Pseudo-Ranks: The Better Way of Ranking?

Zimmermann, Georg; Brunner, Edgar; Brannath, Werner; Happ, Martin; Bathke, Arne C.

doi:10.1080/00031305.2021.1972836

Cited by 5 publications

(4 citation statements)

References 45 publications

(39 reference statements)

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“…The nonparametric Kruskal-Wallis test is applied when the assumptions for a normal distribution of each group with approximately equal variance in the scores are not met. The Kruskal-Wallis test, which measures median or mean equality (Bower et al, 2023), is a non-parametric method for comparing independent samples (Zimmermann et al, 2022). If a random sample of size from a large population consists of distinct groups or categories that are adequately represented in the sample, it is necessary to compare the groups of sizes with , in accordance with a continuous response variable .…”

Section: Research Objective Methodology and Datamentioning

confidence: 99%

Corporate Debt Dynamics: Sectoral Clustering Analysis Using NACE Classification in Slovakia

Gajdosikova,

Valaskova,

Lopatka

et al. 2024

JOBS Journal

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Research background: Many authors are currently exploring the impact of the industry on the financial structure of enterprises since there are statistically significant differences across various sectors, exposing the industry as a critical factor influencing corporate indebtedness. Clusters of sectors with homogeneous patterns of indebtedness and comparable levels of debt in various economic conditions may be determined, and, therefore, firms and their debt levels should be systematically examined and evaluated. Purpose of the article: The main aim of this paper is to evaluate the level of debt in the Slovak environment on a sample of 19,480 firms from various sectors and to identify the relationships among them to identify sectors with homogeneous patterns of indebtedness and, consequently, comprehend which sectors are the most stable and independent. Methods: Because NACE classification provides a framework for gathering and presenting statistical data based on many economic sectors, the number of input data was reduced based on cluster analysis. Using Ward's hierarchical clustering method using squared Euclidean distance, selected indebtedness ratios were used to define the sectors with comparable debt levels. To determine if there were statistically significant differences between the calculated debt ratios related to the economic sector, the Kruskal-Wallis test was performed. Subsequently, as the results indicated significant differences across various indebtedness ratios, a post hoc analysis was performed. Findings & Value added: A group of NACE activities in which enterprises are sufficiently comparable that it is appropriate to evaluate their debt levels using the chosen indebtedness ratios are considered to be in sectors C, F, G and H, which included to tertiary sector, while the sectors K, R and S may also be grouped in one cluster, while form the secondary sector. The key relevance of our findings is the benchmarking of selected sectors about indebtedness, which may be used to further examine their growth in each of the V4 nations, which is an essential area for the evolution of the European economy as a whole. Studies considering a relatively significant amount of capital structure determinants may be beneficial to owners and managers, regulators, and financial institutions since debt policy affects firm performance, value, and survival.

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Section: Research Objective Methodology and Datamentioning

confidence: 99%

Corporate Debt Dynamics: Sectoral Clustering Analysis Using NACE Classification in Slovakia

Gajdosikova,

Valaskova,

Lopatka

et al. 2024

JOBS Journal

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“…The unweighted mean distribution is, however, one reference distribution of choice that helps in reducing the issues obtained with the weighted version. Whether the unweighted version is the "best" one, can not be answered and guaranteed, in general (Zimmermann et al, 2022).…”

Section: Hypotheses Formulated In Terms Of Relative Effectsmentioning

confidence: 99%

rankFD: An R Software Package for Nonparametric Analysis of General Factorial Designs

Konietschke,

Brunner

2023

The R Journal

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Many experiments can be modeled by a factorial design which allows statistical analysis of main factors and their interactions. A plethora of parametric inference procedures have been developed, for instance based on normality and additivity of the effects. However, often, it is not reasonable to assume a parametric model, or even normality, and effects may not be expressed well in terms of location shifts. In these situations, the use of a fully nonparametric model may be advisable. Nevertheless, until very recently, the straightforward application of nonparametric methods in complex designs has been hampered by the lack of a comprehensive R package. This gap has now been closed by the novel R-package rankFD that implements current state of the art nonparametric ranking methods for the analysis of factorial designs. In this paper, we describe its use, along with detailed interpretations of the results.

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“…the pseudo-rank of X ik among all N observations. We refer to the recent literature for more details on the usefulness (Zimmermann et al 2021) and the efficient computation (Happ et al 2020) of pseudo-ranks. Additionally, we define by…”

Section: Confidence Distributions For Nonparametric Effects In Severa...mentioning

confidence: 99%

A unifying framework for rank and pseudo-rank based inference using nonparametric confidence distributions

Beck

Bathke

2023

Stat Papers

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Nonparametric confidence distributions estimate statistical functionals by a distribution function on the parameter space, instead of the classical point or interval estimators. The concept bears analogy to the Bayesian posterior, but is nevertheless a completely frequentist concept. In order to ensure the desired statistical properties, we require that the cumulative distribution function on the parameter space is, evaluated at the true parameter, uniformly distributed over the unit interval. Our main focus lies on developing confidence distributions for the nonparametric relative effect and some natural extensions thereof. We develop asymptotic, range preserving and—especially important in the case of small sample sizes—approximate confidence distributions based on rank and pseudo-rank procedures. Due to the close relationship between point estimators, confidence intervals and p-values, these can all be approached in a unified manner within the framework of confidence distributions. The main goal of our contribution is to make the powerful theory of confidence distributions available in a nonparametric context, that is, for situations where methods relying on parametric assumptions are not justifiable. Application of the proposed methods and interpretation of the results is demonstrated using real data sets, including ordinal, non-metric data.

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Pseudo-Ranks: The Better Way of Ranking?

Cited by 5 publications

References 45 publications

Corporate Debt Dynamics: Sectoral Clustering Analysis Using NACE Classification in Slovakia

Corporate Debt Dynamics: Sectoral Clustering Analysis Using NACE Classification in Slovakia

rankFD: An R Software Package for Nonparametric Analysis of General Factorial Designs

A unifying framework for rank and pseudo-rank based inference using nonparametric confidence distributions

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