Journal article Open Access

New Log Likelihood Estimation Function

Louangrath, P.


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  <dc:creator>Louangrath, P.</dc:creator>
  <dc:date>2015-06-30</dc:date>
  <dc:description>This paper provides a New Log-Likelihood Estimator (NLLE) function as a tool for value approximation. We improved the accuracy of the log MLE in two steps (i) determine the log likelihood of a random variable X, and (ii) adjust the estimate by a factor of . In-Sample testing was accomplished by using daily SET100 indices over a period of 60 days. Out-of-sample data were used for confirmatory verification; out-of-sample data came from 5 major stock markets: NASDAQ, DOW, SP500, DAX, and CAC40. Relevant tests used to compare the results of the proposed NLLE include Cramer-Rao Lower Bound (CRLB), Likelihood Ratio Test, Wald statistic, and Lagrange Multiplier (Score Statistic). It was found that NLLE is more efficient than the conventional MLE. It gives practitioners a better tool for value estimation in many fields of natural and social sciences.</dc:description>
  <dc:identifier>https://zenodo.org/record/1320774</dc:identifier>
  <dc:identifier>10.5281/zenodo.1320774</dc:identifier>
  <dc:identifier>oai:zenodo.org:1320774</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>doi:10.5281/zenodo.1320765</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:source>Inter. J. Res. Methodol. Soc. Sci 1(2) 36-47</dc:source>
  <dc:subject>data types, quantitative data, nominal data, ordinal data</dc:subject>
  <dc:title>New Log Likelihood Estimation Function</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
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