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• Published Subscription 18 Mar 2019

  Evaluation of Penalized Estimation Methods for Big Data Analysis

  By Md. Siddikur Rahman, Md. Mujibur Rahman, M. A. Matin

  Abstract: In this paper we described the sparse Laplacian shrinkage (SLS) method which is a penalized method for variable selection and estimation in big data analysis that uses a combination of the minimax concave penalty (MCP) and Laplacian quadratic as the penalty. The SLS uses the MCP to promote sparsity and Laplacian quadratic penalty to encourage smoothness among coefficients associated with the correlated predictors. An important advantage of the MCP over …

  Published in Research & Reviews: A Journal of Bioinformatics · Vol. 2, Issue 2, 2015 · pp. 49–55 Read article →