Zobrazeno 1 - 10
of 125
pro vyhledávání: '"Chaudhuri, Sanjay"'
Approximate Bayesian Computation (ABC) methods are applicable to statistical models specified by generative processes with analytically intractable likelihoods. These methods try to approximate the posterior density of a model parameter by comparing
Externí odkaz:
http://arxiv.org/abs/2403.05080
Autor:
Bhattacharyya, Jhimli, Kumar, Gopinatha Suresh, Maiti, Souvik, Miyoshi, Daisuke, Chaudhuri, Sanjay
The phenomenon of hysteresis is commonly observed in many UV thermal experiments involving unmodified or modified nucleic acids. In presence of hysteresis, the thermal curves are irreversible and demand a significant effort to produce the reaction-sp
Externí odkaz:
http://arxiv.org/abs/2209.03957
In this article, we describe a {\tt R} package for sampling from an empirical likelihood-based posterior using a Hamiltonian Monte Carlo method. Empirical likelihood-based methodologies have been used in Bayesian modeling of many problems of interest
Externí odkaz:
http://arxiv.org/abs/2209.01289
Autor:
Chaudhuri, Sanjay, Yin, Teng
In recent times empirical likelihood has been widely applied under Bayesian framework. Markov chain Monte Carlo (MCMC) methods are frequently employed to sample from the posterior distribution of the parameters of interest. However, complex, especial
Externí odkaz:
http://arxiv.org/abs/2209.01269
We consider an empirical likelihood framework for inference for a statistical model based on an informative sampling design and population-level information. The population-level information is summarized in the form of estimating equations and incor
Externí odkaz:
http://arxiv.org/abs/2209.01247
Publikováno v:
In Journal of Molecular Liquids 1 September 2024 409
This paper introduces a general framework for estimating variance components in the linear mixed models via general unbiased estimating equations, which include some well-used estimators such as the restricted maximum likelihood estimator. We derive
Externí odkaz:
http://arxiv.org/abs/2105.07563
Many scientifically well-motivated statistical models in natural, engineering, and environmental sciences are specified through a generative process. However, in some cases, it may not be possible to write down the likelihood for these models analyti
Externí odkaz:
http://arxiv.org/abs/2011.07721
Autor:
Ghosh, Subhro, Chaudhuri, Sanjay
We investigate the problem of semi-parametric maximum likelihood under constraints on summary statistics. Such a procedure results in a discrete probability distribution that maximises the likelihood among all such distributions under the specified c
Externí odkaz:
http://arxiv.org/abs/1910.01396
Autor:
Chaudhuri, Sanjay, Handcock, Mark S.
Publikováno v:
Statistics and Applications, Volume 16, No. 1, 2018 (New Series), pp 245-268
We consider an empirical likelihood framework for inference for a statistical model based on an informative sampling design. Covariate information is incorporated both through the weights and the estimating equations. The estimator is based on condit
Externí odkaz:
http://arxiv.org/abs/1905.00803