Journal article Open Access

New Log Likelihood Estimation Function

Louangrath, P.


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    "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>", 
    "language": "eng", 
    "title": "New Log Likelihood Estimation Function", 
    "license": {
      "id": "CC-BY-4.0"
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    "journal": {
      "volume": "1", 
      "issue": "2", 
      "pages": "36-47", 
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    "keywords": [
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    "publication_date": "2015-06-30", 
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        "affiliation": "Bangkok University - International College", 
        "name": "Louangrath, P."
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