Zobrazeno 1 - 10
of 368
pro vyhledávání: '"Mandal, Abhijit"'
This paper introduces a robust estimation strategy for the spatial functional linear regression model using dimension reduction methods, specifically functional principal component analysis (FPCA) and functional partial least squares (FPLS). These te
Externí odkaz:
http://arxiv.org/abs/2410.19140
A function-on-function regression model with quadratic and interaction effects of the covariates provides a more flexible model. Despite several attempts to estimate the model's parameters, almost all existing estimation strategies are non-robust aga
Externí odkaz:
http://arxiv.org/abs/2410.18338
Preserving multipartite entanglement amidst decoherence poses a pivotal challenge in quantum information processing. Also the measurement of multipartite entanglement in mixed states amid decoherence presents a formidable task. Employing reservoir me
Externí odkaz:
http://arxiv.org/abs/2408.09801
Eylee Jung \textit{et.al}\cite{jung2008} had conjectured that $P_{max}=\frac{1}{2}$ is a necessary and sufficient condition for the perfect two-party teleportation and consequently the Groverian measure of entanglement for the entanglement resource m
Externí odkaz:
http://arxiv.org/abs/2407.11519
The panel data regression models have become one of the most widely applied statistical approaches in different fields of research, including social, behavioral, environmental sciences, and econometrics. However, traditional least-squares-based techn
Externí odkaz:
http://arxiv.org/abs/2108.02408
The presence of outlying observations may adversely affect statistical testing procedures that result in unstable test statistics and unreliable inferences depending on the distortion in parameter estimates. In spite of the fact that the adverse effe
Externí odkaz:
http://arxiv.org/abs/2104.07723
We propose a new sampling algorithm combining two quite powerful ideas in the Markov chain Monte Carlo literature -- adaptive Metropolis sampler and two-stage Metropolis-Hastings sampler. The proposed sampling method will be particularly very useful
Externí odkaz:
http://arxiv.org/abs/2101.00118
Density-based minimum divergence procedures represent popular techniques in parametric statistical inference. They combine strong robustness properties with high (sometimes full) asymptotic efficiency. Among density-based minimum distance procedures,
Externí odkaz:
http://arxiv.org/abs/2012.11735
Autor:
Sarkar, Md Muttakin, Choudhury, Subhankar, Mandal, Abhijit, Mazumdar, Sourav, Ghosh, Narendra Nath, Chattopadhyay, Asoke P., Roy, Brindaban, Baildya, Nabajyoti
Publikováno v:
In Next Sustainability 2024 4
Autor:
Mandal, Abhijit, Ghosh, Samiran
We propose a robust variable selection procedure using a divergence based M-estimator combined with a penalty function. It produces robust estimates of the regression parameters and simultaneously selects the important explanatory variables. An effic
Externí odkaz:
http://arxiv.org/abs/1912.12550