#### Filter Results:

- Full text PDF available (11)

#### Publication Year

2006

2017

- This year (1)
- Last 5 years (7)
- Last 10 years (10)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Key Phrases

Learn More

Consider a probit regression problem in which Y 1 , ..., Y n are independent Bernoulli random variables such that Pr.Y i D 1/ D Φ.x T i β/ where x i is a p-dimensional vector of known covariates that are associated with Y i , β is a p-dimensional vector of unknown regression coefficients and Φ./ denotes the standard normal distribution function. We study… (More)

- Vivekananda Roy, James P. Hobert
- J. Multivariate Analysis
- 2010

We consider Bayesian analysis of data from multivariate linear regression models whose errors have a distribution that is a scale mixture of normals. Such models are used to analyze data on financial returns, which are notoriously heavy-tailed. Let π denote the intractable posterior density that results when this regression model is combined with the… (More)

- Vivekananda Roy, Evangelos Evangelou, Zhengyuan Zhu
- Biometrics
- 2016

Spatial generalized linear mixed models (SGLMMs) are popular models for spatial data with a non-Gaussian response. Binomial SGLMMs with logit or probit link functions are often used to model spatially dependent binomial random variables. It is known that for independent binomial data, the robit regression model provides a more robust (against extreme… (More)

The reversible Markov chains that drive the data augmentation (DA) and sandwich algorithms define self-adjoint operators whose spectra encode the convergence properties of the algorithms. When the target distribution has uncountable support, as is nearly always the case in practice, it is generally quite difficult to get a handle on these spectra. We show… (More)

We consider Bayesian analysis of data from multivariate linear regression models whose errors have a distribution that is a scale mixture of normals. Such models are used to analyze data on financial returns, which are notoriously heavy-tailed. Let π denote the intractable posterior density that results when this regression model is combined with the… (More)

A sequence of random variables {X n } n≥0 is called regenerative if it can be broken up into iid components. The problem addressed in this paper is to determine under what conditions is a Markov chain regenerative. It is shown that an irreducible Markov chain with a countable state space is regenerative for any initial distribution if and only if it is… (More)

Estimating standard errors for importance sampling estimators with multiple Markov chains" (2015). Abstract The naive importance sampling estimator based on the samples from a single importance density can be extremely numerically unstable. We consider multiple distributions importance sampling es-timators where samples from more than one probability… (More)

- Krishna B. Athreya, Vivekananda Roy
- J. Applied Probability
- 2015

Let f be an integrable function on an infinite measure space (S, S, π). We show that if a regenerative sequence {Xn} n≥0 with canonical measure π could be generated then a consistent estimator of λ ≡ S f dπ can be produced. We further show that under appropriate second moment conditions, a confidence interval for λ can also be derived. This is illustrated… (More)

- Vivekananda Roy
- Computational Statistics & Data Analysis
- 2014

It is known that the robit regression model for binary data is a robust alternative to the more popular probit and logistic models. The robit model is obtained by replacing the normal distribution in the probit regression model with the Student's t distribution. Unlike the probit and logistic models, the robit link has an extra degrees of freedom (df)… (More)

- Anthony R. Sindt, Vivekananda Roy
- 2016