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pro vyhledávání: '"Lahiri S."'
We combine stochastic thermodynamics, large deviation theory, and information theory to derive fundamental limits on the accuracy with which single cell receptors can estimate external concentrations. As expected, if estimation is performed by an ide
Externí odkaz:
http://arxiv.org/abs/2002.10567
Selection of important covariates and to drop the unimportant ones from a high-dimensional regression model is a long standing problem and hence have received lots of attention in the last two decades. After selecting the correct model, it is also im
Externí odkaz:
http://arxiv.org/abs/1909.06649
Publikováno v:
In Chemical Engineering and Processing - Process Intensification April 2022 174
Autor:
Das, Debraj, Lahiri, S. N.
Least Absolute Shrinkage and Selection Operator or the Lasso, introduced by Tibshirani (1996), is a popular estimation procedure in multiple linear regression when underlying design has a sparse structure, because of its property that it sets some re
Externí odkaz:
http://arxiv.org/abs/1710.10709
The Adaptive Lasso(Alasso) was proposed by Zou [\textit{J. Amer. Statist. Assoc. \textbf{101} (2006) 1418-1429}] as a modification of the Lasso for the purpose of simultaneous variable selection and estimation of the parameters in a linear regression
Externí odkaz:
http://arxiv.org/abs/1703.03165
Autor:
Lahiri, S. N., Robinson, Peter M.
Publikováno v:
Bernoulli 2016, Vol. 22, No. 1, 345-375
Central limit theorems are established for the sum, over a spatial region, of observations from a linear process on a $d$-dimensional lattice. This region need not be rectangular, but can be irregularly-shaped. Separate results are established for th
Externí odkaz:
http://arxiv.org/abs/1601.01131
Publikováno v:
In Ultrasonics Sonochemistry August 2021 76
Publikováno v:
In Ultrasonics Sonochemistry May 2021 73