On Inequalities Involving Moments Of Discrete Uniform Distributions

Two circular Polya distributions of orderkare derived by means of generalized urn models and by compounding, respectively, the type I and type II circular binomial distributions of orderkof Makri and Philippou (1994) with the beta distribution. It is noted that the above two distributions include, as special cases, new circular hypergeometric, negative hypergeometric, and discrete uniform distributions of the same order and type. The means of the new distributions are obtained and two asymptotic results are established relating them to the above-mentioned circular binomial distributions of orderk.

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An Introduction to Statistical Models for Networks

The Oxford Handbook of Social Networks ◽

10.1093/oxfordhb/9780190251765.013.17 ◽

2020 ◽

pp. 218-233

Author(s):

Valentina Kuskova ◽

Stanley Wasserman

Keyword(s):

Social System ◽

Exponential Family ◽

Behavioral Science ◽

Social Science Research ◽

Science Research ◽

Uniform Distributions ◽

The Social ◽

Special Cases ◽

Social And Behavioral Science ◽

Network Methods

Network theoretical and analytic approaches have reached a new level of sophistication in this decade, accompanied by a rapid growth of interest in adopting these approaches in social science research generally. Of course, much social and behavioral science focuses on individuals, but there are often situations where the social environment—the social system—affects individual responses. In these circumstances, to treat individuals as isolated social atoms, a necessary assumption for the application of standard statistical analysis is simply incorrect. Network methods should be part of the theoretical and analytic arsenal available to sociologists. Our focus here will be on the exponential family of random graph distributions, p*, because of its inclusiveness. It includes conditional uniform distributions as special cases.

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Swarm Learning for decentralized and confidential clinical machine learning

Nature ◽

10.1038/s41586-021-03583-3 ◽

2021 ◽

Author(s):

Stefanie Warnat-Herresthal ◽

◽

Hartmut Schultze ◽

Krishnaprasad Lingadahalli Shastry ◽

Sathyanarayanan Manamohan ◽

...

Keyword(s):

Machine Learning ◽

Precision Medicine ◽

Distributed Data ◽

X Ray ◽

Uniform Distributions ◽

Privacy Laws ◽

Data Owner ◽

Machine Learning Approach ◽

Chest X Ray ◽

Privacy Legislation

AbstractFast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.

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Distributions associated with simultaneous multiple hypothesis testing

Journal of Statistical Distributions and Applications ◽

10.1186/s40488-020-00109-6 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Chang Yu ◽

Daniel Zelterman

Keyword(s):

Latent Variable ◽

Power Analysis ◽

Marginal Distribution ◽

Multiple Hypothesis Testing ◽

Family Wise Error Rate ◽

Uniform Distributions ◽

Multiple Hypothesis ◽

The Family ◽

Alternative Hypotheses ◽

Cancer Studies

Abstract We develop the distribution for the number of hypotheses found to be statistically significant using the rule from Simes (Biometrika 73: 751–754, 1986) for controlling the family-wise error rate (FWER). We find the distribution of the number of statistically significant p-values under the null hypothesis and show this follows a normal distribution under the alternative. We propose a parametric distribution ΨI(·) to model the marginal distribution of p-values sampled from a mixture of null uniform and non-uniform distributions under different alternative hypotheses. The ΨI distribution is useful when there are many different alternative hypotheses and these are not individually well understood. We fit ΨI to data from three cancer studies and use it to illustrate the distribution of the number of notable hypotheses observed in these examples. We model dependence in sampled p-values using a latent variable. These methods can be combined to illustrate a power analysis in planning a larger study on the basis of a smaller pilot experiment.

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Stabilizing effect of methylcellulose on the dispersion of multi-walled carbon nanotubes in cementitious composites

Nanotechnology Reviews ◽

10.1515/ntrev-2020-0009 ◽

2020 ◽

Vol 9 (1) ◽

pp. 93-104

Author(s):

Mingrui Du ◽

Yuan Gao ◽

Guansheng Han ◽

Luan Li ◽

Hongwen Jing

Keyword(s):

Carbon Nanotubes ◽

Pore Solution ◽

Cementitious Materials ◽

High Ionic Strength ◽

Cementitious Composites ◽

Uniform Distributions ◽

Stabilizing Effect ◽

Multi Walled Carbon Nanotubes ◽

The Stability ◽

Walled Carbon Nanotubes

AbstractMulti-walled carbon nanotubes (MWCNTs) have been added in the plain cementitious materials to manufacture composites with the higher mechanical properties and smart behavior. The uniform distributions of MWCNTs is critical to obtain the desired enhancing effect, which, however, is challenged by the high ionic strength of the cement pore solution. Here, the effects of methylcellulose (MC) on stabilizing the dispersion of MWCNTs in the simulated cement pore solution and the viscosity of MWCNT suspensions werestudied. Further observations on the distributions of MWCNTs in the ternary cementitious composites were conducted. The results showed that MC forms a membranous envelope surrounding MWCNTs, which inhibits the adsorption of cations and maintains the steric repulsion between MWCNTs; thus, the stability of MWCNT dispersion in cement-based composites is improved. MC can also work as a viscosity adjuster that retards the Brownian mobility of MWCNTs, reducing their re-agglomerate within a period. MC with an addition ratio of 0.018 wt.% is suggested to achieve the optimum dispersion stabilizing effect. The findings here provide a way for stabilizing the other dispersed nano-additives in the cementitious composites.

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