Linear Failure Rate Distribution Research Articles

Reliability Analysis for Unknown Age Class of Lifetime Distribution with Real Applications in Medical Science

Analyzing the reliability of the aging class of life distribution provides important information about how long a product lasts and sustainability measures that are essential for determining the environmental impact and formulating resource-saving plans. The study emphasizes the goodness-of-fit technique of the nonparametric test for the NBRUmgf class because age data are crucial for applications. Evaluations were conducted using the test’s asymptotic properties and Pitman efficiency methodology for some selected asymmetric probability models, and the outcomes were compared with those of alternative methods. We assessed the test’s power against widely used reliability distributions for some well-known alternative asymmetric distributions, including the Weibull, Gamma, and linear failure rate (LFR) distributions, and provided percentiles for both censored and uncensored data. This study shows the efficacy of the test in various sectors using real-world datasets and comprehensive tables of test statistics.

A novel probabilistic model: Simulation and modeling the time duration in musical education and engineering

Statistical analysis of the time-to-event scenarios commonly involves the use of the exponential and Rayleigh distributions. However, the exponential distribution is constrained by its constant failure rate, while the Rayleigh distribution exhibits a growing (or increasing) failure rate. The linear failure rate distribution provides a broader perspective by encompassing both of these distributions, including the possibility of a non-increasing hazard function. In this paper, a new distribution is introduced, which could be considered as one of the major modifications to the linear failure rate distribution, known as the weighted linear failure rate distribution. The model proposed illustrates both monotonic and a known non-monotonic failure rate. This paper extensively explores the characteristics of the proposed distribution. It presents the maximum likelihood estimates for the unknown parameters and includes a simulation study. The analysis focuses on three real data sets obtained from the fields of musical education and engineering. By employing three decisive tools, it is observed that the current distribution provides a superior fit in comparison to several other widely recognized distributions.

Advancing estimation techniques and their applications in engineering and medical data analysis through novel continuous and discrete linear failure rate distribution extension

In this paper, we present a new heavy-tailed distribution called the heavy-tailed linear failure rate (HTLFR) distribution. Various statistical properties of the proposed distribution are derived, including the quantile function, the median, the ordinary moments, the moment generating function, the incomplete moments and the conditional moments. Some actuarial measures such as value at risk, expected shortfall, tail value at risk, tail variance and tail variance premium are calculated. Three different methods of estimation such as the maximum likelihood method, the maximum product spacing method and the Bayesian method as well as some simulation results for the model parameters of the HTLFR distribution under complete samples are examined. The results of the real data set show that the proposed distribution has greater flexibility and has been empirically evaluated under complete data. Discrimination analysis was employed to ensure fairness, equity, and accuracy in decision-making processes for selecting the best model, comparing the proposed distribution with known distributions. A discrete analog of the HTLFR distribution is proposed.

The Role of Risk Factors in System Performance: A Comprehensive Study with Adaptive Progressive Type-II Censoring

The quality performance of many vital systems depends on how long the units are performing; hence, research works started focusing on increasing the reliability of systems while taking into consideration that many factors may cause the failures of operating systems. In this study, the combination of a parametric generalized linear failure rate distribution model and an adaptive progressive Type-II censoring scheme for practical purposes is explored. A comprehensive investigation is performed on the risk factors that cause failure and determines which of the factors has a more harmful effect on the units. A lifetime experiment is performed under the condition of an adaptive progressive Type-II censoring scheme to obtain observations as a result of the competing factors of failures. The obtained observations are assumed to follow a three-parameter generalized linear failure rate distribution and are assumed to be competing to cause failure. Two statistical inference methods are employed for estimating this model’s parameters: the frequentist maximum likelihood method and the Bayesian approach. Our model’s validity is demonstrated through extensive simulations and real data applications in the medical and electrical engineering fields.

Estimation of Rényi entropy of linear failure rate distribution based on Generalized Type- II Hybrid Censored Samples

Estimation of Rényi entropy of linear failure rate distribution based on Generalized Type- II Hybrid Censored Samples

A new extension of linear failure rate distribution with estimation, simulation, and applications

In this article, we provide a new three-parameter model derived from the newly reduced Cauchy power-G family and linked to the linear failure rate model. The truncated Cauchy power linear failure rate (TCPLFR) is the name given to this distribution. The TCPLFR distribution also contains the truncated Cauchy power Rayleigh distribution and the truncated Cauchy power exponential distribution as sub-models. The TCPLFR distribution has rising, falling, and U-shaped hazard rate functions. The distribution characteristics of the TCPLFR are presented. To compute the population parameters’ point and estimated confidence intervals, the maximum likelihood approach is employed. We explore the behavior of the maximum likelihood estimates as well as the estimated confidence intervals for the model parameters using Monte Carlo simulation. To demonstrate the significance and flexibility of the TCPLFR distribution, the Akaike information criterion (D1), Bayesian information criterion (D2), consistent Akaike information criterion (D3), Hannan–Quinn information criterion (D4), and Kolmogorov–Smirnov (D5) statistic with its p-value (D6) were employed. According to a real-world data analysis, the truncated Cauchy power linear failure rate distribution outperforms alternative models with two, three, and four parameters.

Weighted Linear Failure Rate Distribution: Properties, Regression Model, and Applications

In this paper, an extension of the linear failure rate distribution is proposed, called the weighted linear failure rate distribution. This class is a generalization of the two-parameter linear failure rate distribution as well as some other lifetime distributions. The new model has the advantage of being capable of modeling various shapes of aging and failure criteria. Different properties of this new distribution and the inference of the old parameters are discussed, the skewness parameter is examined, and some well-known lifetime distributions are introduced as special sub models. Finally, an application using various types of data is presented to demonstrate the flexibility of this distribution and for illustrative purposes.

Predicting Failure times for some Unobserved Events with Application to Real-Life Data

This study aims to predict failure times for some units in some lifetime experiments. In some practical situations, the experimenter may not be able to register the failure times of all units during the experiment. Recently, this situation can be described by a new type of censored data called multiply-hybrid censored data. In this paper, the linear failure rate distribution is well-fitted to some real-life data and hence some statistical inference approaches are applied to estimate the distribution parameters. A two-sample prediction approach applied to extrapolate a new sample simulates the observed data for predicting the failure times for the unobserved units.

Change Point Detection in Linear Failure Rate Distribution Under Random Censorship

Change Point Detection in Linear Failure Rate Distribution Under Random Censorship

Topp–Leone Modified Weibull Model: Theory and Applications to Medical and Engineering Data

In this article, a four parameter lifetime model called the Topp–Leone modified Weibull distribution is proposed. The suggested distribution can be considered as an alternative to Kumaraswamy Weibull, generalized modified Weibull, extend odd Weibull Lomax, Weibull-Lomax, Marshall-Olkin alpha power extended Weibull and exponentiated generalized alpha power exponential distributions, etc. The suggested model includes the Topp-Leone Weibull, Topp-Leone Linear failure rate, Topp-Leone exponential and Topp-Leone Rayleigh distributions as a special case. Several characteristics of the new suggested model including quantile function, moments, moment generating function, central moments, mean, variance, coefficient of skewness, coefficient of kurtosis, incomplete moments, the mean residual life and the mean inactive time are derived. The probability density function of the Topp–Leone modified Weibull distribution can be right skewed and uni-modal shaped but, the hazard rate function may be decreasing, increasing, J-shaped, U-shaped and bathtub on its parameters. Three different methods of estimation as; maximum likelihood, maximum product spacing and Bayesian methods are used to estimate the model parameters. For illustrative reasons, applications of the Topp–Leone modified Weibull model to four real data sets related to medical and engineering sciences are provided and contrasted with the fit reached by several other well-known distributions.

A New Application of Generalized Linear Failure Rate Distribution in Agriculture Using Ranked Set Sampling

Abstract This article deals with perform inferences about stress–strength model R=P[Y<X] when X and Y are two independent random variables with generalized linear failure rate distribution (GLFRD) with the same and known scale parameters but not identically shaped parameters. In the literature, estimators based on simple random sample (SRS) are widely used in estimating stress–strength model. But in the resent years, ranked set sampling (RSS) has become popular in performing statistical inference. Therefore, the estimator of R based on RSS using maximum likelihood and modified maximum likelihood are obtained. Monte Carlo simulation study is performed to compare the estimators of R are obtained using RSS and SRS. Finally, agriculture application is presented.

Bayesian Estimation in Shared Positive Stable Frailty Models

The concept of frailty provides a suitable way to introduce random effects in the model to account for association and unobserved heterogeneity. In its simplest form, a frailty is an unobserved random factor that modifies multiplicatively the hazard function of an individual or a group or cluster of individuals. In this paper, we study positive stable distribution as frailty distribution and two different baseline distributions namely Pareto and linear failure rate distribution. We estimate parameters of proposed models by introducing Bayesian estimation procedure using Markov Chain Monte Carlo (MCMC) technique. In the present study a simulation is done to compare the true values of parameters with the estimated value. We try to fit the proposed models to a real life bivariate survival data set of McGrilchrist and Aisbett (1991) related to kidney infection. Also, we present a comparison study for the same data by using model selection criterion, and suggest a better model.

On The Mean First Exit Time For A Compound Poisson Process

This paper introduces the beta linear failure rate geometric (BLFRG) distribution, which contains a number of distributions including the exponentiated linear failure rate geometric, linear failure rate geometric, linear failure rate, exponential geometric, Rayleigh geometric, Rayleigh and exponential distributions as special cases. The model further generalizes the linear failure rate distribution. A comprehensive investigation of the model properties including moments, conditional moments, deviations, Lorenz and Bonferroni curves and entropy are presented. Estimates of model parameters are given. Real data examples are presented to illustrate the usefulness and applicability of the distribution.

Likelihood Ratio Test and Non-parametric Test for Load Sharing

In present article, we propose a likelihood ratio test and a non-parametric test for testing the load sharing effect observed in the two component parallel load sharing system. We have modeled the load sharing phenomenon observed in such system by the exponentiated conditional distribution function based load sharing model proposed by Sutar and Naik-Nimbalkar (2016). We have taken component baseline distribution as Weibull distribution and linear failure rate distribution. The simulation study to see the performance of proposed test procedures is reported. We analyze two data sets for illustrative purpose.

A New Exponentiated Generalized Linear Exponential Distribution: Properties and Application

A new exponentiated generalized linear exponential distribution (NEGLED) is introduced, which poses increasing, decreasing, bathtub-shaped, and constant hazard rate. Its various mathematical properties such as moments, quantiles, order statistics, hazard rate function (HRF), stress–strength parameter, etc. are derived. Five distributions, exponential distribution (ED), generalized linear exponential distribution (GLED), Rayleigh distribution (RD), Weibull distribution (WD), and generalized linear failure rate distribution (GLFRD) were used for comparison with NEGLED model using a dataset of blood cancer patients. Estimation of the parameters using the maximum likelihood estimation (MLE) method was obtained and to evaluate their performance a simulation study has been carried out. Finally, a dataset of 40 Leukemia patients was analysed for illustrative purpose proving that the NEGLED outperforms compared distributions.

Type II Half Logistic Linear Failure Rate Distribution with Application

In recent years, several of new improved probability distributions have been discovered from the current distributions to facilitate their applications in various areas. A new three-parameter model extended from the linear failure rate model, the so called the type II half logistic linear failure rate distribution. Some mathematical properties of the new distribution are proposed. Explicit expressions for the moments, probability weighted moments and order statistics are calculated. Maximum likelihood estimation method is assessed to estimate the model parameters are presented. The superiority of the new distribution is illustrated with an application to one real data set.

On the Bayesian Analysis of Constant-Stress Life Test Model Under Type-Ii Censoring

In this article, a constant-stress partially accelerated life test model of high reliability products and materials with type-II censored data from the linear failure rate distribution is considered. Two different methods of estimation likelihood and Bayesian are used to estimate the unknown parameters of the model. The maximum likelihood estimates of the model parameters are obtained using the Newton–Raphson technique. The posterior means and posterior variances are derived under the squared error loss function using Lindley’s approximation procedure. The advantages of this approximation are discussed. Monte Carlo simulations are made for comparing and evaluating the performance of the proposed methods of estimation.

Improving the bridge structure by using linear failure rate distribution

In this paper, a system of five components is studied; one of these components is a bridge network component. Each of these components has a non-constant failure rate. The system components have linear failure rate lifetime distribution. The given system is improved by using three methods: reduction, warm standby with perfect switch and warm standby with imperfect switch. The reliability equivalence factors of the bridge structure system are obtained. The γ-fractiles are obtained to compare the original system with these improved systems. Finally, we present numerical results to show the difference between these methods.

Statistical Inference of Constant-Stress Partially Accelerated Life Test Model Using Failure-Censored Data from the Linear Failure Rate Distribution

In this article, the case of failure-censored constant-stress partially accelerated life test for highly reliable materials or devices assuming the linear failure rate distribution is considered. The maximum likelihood estimates of the distribution parameters and the acceleration factor are obtained. In order to evaluate the performance, the mean squared errors are calculated using different sizes of samples. In addition, the approximate confidence intervals of model parameters are constructed. For illustrative purposes, Monte Carlo simulation studies are offered.

On Estimating a Constant Stress Life Test Model Using Time-Censored Data from the Linear Failure Rate Distribution

In this paper, a constant stress partially accelerated life test model is considered and investigated using type-I censored data from the linear failure rate distribution. The maximum likelihood estimates (point and interval) of the distribution parameters and the acceleration factor are obtained. For accuracy reasons, the mean squared errors are calculated using different sizes of samples. For illustration, Monte Carlo simulation studies are presented.