A Cluster Analysis Method for Grouping Means in the Analysis of Variance

1974/09/01 by A. J. Scott, A.J. Scott, M. Knott · 64 citations
Mathematics · Decision Sciences · #Statistical Methods in Clinical Trials #Optimal Experimental Design Methods #Advanced Statistical Methods and Models

paper · doi:10.2307/2529204

Abstract

It is sometimes useful in an analysis of variance to split the treatments into reasonably homogeneous groups. Multiple comparison procedures are often used for this purpose, but a more direct method is to use the techniques of cluster analysis. This approach is illustrated for several sets of data, and a likelihood ratio test is developed for judging the significance of differences among the resulting groups.

Citations

• Multiple Range and Multiple F Tests
• Comparing Individual Means in the Analysis of Variance

Cited by

• Deep learning and its application in geochemical mapping
• Improved processing methods for eddy covariance measurements in calculating sensible heat fluxes at glacier surfaces
• Action of cannabidiol on the anxiety and other effects produced by ?9-THC in normal subjects
• On Optimal Multiple Changepoint Algorithms for Large Data
• Detecting changes in slope with an L₀ penalty
• How Low Can You Go? The Data-Light SE Challenge
• Change Point Detection for Functional Autoregressive Processes on the Sphere
• Predicting Community Smells' Occurrence on Individual Developers by Sentiments
• ANOVATS: A subsampling-based test to detect differences among short time series in marine studies
• Change-in-velocity detection for multidimensional data
• Phosphate solubilization by endophytic bacteria isolated from banana trees
• Influence of taxonomic resolution on the value of anthropogenic pollen indicators
• Optimal Detection of Changepoints With a Linear Computational Cost
• Data segmentation algorithms: Univariate mean change and beyond
• A log-linear time algorithm for constrained changepoint detection
• Using mobile phone data to evaluate access to essential services following natural hazards
• Efficient Likelihood-Based Temporal Changepoint Detection in Spatio-Temporal Processes
• UNIVARIATE AND MULTIVARIATE APPROACHES IN THE CHARACTERIZATION OF LIMA BEAN GENOTYPES
• A Digital Pheromone-Based Approach for In/Out-of-Control Classification
• Linearly penalized segmentation, binary segmentation, bottom-up segmentation and window-based methods for change point detection
• Consistent Kernel Change-Point Detection under m-Dependence for Text Segmentation
• Subspace Change-Point Detection via Low-Rank Matrix Factorisation
• Jump Interval-Learning for Individualized Decision Making
• Optimal nonparametric change point detection and localization
• Developer-LLM Conversations: An Empirical Study of Interactions and Generated Code Quality
• Minimal Data, Maximum Clarity: A Heuristic for Explaining Optimization
• Reducing overfitting in challenge-based competitions
• Why is Differential Evolution Better than Grid Search for Tuning Defect Predictors?
• Online Multivariate Changepoint Detection: Leveraging Links With Computational Geometry
• Prediction of tool-wear in turning of medical grade cobalt chromium\n molybdenum alloy (ASTM F75) using non-parametric Bayesian models
• How Secondary School Girls Perceive Computational Thinking Practices through Collaborative Programming with the Micro:bit
• Estimation of multiple change points under a generalised Ornstein-Uhlenbeck framework
• Determination of a structural break in a mean-reverting process
• Efficacy of Statistical and Artificial Intelligence-based False Information Cyberattack Detection Models for Connected Vehicles
• Changes in tropical cyclone activity in north Indian Ocean during satellite era (1981–2014)
• Univariate Mean Change Point Detection: Penalization, CUSUM and Optimality
• Do practitioners intentionally self-fix Technical Debt and why?
• Fixed-Confidence Multiple Change Point Identification under Bandit Feedback
• Dually Hierarchical Drift Adaptation for Online Configuration Performance Learning
• The discriminant power of RNA features for pre-miRNA recognition. [europepmc]
• Severity and disease control before house dust mite immunotherapy initiation: ANTARES a French observational survey. [europepmc]
• Automatic learning of pre-miRNAs from different species. [europepmc]
• Unsupervised Idealization of Ion Channel Recordings by Minimum Description Length: Application to Human PIEZO1-Channels. [europepmc]
• Label-free Raman hyperspectral imaging analysis localizes the cyanogenic glucoside dhurrin to the cytoplasm in sorghum cells. [europepmc]
• Rapid detection and differentiation of avian infectious bronchitis virus: an application of Mass genotype by melting temperature analysis in RT-qPCR using SYBR Green I. [europepmc]
• Detecting Multiple Change Points Using Adaptive Regression Splines With Application to Neural Recordings. [europepmc]
• Lactic Acid Bacteria as Biological Control of Staphylococcus aureus in Coalho Goat Cheese. [europepmc]
• Genetic effects on the efficiency and responsiveness to phosphorus use in popcorn as estimated by diallel analysis. [europepmc]
• A computationally efficient nonparametric approach for changepoint detection. [europepmc]
• Top-down machine learning approach for high-throughput single-molecule analysis. [europepmc]
• On optimal multiple changepoint algorithms for large data. [europepmc]
• Synergies between urban heat island and heat waves in Seoul: The role of wind speed and land use characteristics. [europepmc]
• Effectiveness of Push-Pull Systems to Fall Armyworm ( Spodoptera frugiperda ) Management in Maize Crops in Morelos, Mexico. [europepmc]
• Genetic mapping and QTL analysis for peanut smut resistance. [europepmc]
• COVID-19 hospitalizations in five California hospitals: a retrospective cohort study. [europepmc]
• Jasmonates promote enhanced production of bioactive caffeoylquinic acid derivative in Eclipta prostrata (L.) L. hairy roots. [europepmc]
• High-throughput phenotyping allows the selection of soybean genotypes for earliness and high grain yield. [europepmc]
• Reconstructing the historical expansion of industrial swine production from Landsat imagery. [europepmc]
• Time lag effects of COVID-19 policies on transportation systems: A comparative study of New York City and Seattle. [europepmc]
• Novel indicator for the spread of new coronavirus disease 2019 and its association with human mobility in Japan. [europepmc]
• Katdetectr: an R/bioconductor package utilizing unsupervised changepoint analysis for robust kataegis detection. [europepmc]
• Multi-trait selection for nutritional and physiological quality of cacao genotypes in irrigated and non-irrigated environments. [europepmc]
• Directly selecting cell-type marker genes for single-cell clustering analyses. [europepmc]
• Using cluster-based permutation tests to estimate MEG/EEG onsets: How bad is it? [europepmc]

Related

• Comparing Individual Means in the Analysis of Variance
• Complex Analyses of Variance: General Problems
• K‐means clustering: A half‐century synthesis
• Unique Metric for Health Analysis with Optimization of Clustering\n Activity and Cross Comparison of Results from Different Approach
• Post-clustering difference testing: valid inference and practical considerations