Journal article Open Access

New Log Likelihood Estimation Function

Louangrath, P.


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{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<p>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 <em>X</em>, 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.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Bangkok University - International College", 
      "@id": "https://orcid.org/0000-0001-5272-5159", 
      "@type": "Person", 
      "name": "Louangrath, P."
    }
  ], 
  "headline": "New Log Likelihood Estimation Function", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2015-06-30", 
  "url": "https://zenodo.org/record/1320774", 
  "version": "1A", 
  "keywords": [
    "data types, quantitative data, nominal data, ordinal data"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.1320774", 
  "@id": "https://doi.org/10.5281/zenodo.1320774", 
  "@type": "ScholarlyArticle", 
  "name": "New Log Likelihood Estimation Function"
}
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