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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)
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)
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)
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)
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)
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)