Journal article Open Access

# New Log Likelihood Estimation Function

Louangrath, P.

### DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<identifier identifierType="DOI">10.5281/zenodo.1320774</identifier>
<creators>
<creator>
<creatorName>Louangrath, P.</creatorName>
<givenName>P.</givenName>
<familyName>Louangrath</familyName>
<nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-5272-5159</nameIdentifier>
<affiliation>Bangkok University - International College</affiliation>
</creator>
</creators>
<titles>
<title>New Log Likelihood Estimation Function</title>
</titles>
<publisher>Zenodo</publisher>
<publicationYear>2015</publicationYear>
<subjects>
<subject>data types, quantitative data, nominal data, ordinal data</subject>
</subjects>
<dates>
<date dateType="Issued">2015-06-30</date>
</dates>
<language>en</language>
<resourceType resourceTypeGeneral="Text">Journal article</resourceType>
<alternateIdentifiers>
<alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1320774</alternateIdentifier>
</alternateIdentifiers>
<relatedIdentifiers>
<relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1320765</relatedIdentifier>
</relatedIdentifiers>
<version>1A</version>
<rightsList>
<rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
</rightsList>
<descriptions>
<description descriptionType="Abstract">&lt;p&gt;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 &lt;em&gt;X&lt;/em&gt;, 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.&lt;/p&gt;</description>
</descriptions>
</resource>

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