Track topics on Twitter Track topics that are important to you
We consider a stochastic blockmodel equipped with node covariate information, that is helpful in analyzing social network data. The key objective is to obtain maximum likelihood estimates of the model parameters. For this task, we devise a fast, scalable Monte Carlo EM type algorithm based on case-control approximation of the log-likelihood coupled with a subsampling approach. A key feature of the proposed algorithm is its parallelizability, by processing portions of the data on several cores, while leveraging communication of key statistics across the cores during each iteration of the algorithm. The performance of the algorithm is evaluated on synthetic data sets and compared with competing methods for blockmodel parameter estimation. We also illustrate the model on data from a Facebook derived social network enhanced with node covariate information.
This article was published in the following journal.
Name: Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
Linear scaling density functional theory is important for understanding electronic structure properties of nanometer scale systems. Recently developed stochastic density functional theory can achieve ...
Child abuse pediatricians (CAPs) are often asked to determine the likelihood that a particular child has been sexually abused. These determinations affect medical and legal interventions, and are impo...
Modern inverse radiotherapy treatment planning requires non-convex, large-scale optimizations that must be solved within a clinically feasible timeframe. We have developed and tested a quantum-inspire...
We propose autoregressive Bayesian semi-parametric models for gap times between recurrent events. The aim is two-fold: inference on the effect of possibly time-varying covariates on the gap times and ...
Studies often want to test for the association between an unmeasured covariate and an outcome. In the absence of a measurement, the study may substitute values generated from a prediction model. Justi...
This study evaluates the effectiveness of stochastic resonance electric stimulation on neuromuscular control and proprioception in healthy and individuals with stroke.
The purpose of this study is to examine the efficacy of a specially-constructed crib mattress that delivers gentle vibrations (stochastic vibrotactile stimulation) as a complementary, non-...
The aim of this research is to study the influence of stochastic modulated vibrations on the autonomic nervous system of breast cancer patients during radiation therapy
The interpersonal problems of adolescents with ADHD may be the most debilitating aspect of their psychopathologic behaviour. This being said, the investigators still do not have a clear un...
Obsessive-compulsive disorder (OCD) is a highly disabling psychiatric illness, characterized by obsessional thoughts that cause patients to perform time-consuming and distressing compulsiv...
A scale comprising 18 symptom constructs chosen to represent relatively independent dimensions of manifest psychopathology. The initial intended use was to provide more efficient assessment of treatment response in clinical psychopharmacology research; however, the scale was readily adapted to other uses. (From Hersen, M. and Bellack, A.S., Dictionary of Behavioral Assessment Techniques, p. 87)
The large scale production of pharmaceutically important and commercially valuable RECOMBINANT PROTEINS.
Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters.
A subjective psychometric response scale used to measure distinct behavioral or physiological phenomena based on linear numerical gradient or yes/no alternatives.
Conditional probability of exposure to a treatment given observed covariates.