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Comparison of Different Methods for ...
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Tang, Qi.
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Comparison of Different Methods for Estimating Log-normal Means.
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Comparison of Different Methods for Estimating Log-normal Means./
作者:
Tang, Qi.
面頁冊數:
50 p.
附註:
Source: Masters Abstracts International, Volume: 53-01.
Contained By:
Masters Abstracts International53-01(E).
標題:
Statistics. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1527857
ISBN:
9781303980107
Comparison of Different Methods for Estimating Log-normal Means.
Tang, Qi.
Comparison of Different Methods for Estimating Log-normal Means.
- 50 p.
Source: Masters Abstracts International, Volume: 53-01.
Thesis (M.S.)--East Tennessee State University, 2014.
The log-normal distribution is a popular model in many areas, especially in biostatistics and survival analysis where the data tend to be right skewed. In our research, a total of ten different estimators of log-normal means are compared theoretically. Simulations are done using different values of parameters and sample size. As a result of comparison, "a degree of freedom adjusted" maximum likelihood estimator and Bayesian estimator under quadratic loss are the best when using the mean square error (MSE) as a criterion. The ten estimators are applied to a real dataset, an environmental study from Naval Construction Battalion Center (NCBC), Super Fund Site in Rhode Island.
ISBN: 9781303980107Subjects--Topical Terms:
517247
Statistics.
Comparison of Different Methods for Estimating Log-normal Means.
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The log-normal distribution is a popular model in many areas, especially in biostatistics and survival analysis where the data tend to be right skewed. In our research, a total of ten different estimators of log-normal means are compared theoretically. Simulations are done using different values of parameters and sample size. As a result of comparison, "a degree of freedom adjusted" maximum likelihood estimator and Bayesian estimator under quadratic loss are the best when using the mean square error (MSE) as a criterion. The ten estimators are applied to a real dataset, an environmental study from Naval Construction Battalion Center (NCBC), Super Fund Site in Rhode Island.
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