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This research aims to explore how choosing the wrong statistical model affects mean estimation.

Mis-specification effects on mean and model choice: A comparison of log-normal and log-logistic distributions

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This research aims to explore how choosing the wrong statistical model affects mean estimation.
Derived maximum likelihood estimator (MLE) and quasi-MLE (QMLE) for both distributions under mis-specification.
Constructed bootstrap confidence intervals to evaluate estimation uncertainty.
Used simulation and bootstrap experiments to validate theoretical findings.
Showed that mis-specification significantly distorts mean estimation with variance in the ratio of biases.
Quantified the difference in mean squared errors between the log-normal and log-logistic models.
Demonstrated practical implications using a real-world dataset aligned with theoretical outcomes.

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Randomized trial investigates mis-specification effects on model choice in skewed data, suggesting key estimation strategies.

Yadav et al. (2026) studied this question.

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