Asymptotic Properties of MLE's for Distributions Generated from an Exponential Distribution by a Generalized Log-Logistic Transformation

Autor:	James Gleaton, Ping Sa, Sami Hamid
Rok vydání:	2022
Zdroj:	Journal of Probability and Statistical Science. 20:204-227
ISSN:	2816-9646 1726-3328
DOI:	10.37119/jpss2022.v20i1.543
Popis:	A generalized log-logistic (GLL) family of lifetime distributions is one in which any pair of distributions are related through a GLL transformation, for some (non-negative) value of the transformation parameter k (the odds function of the second distribution is the k-th power of the odds function of the first distribution). We consider GLL families generated from an exponential distribution. It is shown that the Maximum Likelihood Estimators (MLE’s) for the parameters of the generated, or composite, distribution have the properties of strong consistency and asymptotic normality and efficiency. Data simulation is also found to support the condition of asymptotic efficiency. Keywords Generalized log-logistic exponential distribution; asymptotic properties of MLE’s; simulation
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::18fcc069f2c70ef41b294c94bc1af47b https://doi.org/10.37119/jpss2022.v20i1.543 Zobrazit plný text záznamu