Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Robust parameters estimation of sparse data is generally applied to the test cases of time-consuming or high cost data collection. This study concerns with the problem in small sample size which is often encountered in the client data processing for speaker verification.  found that there always exists a coverage mismatch problem beten the samples and its population in terms of probability density function (pdf) when the sample size is less than 20.  call this special problem the distribution mismatch (DM) problem. The paper proposes to solve the DM problem through addressing a new coverage-based estimator.
['Wen-Hui Lo', 'Sin-Horng Chen']
I am writing a research paper on a idea Robust estimation for sparse data
[{"from": "human", "value": "I am writing a research paper on a idea Robust estimation for sparse data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Robust parameters estimation of sparse data is generally applied to the test cases of time-consuming or high cost data collection. This study concerns with the problem in small sample size which is often encountered in the client data processing for speaker verification.  found that there always exists a coverage mismatch problem beten the samples and its population in terms of probability density function (pdf) when the sample size is less than 20.  call this special problem the distribution mismatch (DM) problem. The paper proposes to solve the DM problem through addressing a new coverage-based estimator."}]