Comparison of Three Procedures for Smoothing Score Distributions

1991 ◽  
Author(s):  
D. R. Divgi
Keyword(s):  
Score Distributions

Commentary on “Validation Methods for Aggregate-Level Test Scale Linking: A Case Study Mapping School District Test Score Distributions to a Common Scale”

2020 ◽  
pp. 107699862095666
Author(s):  
Alina A. von Davier
Keyword(s):  
School District ◽  
Test Score ◽  
Applied Research ◽  
Aggregate Level ◽  
Validation Methods ◽  
Common Scale ◽  
Scale Linking ◽  
Score Distributions

In this commentary, I share my perspective on the goals of assessments in general, on linking assessments that were developed according to different specifications and for different purposes, and I propose several considerations for the authors and the readers. This brief commentary is structured around three perspectives (1) the context of this research, (2) the methodology proposed here, and (3) the consequences for applied research.

The interpretation and analysis of subjective body condition scores

1978 ◽  
Vol 26 (2) ◽  
pp. 119-125 ◽  
Author(s):  
D. G. Evans
Keyword(s):  
Body Condition ◽  
Ad Hoc ◽  
Ranking Methods ◽  
Analyse Data ◽  
Body Condition Scores ◽  
Score Distributions

ABSTRACTSubjective scoring data may be analysed by ranking methods, ANOVA methods or by inspection of the score distributions. These methods are discussed with particular reference to body condition scores of cows and ewes. A combination of ANOVA and ad hoc inspection was used to analyse data from trials carried out in 1976 to investigate the precision of condition scoring. On the basis of the results, recommendations are made concerning the use of condition scoring on farms and in scoring trials where the object is to monitor assessor bias and inconsistency.

Psychometric properties and population-based score distributions of the Japanese Sleep Questionnaire for Preschoolers

2014 ◽  
Vol 15 (4) ◽  
pp. 451-458 ◽  
Author(s):  
Sachiko Shimizu ◽  
Kumi Kato-Nishimura ◽  
Ikuko Mohri ◽  
Kuriko Kagitani-Shimono ◽  
Masaya Tachibana ◽  
...  
Keyword(s):  
Psychometric Properties ◽  
Population Based ◽  
Sleep Questionnaire ◽  
Score Distributions

scholarly journals New mixture models for decoy-free false discovery rate estimation in mass spectrometry proteomics

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i745-i753
Author(s):  
Yisu Peng ◽  
Shantanu Jain ◽  
Yong Fuga Li ◽  
Michal Greguš ◽  
Alexander R. Ivanov ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
False Discovery Rate ◽  
Mixture Models ◽  
Experimental Spectrum ◽  
Supplementary Information ◽  
Accurate Estimation ◽  
Component Mixture ◽  
Second Best ◽  
False Discovery ◽  
Score Distributions

Abstract Motivation Accurate estimation of false discovery rate (FDR) of spectral identification is a central problem in mass spectrometry-based proteomics. Over the past two decades, target-decoy approaches (TDAs) and decoy-free approaches (DFAs) have been widely used to estimate FDR. TDAs use a database of decoy species to faithfully model score distributions of incorrect peptide-spectrum matches (PSMs). DFAs, on the other hand, fit two-component mixture models to learn the parameters of correct and incorrect PSM score distributions. While conceptually straightforward, both approaches lead to problems in practice, particularly in experiments that push instrumentation to the limit and generate low fragmentation-efficiency and low signal-to-noise-ratio spectra. Results We introduce a new decoy-free framework for FDR estimation that generalizes present DFAs while exploiting more search data in a manner similar to TDAs. Our approach relies on multi-component mixtures, in which score distributions corresponding to the correct PSMs, best incorrect PSMs and second-best incorrect PSMs are modeled by the skew normal family. We derive EM algorithms to estimate parameters of these distributions from the scores of best and second-best PSMs associated with each experimental spectrum. We evaluate our models on multiple proteomics datasets and a HeLa cell digest case study consisting of more than a million spectra in total. We provide evidence of improved performance over existing DFAs and improved stability and speed over TDAs without any performance degradation. We propose that the new strategy has the potential to extend beyond peptide identification and reduce the need for TDA on all analytical platforms. Availabilityand implementation https://github.com/shawn-peng/FDR-estimation. Supplementary information Supplementary data are available at Bioinformatics online.

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