TY  - JOUR
T1  - Bias corrected maximum likelihood estimators for the parameters of the generalized normal distribution
AU  - Gül, Hasan Hüseyin
AU  - Doğru, Fatma Zehra
PY  - 2024
DA  - December
Y2  - 2024
DO  - 10.31801/cfsuasmas.1439744
JF  - Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics
JO  - Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat.
PB  - Ankara University
WT  - DergiPark
SN  - 1303-5991
SP  - 1050
EP  - 1071
VL  - 73
IS  - 4
LA  - en
AB  - The generalized normal (GN) distribution was defined as a generalization of the normal, Laplace, and uniform distributions, with extensive application areas modeling different data settings. At the same time, its maximum likelihood estimators (MLEs) are biased in finite samples. Since such biases may affect the accuracy of estimates, we consider constructing unbiased estimators for unknown parameters of GN distribution. This article adopts the bias-corrected approach, following the analytical methodology suggested by Cox and Snell [1]. Additionally, we explore both regular biases and parametric Bootstrap bias correction techniques. A comprehensive Monte Carlo simulation is conducted to compare the performances of these estimators in estimating GN parameters. Finally, a real data example is presented to illustrate the application of methods.
KW  - Bootstrap bias-correction
KW  - Cox-Snell bias-correction
KW  - generalized normal distribution
KW  - maximum likelihood estimators
KW  - Monte-Carlo simulation
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UR  - https://doi.org/10.31801/cfsuasmas.1439744
L1  - https://dergipark.org.tr/en/download/article-file/3740127
ER  -