A General Class of Trimodal Distributions: Properties and Inference
Roberto Vila, Victor Serra, Mehmet N. \c{C}ankaya, Felipe Quintino

TL;DR
This paper introduces a new class of trimodal probability distributions achieved through transformations, focusing on the Gaussian case, and demonstrates their effectiveness in modeling real trimodal data sets using computational methods.
Contribution
It proposes a novel class of trimodal distributions based on transformations, with detailed analysis of the trimodal Gaussian model and application to real data.
Findings
The new distributions can effectively model trimodal data.
Analytical properties of the trimodal Gaussian are derived.
Real data sets demonstrate the model's practical utility.
Abstract
The modality is important topic for modelling. Using parametric models is an efficient way when real data set shows trimodality. In this paper we propose a new class of trimodal probability distributions, that is, probability distributions that have up to three modes. Trimodality itself is achieved by applying a proper transformation to density function of certain continuous probability distributions. At first, we obtain preliminary results for an arbitratry density function and, next, we focus on the Gaussian case, studying trimodal Gaussian model more deeply. The Gaussian distribution is applied to produce the trimodal form of Gaussian known as normal distribution. The tractability of analytical expression of normal distribution, and properties of the trimodal normal distribution are important reasons why we choose normal distribution. Furthermore, the existing distributions…
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Taxonomy
TopicsBayesian Methods and Mixture Models · Statistical Distribution Estimation and Applications · Advanced Statistical Methods and Models
