A New Logarithmic Tangent-U Family of Distributions with Reliability Analysis in Engineering Data

This article introduces a new family of distributions through the transformation involving tangent functions, referred to as the New Logarithmic Tangent-U family. The newly introduced family may be written as the NLT-U family of distribution. An overview of fundamental characteristics inherent to the proposed family of distributions is provided. We derived a special sub-model of the NLT-U distributions family using the Weibull distribution as a baseline reference. This special model is termed the New Logarithmic Tangent-Weibull (NLT-Wei) distribution. The hazard function of the special model is very flexible; it captures increasing, decreasing, unimodal, increasing-decreasing-decreasing, and bathtub shapes. The Maximum Likelihood estimation scheme is employed to estimate the model parameters of the proposed family. To assess the stability of the parameter estimation procedure, we perform a Monte Carlo simulation analysis. The results (numerically as well as graphically) of these simulations demonstration that biases and mean square errors decrease as sample sizes grow, even with smaller samples. We thoroughly examine the significance of the NLT-Wei distribution and illustrate its application using three real-world data sets from the engineering field. The suitability and flexibility of the NLT-Wei distribution are compared against several other distributions, including the Exponentiated Weibull, Kumaraswamy Weibull, New Generalized Logarithmic Weibull, Weibull, and Flexible Weibull distributions. The paper ends with a concluding discussion.

Authors

Institutions

Publication Details

Journal
Computational Journal of Mathematical and statistical Sciences
Published
2025-02-16
DOI
https://doi.org/10.21608/cjmss.2025.342223.1090
Citations
20
Primary Topic
Probabilistic and Robust Engineering Design
Type
article
Field-Weighted Citation Impact
27.97
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A New Logarithmic Tangent-U Family of Distributions with Reliability Analysis in Engineering Data

Meshayil M. Alsolmi
20 citations
Computational Journal of Mathematical and statistical Sciences
Probabilistic and Robust Engineering Design
27.97
article

A New Logarithmic Tangent-U Family of Distributions with Reliability Analysis in Engineering Data

Meshayil M. Alsolmi
article en
20 citations

Abstract

This article introduces a new family of distributions through the transformation involving tangent functions, referred to as the New Logarithmic Tangent-U family. The newly introduced family may be written as the NLT-U family of distribution. An overview of fundamental characteristics inherent to the proposed family of distributions is provided. We derived a special sub-model of the NLT-U distributions family using the Weibull distribution as a baseline reference. This special model is termed the New Logarithmic Tangent-Weibull (NLT-Wei) distribution. The hazard function of the special model is very flexible; it captures increasing, decreasing, unimodal, increasing-decreasing-decreasing, and bathtub shapes. The Maximum Likelihood estimation scheme is employed to estimate the model parameters of the proposed family. To assess the stability of the parameter estimation procedure, we perform a Monte Carlo simulation analysis. The results (numerically as well as graphically) of these simulations demonstration that biases and mean square errors decrease as sample sizes grow, even with smaller samples. We thoroughly examine the significance of the NLT-Wei distribution and illustrate its application using three real-world data sets from the engineering field. The suitability and flexibility of the NLT-Wei distribution are compared against several other distributions, including the Exponentiated Weibull, Kumaraswamy Weibull, New Generalized Logarithmic Weibull, Weibull, and Flexible Weibull distributions. The paper ends with a concluding discussion.

Computational Journal of Mathematical and statistical SciencesVol. 4(1)
University of Jeddah (SA)
Openalex Percentile: Top 0%
Probabilistic and Robust Engineering Design
27.97
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.