H
Hukum Chandra
Researcher at Indian Agricultural Statistics Research Institute
Publications - 83
Citations - 1052
Hukum Chandra is an academic researcher from Indian Agricultural Statistics Research Institute. The author has contributed to research in topics: Small area estimation & Estimator. The author has an hindex of 17, co-authored 75 publications receiving 825 citations. Previous affiliations of Hukum Chandra include University of Southampton & University of Wollongong.
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Outlier Robust Small Area Estimation
TL;DR: Simulations based on realistic outlier-contaminated data show that the bias correction proposed often leads to more efficient estimators, and the mean-squared error estimation methods proposed appear to perform well with a variety of outlier robust small area estimators.
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Technological impact on energy consumption in rainfed soybean cultivation in Madhya Pradesh
TL;DR: In this paper, the authors studied the energy consumption patterns of these farms and a linear programming technique was applied to determine optimal energy resource allocation for maximum yield obtainable under business-as-usual and improved cultivation practices.
Journal Article
Outlier robust small area estimation
TL;DR: In this article, the idea of an outlier robust bias correction for these estimators was developed and also proposed two different analytical mean squared error estimators for the ensuring bias corrected estimators.
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A Random Effect Block Bootstrap for Clustered Data
Ray Chambers,Hukum Chandra +1 more
TL;DR: This article describes a random effect block bootstrap approach for clustered data that is simple to implement, free of both the distribution and the dependence assumptions of the parametric bootstrap, and is consistent when the mixed model assumptions are valid.
On Bias-Robust Mean Squared Error Estimation for Pseudo-Linear Small Area Estimators
TL;DR: In this article, bias-robust mean squared error estimation for estimators of finite population domain means that can be expressed in pseudo-linear form is discussed, i.e. as weighted sums of sample values.