Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Dey, Arabin Kumar"'
Autor:
Bhambu, Aryan, Dey, Arabin Kumar
This research paper introduces innovative approaches for multivariate time series forecasting based on different variations of the combined regression strategy. We use specific data preprocessing techniques which makes a radical change in the behavio
Externí odkaz:
http://arxiv.org/abs/2405.04539
Autor:
Bhambu, Aryan, Dey, Arabin Kumar
In this paper we propose a novel procedure to construct a confidence interval for multivariate time series predictions using long short term memory network. The construction uses a few novel block bootstrap techniques. We also propose an innovative b
Externí odkaz:
http://arxiv.org/abs/2211.13915
Autor:
Goswami, Rahul, Dey, Arabin Kumar
Recently Goswami et al. \cite{goswami2022concordance} introduced two novel implementations of combined regression strategy to find the conditional survival function. The paper uses regression-based weak learners and provides an alternative version of
Externí odkaz:
http://arxiv.org/abs/2210.12006
Autor:
Goswami, Rahul, Dey, Arabin Kumar
In this paper, we predict conditional survival functions through a combined regression strategy. We take weak learners as different random survival trees. We propose to maximize concordance in the right-censored set up to find the optimal parameters.
Externí odkaz:
http://arxiv.org/abs/2209.11919
In this paper, we show an innovative way to construct bootstrap confidence interval of a signal estimated based on a univariate LSTM model. We take three different types of bootstrap methods for dependent set up. We prescribe some useful suggestions
Externí odkaz:
http://arxiv.org/abs/2007.00254
This paper provides two different novel approaches of slice sampling to estimate the parameters of absolute continuous Marshall-Olkin bivariate Pareto distribution with location and scale parameters. We carry out the bayesian analysis taking gamma pr
Externí odkaz:
http://arxiv.org/abs/1809.06405
In this paper we formulate a four parameter absolute continuous Geometric Marshall-Olkin bivariate Pareto distribution and study its parameter estimation through EM algorithm and also explore the bayesian analysis through slice cum Gibbs sampler appr
Externí odkaz:
http://arxiv.org/abs/1809.06052
Autor:
Dey, Arabin Kumar, Jhamb, Himanshu
In this article we select the unknown dimension of the feature by re- versible jump MCMC inside a simulated annealing in bayesian set up of collaborative filter. We implement the same in MovieLens small dataset. We also tune the hyper parameter by us
Externí odkaz:
http://arxiv.org/abs/1808.05480
This paper provides bayesian analysis of singular Marshall-Olkin bivariate Pareto distribution. We consider three parameter singular Marshall-Olkin bivariate Pareto distribution. We consider two types of prior - reference prior and gamma prior. Bayes
Externí odkaz:
http://arxiv.org/abs/1709.05906
In this paper we describe a novel implementation of adaboost for prediction of survival function. We take different variations of the algorithm and compare the algorithms based on system run time and root mean square error. Our construction includes
Externí odkaz:
http://arxiv.org/abs/1709.05515