RG
Renu Garg
Ramanujan College, University of Delhi Kirori Mal College, Maharshi Dayanand University Rohtak, Chaudhary Charan Singh University, University of Delhi
Employment
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Ramanujan College Assistant Professor2022 - Present
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University of Delhi Kirori Mal College Assistant Professor2021 - 2022
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University of Delhi Assistant Professor2018 - 2021
Education
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Maharshi Dayanand University Rohtak Ph.D.2014 - 2017
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Chaudhary Charan Singh University M.Phil.2012 - 2013
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Chaudhary Charan Singh University M.Sc.2008 - 2010
Projects & Funding
Projects & funding information is unavailable.
Publications (21)
- Inference on multicomponent stress-strength model using progressively censored data from exponentiated Pareto distribution Save
- Classical and Bayesian estimation of multicomponent stress–strength reliability with power Lindley distribution under progressive first-failure censored samples Save
- Statistical inference on multicomponent stress–strength reliability with non-identical component strengths using progressively censored data from Kumaraswamy distribution Save
- Stress-strength reliability estimation of multicomponent system with non-identical strength components from inverse Pareto distribution Save
- Non-Bayesian and Bayesian estimation of stress-strength reliability from Topp-Leone distribution under progressive first-failure censoring Save
- Inference of multicomponent stress-strength reliability following Topp-Leone distribution using progressively censored data Save
- On estimation of P(Y < X) for inverse Pareto distribution based on progressively first failure censored data Save
- Reliability inference for multicomponent stress–strength model from Kumaraswamy-G family of distributions based on progressively first failure censored samples Save
- Estimation and testing procedures for the reliability characteristics of Kumaraswamy-G distributions based on the progressively first failure censored samples Save
- On the reliability estimation of multicomponent stress–strength model for Burr XII distribution using progressively first-failure censored samples Save