Model Based Co-clustering of Mixed Numerical and Binary Data

2019 ◽  
pp. 3-22
Author(s):  
Aichetou Bouchareb ◽  
Marc Boullé ◽  
Fabrice Clérot ◽  
Fabrice Rossi
Keyword(s):  
Binary Data ◽  
Model Based

Model-based goodness-of-fit tests for the ordered stereotype model

2019 ◽  
Vol 29 (6) ◽  
pp. 1527-1541
Author(s):  
Daniel Fernández ◽  
Ivy Liu ◽  
Richard Arnold ◽  
Thuong Nguyen ◽  
Martin Spiess
Keyword(s):  
Alternative Model ◽  
Binary Data ◽  
Goodness Of Fit ◽  
Null Model ◽  
Response Variable ◽  
Model Based ◽  
Ordinal Response ◽  
Goodness Of Fit Tests ◽  
Lemeshow Test ◽  
Group Effects

This paper presents two new model-based goodness-of-fit tests for the ordered stereotype model applied to an ordinal response variable. The proposed tests are based on the Lipsitz test, which partitions the subjects into G groups following the popular Hosmer–Lemeshow test for binary data. The tests construct an alternative model where group effects are added into the null model. If the model fits the data well then the null model is correct, and there should be no group effects. One of the main advantages of the ordered stereotype model is that it allows us to determine a new uneven spacing of the ordinal response categories, dictated by the data. The two proposed tests use this new adjusted spacing. One test uses the form of the original ordered stereotype model, and the other uses an ordinary linear model. We demonstrate the performance of both tests under a variety of scenarios. Finally, the results of the application in three examples are presented.

scholarly journals A Sample Covariance-Based Approach For Spatial Binary Data

2020 ◽  
Author(s):  
Sahar Zarmehri ◽  
Ephraim M. Hanks ◽  
Lin Lin
Keyword(s):  
Infectious Disease ◽  
Binary Data ◽  
Isolation By Distance ◽  
Linear Mixed Effect Model ◽  
Nucleotide Polymorphisms ◽  
Mixed Effect ◽  
Model Based ◽  
Rate Of Spread ◽  
Novel Approach ◽  
Sample Covariance

AbstractThe field of landscape genetics enables the study of infectious disease dynamics by connecting the landscape features with evolutionary changes. Quantifying genetic correlation across space is helpful in providing insight into the rate of spread of an infectious disease. We investigate two genetic patterns in spatially referenced single-nucleotide polymorphisms (SNPs): isolation by distance and isolation by resistance. We model the data using a Generalized Linear Mixed effect Model (GLMM) with spatially referenced random effects and provide a novel approach for estimating parameters in spatial GLMMs. In this approach, we use the links between binary probit models and bivariate normal probabilities to directly compute the model-based covariance function for spatial binary data. Parameter estimation is based on minimizing sum of squared distance between the elements of sample covariance and model-based covariance matrices. We analyze data including Brucella Abortus SNPs from spatially referenced hosts in the Greater Yellowstone Ecosystem (GYE).

scholarly journals BayesBinMix: an R Package for Model Based Clustering of Multivariate Binary Data

The R Journal ◽  
2017 ◽  
Vol 9 (1) ◽  
pp. 403 ◽  
Author(s):  
Panagiotis Papastamoulis ◽  
Magnus Rattray
Keyword(s):  
Binary Data ◽  
R Package ◽  
Model Based Clustering ◽  
Model Based ◽  
Multivariate Binary Data

scholarly journals Model based clustering of high-dimensional binary data

2015 ◽  
Vol 87 ◽  
pp. 84-101 ◽  
Author(s):  
Yang Tang ◽  
Ryan P. Browne ◽  
Paul D. McNicholas
Keyword(s):  
Binary Data ◽  
High Dimensional ◽  
Model Based Clustering ◽  
Model Based

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

2001 ◽  
Vol 7 (S2) ◽  
pp. 578-579
Author(s):  
David W. Knowles ◽  
Sophie A. Lelièvre ◽  
Carlos Ortiz de Solόrzano ◽  
Stephen J. Lockett ◽  
Mina J. Bissell ◽  
...  
Keyword(s):  
Image Analysis ◽  
Mammary Epithelial Cells ◽  
Nuclear Organization ◽  
Critical Role ◽  
Quantitative Model ◽  
Mammary Epithelial ◽  
Cell Phenotype ◽  
Proliferating Cells ◽  
Mitotic Apparatus ◽  
Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

Sign in / Sign up

Export Citation Format

Share Document