Hasinur Rahaman Khan

Variable selection for survival data with a class of adaptive elastic net techniques

The accelerated failure time (AFT) models have proved useful in many contexts, though heavy censoring (as for example in cancer survival) and high dimensionality (as for example in microarray data) cause difficulties for model fitting and model selection. We propose new approaches to variable selection for censored data, based on AFT models optimized using regularized weighted least squares. The regularized technique uses a mixture of

Substantial gender gap reduction in Bangladesh explained by the proximity measure of literacy and life expectancy

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Incidence of and Risk Factors for Small Size Babies in Bangladesh

ℓ1 and

Optimal portfolio determination for developing country’s vulnerable stock market data

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730 Factors causing deaths due to injury among children in Bangladesh

ℓ2 norm penalties under two proposed elastic net type approaches. One is the adaptive elastic net and the other is weighted elastic net. The approaches extend the original approaches proposed by Ghosh (Adaptive elastic net: an improvement of elastic net to achieve oracle properties, Technical Reports 2007) and Hong and Zhang (Math Model Nat Phenom 5(3):115–133 2010), respectively. We also extend the two proposed approaches by adding censoring observations as constraints into their model optimization frameworks. The approaches are evaluated on microarray and by simulation. We compare the performance of these approaches with six other variable selection techniques-three are generally used for censored data and the other three are correlation-based greedy methods used for high-dimensional data.

Literate Life Expectancy in Bangladesh: A New Approach of Social Indicator

ABSTRACT When observations are subject to right censoring, weighted least squares with appropriate weights (to adjust for censoring) is sometimes used for parameter estimation. With Stutes weighted least squares method, when the largest observation is censored (), it is natural to apply the redistribution to the right algorithm of Efron [The two sample problem with censored data. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 4. New York: Prentice Hall; 1967. p. 831–853]. However, Efrons redistribution algorithm can lead to bias and inefficiency in estimation. This study explains the issues clearly and proposes some alternative ways of treating . The first four proposed approaches are based on the well known Buckley–James [Linear regression with censored data. Biometrika 1979;66:429–436] method of imputation with the Efrons tail correction and the last approach is indirectly based on a general mean imputation technique in literature. All the new schemes use penalized weighted least squares optimized by quadratic programming implemented with the accelerated failure time models. Furthermore, two novel additional imputation approaches are proposed to impute the tail tied censored observations that are often found in survival analysis with heavy censoring. Several simulation studies and real data analysis demonstrate that the proposed approaches generally outperform Efrons redistribution approach and lead to considerably smaller mean squared error and bias estimates.