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On the Analysis of Over-Dispersed Categorical Data
2021In Chaps. 7 and 8, our focus has been on describing some of the technical aspects of reciprocal averaging (and canonical correlation analysis), so that one can obtain row and column scores that maximize the association between the variables of a two-way contingency table. The foundations under which, such discussions are laid, rests upon the assumption
Shizuhiko Nishisato +3 more
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Modeling Over-Dispersion for Network Data Clustering
2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), 2017Over-dispersed network data mining has emerged as a central theme in data science, evident by a sharp increase in the volume of real-world network data with imbalanced clusters.While most of existing clustering methods are designed for discovering the number of clusters and class specific connectivity patterns, few methods are available to uncover the ...
Lu Wang 0004 +3 more
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The over-dispersion between cells of chromosomal aberrations
Journal of Theoretical Biology, 1979Abstract A survey of the literature suggests that random dispersion of radiation induced aberrations occurs only when uniform fields of predominantly low LET radiations act on cell populations which are homogeneous with regard to cell type, cycle stage and intrinsic radiosensitivity.
D, Couzin, D G, Papworth
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An extension of an over-dispersion test for count data
Computational Statistics & Data Analysis, 2011zbMATH Open Web Interface contents unavailable due to conflicting licenses.
M. Fazil Baksh +2 more
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Confidence Interval of a Proportion with Over-dispersion
Biometrical Journal, 2002A new approach that extends the classical Clopper-Pearson procedure is proposed for the estimation of the (1–α)% confidence interval of a proportion with over-dispersion. Over-dispersion occurs when a proportion of interest shows more variation (variance inflation) than predicted by the binomial distribution. There are two steps in the approach. The
Cong Chen, Robert W. Tipping
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Over‐dispersed count data in crop and agronomy research
Journal of Agronomy and Crop Science, 2019AbstractWhile evaluating plant response to biotic or abiotic stress and genotype–environment interactions and searching causes of yield gap, very often are observed data with non‐normal distributions. One of the commonly encountered types of variables with a non‐normal distribution is count data. Count data are defined as the type of observations which
Michał Kosma +5 more
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A computer program for the analysis of over-dispersed counts and proportions
Computer Methods and Programs in Biomedicine, 1997Over-dispersed binary and count data occur frequently in many fields of application. Examples include occurrence of cavities in one or more teeth, and development of tumors in one or more animals of a litter. Methods of statistical analyses that ignore correlation between observations underestimate the standard errors.
C, Ahn, J, Lee
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SPARCLE: Stream Processing Applications over Dispersed Computing Networks
2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS), 2020In this paper, we propose SPARCLE, a novel scheduling system offering network-aware polynomial-time task assignment and resource allocation algorithms for stream processing applications in dispersed computing networks. In particular, we address two major challenges.
Parisa Rahimzadeh +8 more
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