Journal article Open Access

# New Log Likelihood Estimation Function

Louangrath, P.

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"inLanguage": {
"alternateName": "eng",
"@type": "Language",
"name": "English"
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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>",
"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",
"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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