Rank‐preserving regression: a more robust rank regression model against outliers

Nguelifack

Journal of Coastal Research

et al. 2023

Nguyen, K.A.T.; Nguelifack, B.M.; Nguyen, T.A.T., and Jolly, C.M., 2023. African ocean health production function: Policy implications. Journal of Coastal Research, 39(3), 519–530. Charlotte (North Carolina), ISSN 0749-0208.The study used a two-stage production function, rank regression in the first stage, and probit and ordinary least squares regression in the second stage, to evaluate the effect of socio-economic, environmental, physical, and demographic factors on ocean health. The rank regression analysis coefficients were evaluated using a “t” test. Biodiversity, Tourism, Recreation, Fish Production, Livelihoods, and Sense of Place were all significant at p-value <.01, while carbon storage was significant at p-value <0.05. Artisanal Fisheries were significant at p-value <0.1. The variable Biodiversity had the highest elasticity (.43). A 1.0% increase in biodiversity is likely to increase the Ocean Health Index (OHI) score by 0.43%, while 1.0% increases in sense of place and coastal livelihoods will increase the OHI by 0.14% and 0.13%, respectively. The relative influence result suggests that the most important variables are Tourism and Recreation (27.6%), Natural Products (20.6%), Food Production (14.1%), Carbon Storage (11.0%), and Coastal Protection (4.8%). The probit model showed that human development index influenced ocean health. Development policies may consider the inclusion of changes in Tourism and Recreation, Fish Provision, and Carbon Storage to improve African ocean health.

Section: Stage 1: Wilcoxon Rank Correlation Modelmentioning

confidence: 99%

African Ocean Health Production Function: Policy Implications

Nguelifack

Journal of Coastal Research

et al. 2023

“…But in the growing applications, of major interest are outcomes defined by a pair of subjects, or the "between-subject attributes." 18 The probability index P r(Y i 1 < Y i 2 ), (i 1 , i 2 ) ∈ C n 2 in the Mann-Whitney-Wilcoxon (MWW) rank-sum test is a classical example 7 . Fueled by innovative technologies such as high-throughput sequencing and wearable devices, the pairwise dissimilarity/distance metrics that summarize high-dimensional sequences also entail a between-subject nature 28 .…”

Section: Introductionmentioning

confidence: 99%

On Semiparametric Efficiency of an Emerging Class of Regression Models for Between-subject Attributes

Liu¹,

Lin²,

Chen³

et al. 2022

Preprint

Self Cite

The semiparametric regression models have attracted increasing attention owing to their robustness compared to their parametric counterparts. This paper discusses the efficiency bound for functional response models (FRM), an emerging class of semiparametric regression that serves as a timely solution for research questions involving pairwise observations. This new paradigm is especially appealing to reduce astronomical data dimensions for those arising from wearable devices and high-throughput technology, such as microbiome Beta-diversity, viral genetic linkage, single-cell RNA sequencing, etc. Despite the growing applications, the efficiency of their estimators has not been investigated carefully due to the extreme difficulty to address the inherent correlations among pairs. Leveraging the Hilbert-space-based semiparametric efficiency theory for classical within-subject attributes, this manuscript extends 1 such asymptotic efficiency into the broader regression involving between-subject attributes and pinpoints the most efficient estimator, which leads to a sensitive signal-detection in practice. With pairwise outcomes burgeoning immensely as effective dimension-reduction summaries, the established theory will not only fill the critical gap in identifying the most efficient semiparametric estimator but also propel wide-ranging implementations of this new paradigm for between-subject attributes.

Extending the Mann‐Whitney‐Wilcoxon rank sum test to survey data for comparing mean ranks

Lin

Chen

Liu

et al. 2021

Statistics in Medicine

Self Cite

Statistical methods for analysis of survey data have been developed to facilitate research. More recently, Lumley and Scott (2013) developed an approach to extend the Mann-Whitney-Wilcoxon (MWW) rank sum test to survey data. Their approach focuses on the null of equal distribution. In many studies, the MWW test is called for when two-sample t-tests (with or without equal variance assumed) fail to provide meaningful results, as they are highly sensitive to outliers. In such situations, the null of equal distribution is too restrictive, as interest lies in comparing centers of groups. In this article, we develop an approach to extend the MWW test to survey data to test the null of equal mean rank. Although not as popular as the mean and median, the mean rank is also a meaningful measure of the center of a distribution and is the same as the median for a symmetric distribution. We illustrate the proposed approach and show major differences with Lumley and Scott's alternative using both real and simulated data.