Density Estimation using Entropy Maximization for Semi-continuous Data

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2011.08475.pdf (396.86 KB)

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https://arxiv.org/abs/2011.08475

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http://hdl.handle.net/11603/20262

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UMBC Mathematics and Statistics Department
UMBC Faculty Collection
UMBC Joint Center for Earth Systems Technology (JCET)
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2020-11-17

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journal articles preprints
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Citation of Original Publication

Sai K. Popuri, Nagaraj K. Neerchal, Amita Mehta and Ahmad Mousavi, Density Estimation using Entropy Maximization for Semi-continuous Data, https://arxiv.org/abs/2011.08475

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UMBC High Performance Computing Facility (HPCF)

Abstract

Semi-continuous data comes from a distribution that is a mixture of the point mass at zero and a continuous distribution with support on the positive real line. A clear example is the daily rainfall data. In this paper, we present a novel algorithm to estimate the density function for semi-continuous data using the principle of maximum entropy. Unlike existing methods in the literature, our algorithm needs only the sample values of the constraint functions in the entropy maximization problem and does not need the entire sample. Using simulations, we show that the estimate of the entropy produced by our algorithm has significantly less bias compared to existing methods. An application to the daily rainfall data is provided.