Rank-Based Procedures for Linear Models: Applications to Pharmaceutical Science Data

Abebe, Asheber; Crimin, Kimberly; McKean, Joseph W.; Haas, Joseph V.; Vidmar, Thomas J.

doi:10.1177/009286150103500334

Cited by 14 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This allows users to observe whether the two procedures produce equivalent or conflicting results. Abebe, Crimin, and McKean (2001) and Crimin, Abebe, and McKean (in press) provided a guide to conducting robust analyses using RGLM. It is important to note that the RGLM interface only conducts WA.…”

Section: Modern Rank Statisticsmentioning

confidence: 99%

Modern robust statistical methods: An easy way to maximize the accuracy and power of your research.

Erceg‐Hurn¹,

Mirosevich²

2008

American Psychologist

786

453

View full text Add to dashboard Cite

Classic parametric statistical significance tests, such as analysis of variance and least squares regression, are widely used by researchers in many disciplines, including psychology. For classic parametric tests to produce accurate results, the assumptions underlying them (e.g., normality and homoscedasticity) must be satisfied. These assumptions are rarely met when analyzing real data. The use of classic parametric methods with violated assumptions can result in the inaccurate computation of p values, effect sizes, and confidence intervals. This may lead to substantive errors in the interpretation of data. Many modern robust statistical methods alleviate the problems inherent in using parametric methods with violated assumptions, yet modern methods are rarely used by researchers. The authors examine why this is the case, arguing that most researchers are unaware of the serious limitations of classic methods and are unfamiliar with modern alternatives. A range of modern robust and rank-based significance tests suitable for analyzing a wide range of designs is introduced. Practical advice on conducting modern analyses using software such as SPSS, SAS, and R is provided. The authors conclude by discussing robust effect size indices.

show abstract

Section: Modern Rank Statisticsmentioning

confidence: 99%

Modern robust statistical methods: An easy way to maximize the accuracy and power of your research.

Erceg‐Hurn¹,

Mirosevich²

2008

American Psychologist

786

453

View full text Add to dashboard Cite

show abstract

“…As with the Web RGLM page, it offers side-byside comparisons of the high breakdown and LS fits. These techniques, developed by Chang et al (1999), use a stochastically weighted Wilcoxon norm to obtain estimators that are robust to outliers in both design and response space, while the Wilcoxon analysis is only robust in response space. We plan on finishing this page in the future.…”

Section: Discussionmentioning

confidence: 99%

“…The dataset used for this example was discussed in Afifi and Azen (1972). The purpose of the experiment was to compare methods of reducing human's fear of snakes.…”

Section: Analysis Of Covariance: Snake Data Setmentioning

confidence: 99%

Vol. 7, No. 1 (Full Issue)

2008

J. Mod. App. Stat. Meth.

View full text Add to dashboard Cite

The invited authors of this edited volume have been prolific in the arena of Real Data Analysis (RDA) as it applies to the social and behavioral sciences, especially in the disciplines of education and psychology. Combined, this brain trust represents 3,247 articles in refereed journals, 127 books published, US $45.3 Million in extramural research funding, 34 teaching and 92 research awards, serve(d) as Editor/Assistant Editor/Editorial Board Member for 95 peer reviewed journals, and provide(d) ad hoc reviews for 362 journals. Their enormous footprint on real data analysis is showcased for professors, researchers, educators, administrators, and graduate students in the second text in the AERA/SIG ES Quantitative Methods series.

show abstract

“…In such circumstances, a robust estimation method is desirable. Rank-based regression is a mature tool to achieve robustness with no assumption on the distribution of the error term; see Abebe et al [7], Hettmansperger and Mckean [8] for a comprehensive review.…”

Section: Introductionmentioning

confidence: 99%

Robust continuous piecewise linear regression model with multiple change points

Shi

Wan

2018

J Supercomput

View full text Add to dashboard Cite

This paper considers a robust piecewise linear regression model with an unknown number of change points. Our estimation framework mainly contains two steps: First, we combine the linearization technique with rank-based estimators to estimate the regression coefficients and the location of thresholds simultaneously, given a large number of change points. The associated inferences for all the parameters are easily derived. Second, we use the LARS algorithm via generalized BIC to refine the candidate threshold estimates and obtain the ultimate estimators. The rank-based regression guarantees that our estimators are less sensitive to outliers and heavy-tailed data, and therefore achieves robustness. Simulation studies and an empirical example on BMI and age relationship illustrate the proposed method.

show abstract

Rank-Based Procedures for Linear Models: Applications to Pharmaceutical Science Data

Cited by 14 publications

References 19 publications

Modern robust statistical methods: An easy way to maximize the accuracy and power of your research.

Modern robust statistical methods: An easy way to maximize the accuracy and power of your research.

Vol. 7, No. 1 (Full Issue)

Robust continuous piecewise linear regression model with multiple change points

Contact Info

Product

Resources

About