted as relative growth parameters that depict the differences in means (or quantiles) between the study participants and their pseudo-population counterparts of the original anthropometric variables, rather than the associated z-scores. We use anthropometric growth data from a prospective birth cohort study conducted in Uganda for illustration.

Mediation analysis plays an important role in many research fields, yet it is very challenging to perform estimation and hypothesis testing for high-dimensional mediation effects. We develop a user-friendly $\mathsf{R}$ package HIMA for high-dimensional mediation analysis with varying mediator and outcome specifications. The HIMA package is a comprehensive tool that accommodates various types of high-dimensional mediation models. This paper offers an overview of the functions within HIMA and demonstrates the practical utility of HIMA through simulated datasets. The HIMA package is publicly available from the Comprehensive $\mathsf{R}$ Archive Network at https://CRAN.R-project.org/package=HIMA.

Abstract: In the paper, we propose power weighted quantile regression(PWQR), which can reduce the effect of heterogeneous of the conditional densities of the response effectively and improve efficiency of quantile regression). In addition to PWQR, this article also proves that all the weighting of those that the actual value is less than the estimated value of PWQR and the proportion of all the weighting is very close to the corresponding quantile. At last, this article establishes the relationship between Geomagentic Indices and GIC. According to the problems of power system security operation, we make GIC risk value table. This table can have stronger practical operation ability, can provide power system security operation with important inferences.

This article presents a classification of disease severity for patients with cystic fibrosis (CF). CF is a genetic disease that dramatically decreases life expectancy and quality. The disease is characterized by polymicrobial infections which lead to lung remodeling and airway mucus plugging. In order to quantify disease severity of CF patients and compute a continuous severity index measure, quantile regression, rank scores, and corresponding normalized ranks are calculated for CF patients. Based on the rank scores calculated from the set of quantile regression models, a continuous severity index is computed for each CF patient and can be considered a robust estimate of CF disease severity.