Bayesian Estimation for Item Factor Analysis Models with Sparse Categorical Indicators

Robust Estimation in Linear Regression with Molticollinearity and Sparse Models

‎One of the factors affecting the statistical analysis of the data is the presence of outliers‎. ‎The methods which are not affected by the outliers are called robust methods‎. ‎Robust regression methods are robust estimation methods of regression model parameters in the presence of outliers‎. ‎Besides outliers‎, ‎the linear dependency of regressor variables‎, ‎which is called multicollinearity...

Sparse Bayesian infinite factor models.

We focus on sparse modelling of high-dimensional covariance matrices using Bayesian latent factor models. We propose a multiplicative gamma process shrinkage prior on the factor loadings which allows introduction of infinitely many factors, with the loadings increasingly shrunk towards zero as the column index increases. We use our prior on a parameter-expanded loading matrix to avoid the order...

Bayesian Models for Categorical Data

Bayesian Models for Structured Sparse Estimation via Set Cover Prior

A number of priors have been recently developed for Bayesian estimation of sparse models. In many applications the variables are simultaneously relevant or irrelevant in groups, and appropriately modeling this correlation is important for improved sample efficiency. Although group sparse priors are also available, most of them are either limited to disjoint groups, or do not infer sparsity at g...

Bayesian Estimation and Model Choice in Item Response Models

Item response models are essential tools for analyzing results from many placement tests. Such models are used to quantify the probability of correct response as a function of unobserved examinee ability and other parameters explaining the diiculty and the discriminatory power of the questions in the test. Some of these models also incorporate a threshold parameter for the probability of the co...