Zobrazeno 1 - 10
of 101
pro vyhledávání: '"Zarepour, Mahmoud"'
Autor:
Chegini, Sadegh, Zarepour, Mahmoud
An important functional of Poisson random measure is the negative binomial process (NBP). We use NBP to introduce a generalized Poisson-Kingman distribution and its corresponding random discrete probability measure. This random discrete probability m
Externí odkaz:
http://arxiv.org/abs/2307.00176
Autor:
Hosseini, Reyhaneh, Zarepour, Mahmoud
In this paper, we describe a Bayesian nonparametric approach to make inference for a bivariate spherically symmetric distribution. We consider a Dirichlet invariant process prior on the set of all bivariate spherically symmetric distributions and we
Externí odkaz:
http://arxiv.org/abs/1807.11066
Autor:
Sohrabi, Maryam, Zarepour, Mahmoud
The limiting distribution for M-estimates in a non-stationary autoregressive model with heavy-tailed error is computationally intractable. To make inferences based on the M-estimates, the bootstrap procedure can be used to approximate the sampling di
Externí odkaz:
http://arxiv.org/abs/1603.02665
Autor:
Hosseini, Reyhaneh, Zarepour, Mahmoud
The Bayesian nonparametric inference and Dirichlet process are popular tools in statistical methodologies. In this paper, we employ the Dirichlet process in hypothesis testing to propose a Bayesian nonparametric chi-squared goodness-of-fit test. In o
Externí odkaz:
http://arxiv.org/abs/1602.00197
Autor:
Sohrabi, Maryam, Zarepour, Mahmoud
We consider a robust estimation of the mean vector for a sequence of i.i.d. observations in the domain of attraction of a stable law with different indices of stability, $DS(\alpha_1, \ldots, \alpha_p)$, such that $1<\alpha_{i}\leq 2$, $i=1,\ldots,p$
Externí odkaz:
http://arxiv.org/abs/1510.01811
Autor:
Sohrabi, Maryam, Zarepour, Mahmoud
In this paper, we present the asymptotic distribution of M-estimators for parameters in non-stationary AR(p) processes. The innovations are assumed to be in the domain of attraction of a stable law with index $0<\alpha\le2$. In particular, when the m
Externí odkaz:
http://arxiv.org/abs/1506.05830
Autor:
Labadi, Luai Al, Zarepour, Mahmoud
The beta process has recently been widely used as a nonparametric prior for different models in machine learning, including latent feature models. In this paper, we prove the asymptotic consistency of the finite dimensional approximation of the beta
Externí odkaz:
http://arxiv.org/abs/1411.3434
In recent years, Bayesian nonparametric statistics has gathered extraordinary attention. Nonetheless, a relatively little amount of work has been expended on Bayesian nonparametric hypothesis testing. In this paper, a novel Bayesian nonparametric app
Externí odkaz:
http://arxiv.org/abs/1411.3427
Autor:
QI, WEINAN, ZAREPOUR, MAHMOUD
Publikováno v:
Journal of Applied Probability, 2019 Dec 01. 56(4), 959-980.
Externí odkaz:
https://www.jstor.org/stable/45277616
Autor:
Labadi, Luai Al, Zarepour, Mahmoud
In this paper, we develop simple, yet efficient, procedures for sampling approximations of the two-Parameter Poisson-Dirichlet Process and the normalized inverse-Gaussian process. We compare the efficiency of the new approximations to the correspondi
Externí odkaz:
http://arxiv.org/abs/1209.5359