48 citations · 126 across the 51 of their papers we have counts for
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A Bayesian Multiple Testing Paradigm for Model Selection in Inverse Regression Problems
Debashis Chatterjee, Sourabh Bhattacharya
In this article, we propose a novel Bayesian multiple testing formulation for model and variable selection in inverse setups, judiciously embedding the idea of inverse reference di…
Convergence of Pseudo-Bayes Factors in Forward and Inverse Regression Problems
Debashis Chatterjee, Sourabh Bhattacharya
In the Bayesian literature on model comparison, Bayes factors play the leading role. In the classical statistical literature, model selection criteria are often devised used cross-…
Increasing Domain Infill Asymptotics for Stochastic Differential Equations Driven by Fractional Brownian Motion
Trisha Maitra, Sourabh Bhattacharya
Although statistical inference in stochastic differential equations (SDEs) driven by Wiener process has received significant attention in the literature, inference in those driven…
Posterior Consistency of Bayesian Inverse Regression and Inverse Reference Distributions
Debashis Chatterjee, Sourabh Bhattacharya
We consider Bayesian inference in inverse regression problems where the objective is to infer about unobserved covariates from observed responses and covariates. We establish poste…
Posterior Convergence of Nonparametric Binary and Poisson Regression Under Possible Misspecifications
Debashis Chatterjee, Sourabh Bhattacharya
In this article, we investigate posterior convergence of nonparametric binary and Poisson regression under possible model misspecification, assuming general stochastic process prio…
High-dimensional Asymptotic Theory of Bayesian Multiple Testing Procedures Under General Dependent Setup and Possible Misspecification
Noirrit Kiran Chandra, Sourabh Bhattacharya
In this article, we investigate the asymptotic properties of Bayesian multiple testing procedures under general dependent setup, when the sample size and the number of hypotheses b…
Bayesian Characterizations of Properties of Stochastic Processes with Applications
Sucharita Roy, Sourabh Bhattacharya
In this article, we primarily propose a novel Bayesian characterization of stationary and nonstationary stochastic processes. In practice, this theory aims to distinguish between g…
Posterior Convergence of Gaussian and General Stochastic Process Regression Under Possible Misspecifications
Debashis Chatterjee, Sourabh Bhattacharya
In this article, we investigate posterior convergence in nonparametric regression models where the unknown regression function is modeled by some appropriate stochastic process. In…
Bayes Factor Asymptotics for Variable Selection in the Gaussian Process Framework
Minerva Mukhopadhyay, Sourabh Bhattacharya
Although variable selection is one of the most popular areas of modern statistical research, much of its development has taken place in the classical paradigm compared to the Bayes…
A Short Note on Almost Sure Convergence of Bayes Factors in the General Set-Up
Debashis Chatterjee, Trisha Maitra, Sourabh Bhattacharya
Although there is a significant literature on the asymptotic theory of Bayes factor, the set-ups considered are usually specialized and often involves independent and identically d…
Asymptotic Theory of Dependent Bayesian Multiple Testing Procedures Under Possible Model Misspecification
Noirrit K. Chandra, Sourabh Bhattacharya
We study asymptotic properties of Bayesian multiple testing procedures and provide sufficient conditions for strong consistency under general dependence structure. We also consider…
On Classical and Bayesian Asymptotics in Stochastic Differential Equations with Random Effects having Mixture Normal Distributions
Trisha Maitra, Sourabh Bhattacharya
Delattre et al. (2013) considered a system of stochastic differential equations (SDEs) in a random effects setup. Under the independent and identical (iid) situation, and assuming…
On Asymptotic Inference in Stochastic Differential Equations with Time-Varying Covariates
Trisha Maitra, Sourabh Bhattacharya
In this article, we introduce a system of stochastic differential equations (SDEs) consisting of time-dependent covariates and consider both fixed and random effects set-ups. We al…
Bayes meet Riemann- Bayesian Characterization of Infinite Series with Application to Riemann Hypothesis
Sucharita Roy, Sourabh Bhattacharya
In the classical literature on infinite series there are various tests to determine if a given infinite series converges, diverges, or oscillates. But unfortunately, for very many…
On Classical and Bayesian Asymptotics in State Space Stochastic Differential Equations
Trisha Maitra, Sourabh Bhattacharya
In this article we investigate consistency and asymptotic normality of the maximum likelihood and the posterior distribution of the parameters in the context of state space stochas…
Asymptotic Theory of Bayes Factor in Stochastic Differential Equations: Part II
Trisha Maitra, Sourabh Bhattacharya
The problem of model selection in the context of a system of stochastic differential equations (SDEs) has not been touched upon in the literature. Indeed, properties of Bayes facto…
Asymptotic Theory of Bayes Factor in Stochastic Differential Equations: Part I
Trisha Maitra, Sourabh Bhattacharya
Research on asymptotic model selection in the context of stochastic differential equations (SDEs) is almost non-existent in the literature. In particular, when a collection of SDEs…
On Bayesian Asymptotics in Stochastic Differential Equations with Random Effects
Trisha Maitra, Sourabh Bhattacharya
Delattre et al. (2013) investigated asymptotic properties of the maximum likelihood estimator of the population parameters of the random effects associated with n independent stoch…
On Asymptotics Related to Classical Inference in Stochastic Differential Equations with Random Effects
Trisha Maitra, Sourabh Bhattacharya
Delattre et al. (2013) considered n independent stochastic differential equations (SDEs), where in each case the drift term is associated with a random effect, the distribution of…
Bayesian MISE convergence rates of Polya urn based density estimators: asymptotic comparisons and choice of prior parameters
Sabyasachi Mukhopadhyay, Sourabh Bhattacharya
Mixture models are well-known for their versatility, and the Bayesian paradigm is a suitable platform for mixture analysis, particularly when the number of components is unknown. B…