Estimation and classification using samples from two logistic populations with a common scale parameter. (English) Zbl 1541.62067
Saha Ray, Santanu (ed.) et al., Applied analysis, computation and mathematical modelling in engineering. Select proceedings of AACMME 2021, Rourkela, India, February 24–26, 2021. Singapore: Springer. Lect. Notes Electr. Eng. 897, 221-233 (2022).
Summary: This paper deals with the estimation and classification of two logistic populations with a common scale and different location parameters. Utilizing the Metropolis-Hastings method, we compute the Bayes estimators of the associated unknown parameters. For this purpose, we consider gamma priors for the common scale parameter and normal priors for two location parameters. These Bayes estimators are compared with some of the existing estimators in terms of their bias and the mean squared error numerically. Moreover, utilizing these estimators for the associated parameters, we construct some classification rules in order to classify a single observation into one of the two logistic populations under the same model. The performances of each of the classification rules are evaluated through expected probability of misclassification, numerically. Finally, two real-life data sets have been considered in order to show the potential application of the model problem.
For the entire collection see [Zbl 1521.76009].
For the entire collection see [Zbl 1521.76009].
MSC:
| 62F15 | Bayesian inference |
| 62H30 | Classification and discrimination; cluster analysis (statistical aspects) |
Keywords:
Bayes estimator; classification rules; expected probability of misclassification; Metropolis-Hastings procedure; numerical comparisonReferences:
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