scholarly journals Weighted Means, Strict Ergodicity, and Uniform Distributions

2005 ◽  
Vol 78 (3-4) ◽  
pp. 329-337 ◽  
Author(s):  
V. V. Kozlov
Keyword(s):  
Uniform Distributions ◽  
Weighted Means

An Introduction to Statistical Models for Networks

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.

scholarly journals Swarm Learning for decentralized and confidential clinical machine learning

Nature ◽  
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.

scholarly journals Distributions associated with simultaneous multiple hypothesis testing

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.

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

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.

scholarly journals Spatio-temporal expectile regression models

2019 ◽  
Vol 20 (4) ◽  
pp. 386-409
Author(s):  
Elmar Spiegel ◽  
Thomas Kneib ◽  
Fabian Otto-Sobotka
Keyword(s):  
Tensor Product ◽  
Least Squares Regression ◽  
Expectile Regression ◽  
Specific Distribution ◽  
Weighted Means ◽  
Temporal Models ◽  
Error Terms ◽  
Spatio Temporal ◽  
Set Up ◽  
Main Effects

Spatio-temporal models are becoming increasingly popular in recent regression research. However, they usually rely on the assumption of a specific parametric distribution for the response and/or homoscedastic error terms. In this article, we propose to apply semiparametric expectile regression to model spatio-temporal effects beyond the mean. Besides the removal of the assumption of a specific distribution and homoscedasticity, with expectile regression the whole distribution of the response can be estimated. For the use of expectiles, we interpret them as weighted means and estimate them by established tools of (penalized) least squares regression. The spatio-temporal effect is set up as an interaction between time and space either based on trivariate tensor product P-splines or the tensor product of a Gaussian Markov random field and a univariate P-spline. Importantly, the model can easily be split up into main effects and interactions to facilitate interpretation. The method is presented along the analysis of spatio-temporal variation of temperatures in Germany from 1980 to 2014.

scholarly journals The role of vitamin D in metabolic disturbances in polycystic ovary syndrome: a systematic review

2013 ◽  
Vol 169 (6) ◽  
pp. 853-865 ◽  
Author(s):  
Y H M Krul-Poel ◽  
C Snackey ◽  
Y Louwers ◽  
P Lips ◽  
C B Lambalk ◽  
...  
Keyword(s):  
Systematic Review ◽  
Vitamin D ◽  
Clinical Trials ◽  
Polycystic Ovary Syndrome ◽  
Polycystic Ovary ◽  
Vitamin D Status ◽  
Regression Analyses ◽  
Metabolic Disturbances ◽  
Ovary Syndrome ◽  
Weighted Means

ContextMetabolic disturbances, in particular, insulin resistance (IR) and dyslipidemia, are common in women suffering from polycystic ovary syndrome (PCOS). Evidence is accumulating that vitamin D status may contribute to the development of metabolic disturbances in PCOS.ObjectiveThe aim of this study was to carry out a systematic review addressing the association between vitamin D status, vitamin D receptor polymorphisms, and/or polymorphisms related to vitamin D metabolism and metabolic disturbances in women with PCOS.Design and methodsA systematic search of electronic databases was carried out up to January 2013 for observational studies and clinical trials in women suffering from PCOS with outcome measures that were related to vitamin D status. We conducted univariate and multivariate regression analyses of the weighted means to gain insights into the association between vitamin D, BMI, and IR based on existing literature.ResultsWe found 29 eligible trials with inconsistency in their results. One well-designed randomized controlled trial has been carried out until now. Univariate regression analyses of the weighted means revealed vitamin D to be a significant and independent predictor of IR in both PCOS and control women. The significance disappeared after adjustment for BMI in PCOS women.ConclusionsCurrent evidence suggests an inverse association between vitamin D status and metabolic disturbances in PCOS. Owing to the heterogeneity of the studies, it is hard to draw a definite conclusion. The causal relationship between vitamin D status and metabolic disturbances in PCOS remains to be determined in well-designed placebo-controlled randomized clinical trials.

Index Numbers and Weighted Means

1952 ◽  
Vol 19 (1) ◽  
pp. 90
Author(s):  
Dudley J. Cowden ◽  
R. W. Pfouts
Keyword(s):  
Index Numbers ◽  
Weighted Means

scholarly journals Survey of water proton longitudinal relaxation in liver in vivo

2021 ◽  
Author(s):  
John Charles Waterton
Keyword(s):  
Normal Liver ◽  
Population Variation ◽  
Water Proton ◽  
Proton Relaxation ◽  
Literature Searching ◽  
Repeatability Error ◽  
Coefficients Of Variation ◽  
Weighted Means ◽  
Propagation Of Errors

Abstract Objective To determine the variability, and preferred values, for normal liver longitudinal water proton relaxation rate R1 in the published literature. Methods Values of mean R1 and between-subject variance were obtained from literature searching. Weighted means were fitted to a heuristic and to a model. Results After exclusions, 116 publications (143 studies) remained, representing apparently normal liver in 3392 humans, 99 mice and 249 rats. Seventeen field strengths were included between 0.04 T and 9.4 T. Older studies tended to report higher between-subject coefficients of variation (CoV), but for studies published since 1992, the median between-subject CoV was 7.4%, and in half of those studies, measured R1 deviated from model by 8.0% or less. Discussion The within-study between-subject CoV incorporates repeatability error and true between-subject variation. Between-study variation also incorporates between-population variation, together with bias from interactions between methodology and physiology. While quantitative relaxometry ultimately requires validation with phantoms and analysis of propagation of errors, this survey allows investigators to compare their own R1 and variability values with the range of existing literature.

Sign in / Sign up

Export Citation Format

Share Document