Likelihood-based inference for the power half-normal distribution

Abstract

In this paper we consider an extension of the half-normal distribution based on the distribution of the maximum of a random sample. It is shown that this distribution belongs to the family of beta generalized half-normal distributions. Properties of its density are investigated, maximum likelihood estimation is discussed and the Fisher information matrix is derived. A real data illustration is presented, and comparisons with alternative extensions of the half-normal distribution reveal good performance of the proposed model.

Generalized Truncation Positive Normal Distribution

“…In recent years this distribution has been used to model positive data, and it is becoming an important model in reliability theory despite the fact that it accommodates only decreasing hazard rates. Some of the generalizations of this distribution can be found in Cooray and Ananda [9], Olmos et al [10], Cordeiro et al [11], Gómez and Bolfarine [12], among others.…”

Slash-Weighted Lindley Distribution: Properties, Inference, and Applications

“…The second dataset considers the survival time of 72 guinea pigs injected with different doses of tubercle bacilli. This dataset was analyzed in Kundu [38] and Gómez and Bolfarine [39], among others. Table 5 gives the statistical summaries of this dataset.…”

Truncated Power-Normal Distribution with Application to Non-Negative Measurements

“…In this section, we present two applications to illustrate the performance of the TPN model compared with other usual distributions in the literature, such as the Weibull, gamma, GHN, Birnbaum–Saunders (BS, [ 24 , 25 ]), -Birnbaum–Saunders ( -BS, [ 26 ]), epsilon half-normal (EHN, [ 7 ]), power half-normal (PHN, [ 27 ]) and truncated positive normal (TN, [ 28 ]) models. Model comparison is implemented by using the AIC ([ 29 ]).…”