Interlaboratory comparison of test results of an ordinal or nominal binary property: analysis of variation

Bashkansky, Emil; Gadrich, Tamar; Kuselman, Ilya

doi:10.1007/s00769-011-0856-0

Cited by 30 publications

(15 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bashkansky et al [5] and Gadrich et al [6] propose another approach based on ''ORDANOVA'' (Gadrich and Bashkansky [7]) for the calculation of precision data. This approach is closely related to the one proposed by Wilrich [4] and Wehling et al [3].…”

Section: Introductionmentioning

confidence: 99%

Validation of qualitative PCR methods on the basis of mathematical–statistical modelling of the probability of detection

Uhlig¹,

Frost²,

Colson³

et al. 2015

Accred Qual Assur

View full text Add to dashboard Cite

A new model for the probability of detection (POD curve) for qualitative PCR methods examined in a method validation collaborative study is presented. The model allows the calculation of the POD curve and the limit of detection (LOD 95% ), i.e. the number of copies of the target DNA sequence required to ensure 95 % probability of detection. The between-laboratory variability of the limit of detection is used to derive the betweenlaboratory reproducibility of the PCR method. The model is closely related to the approach for quantitative methods described in ISO 5725.2:2002, and the relative limit of detection approach described in the new standard ISO 16140-2:2014.

show abstract

Section: Introductionmentioning

confidence: 99%

Validation of qualitative PCR methods on the basis of mathematical–statistical modelling of the probability of detection

Uhlig¹,

Frost²,

Colson³

et al. 2015

Accred Qual Assur

View full text Add to dashboard Cite

show abstract

“…There are two ways to test for reproducibility and repeatability: in terms of the percentage of their contribution to the Total Variation; adopting the idea of homogeneity/heterogeneity expressed in Bashkansky et al and Gadrich and Bashkansky According to option (2), under the assumption of homogeneity, there is equivalence between the expected values of sources of variation normalized by the corresponding degrees of freedom:

\frac{E ([], V_{TOT})}{{italicdf}_{TOT}} = \frac{E ([], V_{normalW})}{{italicdf}_{normalW}} = \frac{E ([], V_{normalB})}{{italicdf}_{normalB}}

where V TOT is the Total Variation among the M × K PCs and df TOT = K × M − 1 the corresponding degrees of freedom, V W is the Within Variation component with df W degrees of freedom, whereas V B is the Between Variation component with df B degrees of freedom.…”

Section: Methodsmentioning

confidence: 99%

Analysis of consumer preferences expressed by prioritization chains

Vanacore

Pellegrino

Marmor

et al. 2019

Quality & Reliability Eng

Self Cite

View full text Add to dashboard Cite

In this paper, a novel approach is proposed to analyze and interpret consumer preference data expressed as weak orderings (ie, preference chains) over a set of alternatives. A simple distance metric based on cosine similarity is used to assess the repeatability and reproducibility of consumer preferences. These concepts have been borrowed from metrology and can be usefully adopted to evaluate the meaningfulness of aggregated measures (eg, median preference chain) defined over the preference data set. The proposed approach is fully exploited through a real case study involving a group of consumers expressing their preferences over alternative types of pizza toppings.

show abstract

“…Some results may show that parameters come from another distribution, such as in the distribution of rhythmic proportion, which can be compared to a candidate solution with a Two-sample Kolmogorov-Smirnov test [19]. Some parameters may only represent categorical values, which can be compared with a nominal statistical test [20]. Finally, other results indicate potential individual target values for the melodies, such as the tempo of each melody, which can be directly included in objective function values as the distance from the target.…”

Section: Automatic Objective Functionsmentioning

confidence: 98%

Automatic Evaluation Methods in Evolutionary Music: An Example with Bossa Melodies

Freitas

Guimarães

Barbosa

2012

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Evolutionary algorithms need measures of how appropriate a solution is in order to make decisions. This is always a problem for evolving art as codifying aesthetics is a complex task. In this paper we consider the problem of evaluating melodies. The evaluation of melodies in evolutionary music is an open problem that has been tackled by many authors with interactive evaluation, fitness-free genetic algorithms and even neural networks. However, all approaches based on formal analysis of databases or formal music theory have been partial, which is something to be expected for such a complex problem. Thus, we present many metrics that can be used for evaluating melodies and their practical results when applied to a Bossa Nova database of melodies coded by the authors. Although the paper is meant to extend the cycle of possible ideas for evolutionary composers, we argue that there is still much to be developed in this field and each genre of music will always need specific measures of quality.

show abstract

Interlaboratory comparison of test results of an ordinal or nominal binary property: analysis of variation

Cited by 30 publications

References 12 publications

Validation of qualitative PCR methods on the basis of mathematical–statistical modelling of the probability of detection

Validation of qualitative PCR methods on the basis of mathematical–statistical modelling of the probability of detection

Analysis of consumer preferences expressed by prioritization chains

Automatic Evaluation Methods in Evolutionary Music: An Example with Bossa Melodies

Contact Info

Product

Resources

About