Rank-Based Test for Partial Functional Linear Regression Models

Xie, Tianfa; Cao, Ruiyuan; Yu, Ping

doi:10.1007/s11424-020-8362-2

Cited by 5 publications

(6 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Comparing the two multivariate populations is common in clinical trials, and a rank-based robust test is effective method, which doesn’t depend on the shape of distributions and is robust even if the distribution of population have a heavy tail. 7–9,18,19 In this work, we propose a new test, DST, for the NBF based on ranks. Our test not only captures the signals from both directions where the rank-sum methods may lose power when heterogeneous signals exit, but also detects signals with moderate intensity that were neglected by maximum-type test.…”

Section: Discussionmentioning

confidence: 99%

Directional-sum test for nonparametric Behrens-Fisher problem with applications to the dietary intervention trial

Meng

Yang

et al. 2021

Stat Methods Med Res

View full text Add to dashboard Cite

For a nonparametric Behrens-Fisher problem, a directional-sum test is proposed based on division-combination strategy. A one-layer wild bootstrap procedure is given to calculate its statistical significance. We conduct simulation studies with data generated from lognormal, t and Laplace distributions to show that the proposed test can control the type I error rates properly and is more powerful than the existing rank-sum and maximum-type tests under most of the considered scenarios. Applications to the dietary intervention trial further show the performance of the proposed test.

show abstract

Section: Discussionmentioning

confidence: 99%

Directional-sum test for nonparametric Behrens-Fisher problem with applications to the dietary intervention trial

Meng

Yang

et al. 2021

Stat Methods Med Res

View full text Add to dashboard Cite

show abstract

“… Rank Method: the nonparametric estimation methods are extended ,such as the rank estimation for partial functional linear regression models with FPCA [94], hypothesis test for the parametric component based on the rank score function [95].…”

Section: Other Extensionsmentioning

confidence: 99%

“… Berkeley growth study data: The dataset for functional data analysis with male and female growth datasets. [11,15,52,53,61,68,94,95]  Environment and epidemiology (Temperature, sunspot, corn yield, barley plants, AIDS): Different datasets with the main topics in environment and epidemiology [22, 24, 35, 42, 54, 56, 57,…”

Section: Applicationsmentioning

confidence: 99%

A Literature Review of Semi-functional Partial Linear Regression Models

Fayaz¹

2021

Preprint

View full text Add to dashboard Cite

Background: In the functional data analysis (FDA), the hybrid or mixed data are scalar and functional datasets. The semi-functional partial linear regression model (SFPLR) is one of the first semiparametric models for the scalar response with hybrid covariates. Various extensions of this model are explored and summarized. Methods: Two first research articles, including “semi-functional partial linear regression model”, and “Partial functional linear regression” have more than 300 citations in Google Scholar. Finally, only 106 articles remained according to the inclusion and exclusion criteria such as 1) including the published articles in the ISI journals and excluding 2) non-English and 3) preprints, slides, and conference papers. We use the PRISMA standard for systematic review. Results: The articles are categorized into the following main topics: estimation procedures, confidence regions, time series, and panel data, Bayesian, spatial, robust, testing, quantile regression, varying Coefficient Models, Variable Selection, Single-index model, Measurement error, Multiple Functions, Missing values, Rank Method and Others. There are different applications and datasets such as the Tecator dataset, air quality, electricity consumption, and Neuroimaging, among others. Conclusions: SFPLR is one of the most famous regression modeling methods for hybrid data that has a lot of extensions among other models.

show abstract

“…It is observed that the proposed test maintained correct size and attained more power [27,28]. Xie et al [29] developed a rank test based on the rank score function using functional principal component analysis. Also, asymptotic properties of the test are established under local and alternative hypotheses.…”

Section: Introductionmentioning

confidence: 96%

Development of New Robust Optimal Score Function for the Weibull Distributed Error Term in Multilevel Models

Saleem

Sherwani

Amin

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

A popular robust estimation technique for linear models is the rank-based method as an alternative to the ordinary least square (OLS) and restricted maximum likelihood (REML) in the presence of extreme observations. This method is applied in machine reliability analysis and quantum engineering, especially in artificial intelligence and optimization problems where outliers are commonly observed. This technique is also extended for the multilevel model, where the shape of error distribution contributes a significant role in more efficient estimation. In this study, we proposed the Weibull score function for the Weibull distributed error terms in the multilevel model. The efficiency of the proposed score function is compared with the existing Wilcoxon score function and the traditional method REML via Monte Carlo simulations after adding simulated extreme observations. For small values of shape parameter in Weibull distribution of error term showing the presence of outliers, the Weibull score function was found to be efficient as compared to the Wilcoxon and REML methods. However, for a large value of shape parameter, Wilcoxon score appeared either equally efficient than the Weibull score function. REML is observed least precise in all situations. These findings are verified through a real application on test scores data, with a small value of shape parameter, and the Weibull score function turned out the most efficient.

show abstract

Rank-Based Test for Partial Functional Linear Regression Models

Cited by 5 publications

References 23 publications

Directional-sum test for nonparametric Behrens-Fisher problem with applications to the dietary intervention trial

Directional-sum test for nonparametric Behrens-Fisher problem with applications to the dietary intervention trial

A Literature Review of Semi-functional Partial Linear Regression Models

Development of New Robust Optimal Score Function for the Weibull Distributed Error Term in Multilevel Models

Contact Info

Product

Resources

About