For the problem of model choice in linear regression, we introduce a Bayesian adaptive sampling algorithm (BAS), that samples models without replacement from space models. problems permit enumeration all models, BAS is guaranteed to enumerate 2p iterations where p number potential variables under consideration. larger required, provide conditions which provides perfect replacement. When probabilities are marginal variable inclusion probabilities, may be viewed as "near" median probability...

Abstract Factor analytic models are widely used in social sciences. These have also proven useful for sparse modeling of the covariance structure multidimensional data. Normal prior distributions factor loadings and inverse gamma residual variances a popular choice because their conditionally conjugate form. However, such require elicitation many hyperparameters tend to result poorly behaved Gibbs samplers. In addition, one must choose an informative specification, as high variance face...

In logistic regression, separation occurs when a linear combination of the predictors can perfectly classify part or all observations in sample, and as result, finite maximum likelihood estimates regression coefficients do not exist. Gelman et al. (2008) recommended independent Cauchy distributions default priors for even case separation, reported posterior modes their analyses. As mean does exist prior, natural question is whether means under separation. We prove theorems that provide...

In this article, we highlight some interesting facts about Bayesian variable selection methods for linear regression models in settings where the design matrix exhibits strong collinearity. We first demonstrate via real data analysis and simulation studies that summaries of posterior distribution based on marginal joint distributions may give conflicting results assessing importance strongly correlated covariates. The natural question is which one should be used practice. suggest inclusion...

ABSTRACT There is an increasing prevalence of streaming data generation in diverse fields like healthcare, finance, social media, and weather forecasting. In order to acquire helpful insights from these massive datasets, timely analysis essential. this article, we assume that the are analysed batches. Traditional offline methods, which involve storing analysing all individual records, can be repeatedly applied cumulative data, but encounter significant challenges storage computing costs....

Choosing the subset of covariates to use in regression or generalized linear models is a ubiquitous problem. The Bayesian paradigm addresses problem model uncertainty by considering corresponding all possible subsets covariates, where posterior distribution over used select combine them via averaging (BMA). Although conceptually straightforward, BMA often difficult implement practice, since either number too large for enumeration subsets, calculations cannot be done analytically, both. For...

Abstract Our improved capability to adapt the future changes in discharge is linked our predict magnitude or at least direction of these changes. For agricultural United States Midwest, too much little water has severe socioeconomic impacts. Here, we focus on Raccoon River Van Meter, Iowa, and use a statistical approach examine projected discharge. We build models using rainfall harvested corn soybean acreage explain observed variability. then projections two predictors response. Results are...

The impact of rainfall interpolation techniques and unit hydrograph estimation has been explored for four gauged locations in the Brahmani basin east India. use ground-based satellite-based data, coupled with testing two (Thiessen polygon inverse distance weighting), can yield improved estimates fits to observed flows. Due presence significant errors areal estimate it was found that identification known data assist focusing model calibration on catchment response, thereby reducing...

In this article, we develop a latent class model with probabilities that depend on subject-specific covariates. One of our major goals is to identify important predictors classes. We consider methodology allows estimation classes while allowing for variable selection uncertainty. propose Bayesian approach and implement stochastic search Gibbs sampler posterior computation obtain model-averaged estimates quantities interest such as marginal inclusion predictors. Our methods are illustrated...

Assume that every probability measure $P$ in $\mathscr{P}$ of a statistical structure $(X, \mathscr{A}, \mathscr{P})$ has density $p(x, P)$ w.r.t. (not necessarily $\sigma$-finite) $m$. Let $\mathscr{B}$ be any subfield and suppose the densities are factored as P) = g(x, P)h(x)$ where $g$ is $\mathscr{B}$-measurable. Then pairwise sufficient contains supports $P$'s. further $m$ locally localizable above. Two partial orders introduced for subfields. Assuming support, constructed which...

Journal Article Finite population estimators in stochastic search variable selection Get access Merlise A. Clyde, Clyde Department of Statistical Science, Duke University, Durham, North Carolina 27708-0251, U.S.A., clyde@stat.duke.edu Search for other works by this author on: Oxford Academic Google Scholar Joyee Ghosh Statistics and Actuarial The University Iowa, Iowa City, 52242-1409, joyee-ghosh@uiowa.edu Biometrika, Volume 99, Issue 4, December 2012, Pages 981–988,...