Recent Developments in Distribution Theory: A Brief Survey and Some New Generalized Classes of distributions

The generalization of the classical distributions is an old practice and has been considered as precious as many other practical problems in statistics. These generalizations started with the introduction of the additional location, scale or shape parameters. In the last couple of years, this branch of statistics has received a great deal of attention and quite a few new generalized classes of distributions have been introduced. We present a brief survey of this branch and introduce several new families as well

(R2027) A New Class of Pareto Distribution: Estimation and its Applications

The classical Pareto distribution is a positively skewed and right heavy-tailed lifetime distribution having a lot many applications in various fields of science and social science. In this work, via logarithmic trans-formed method, a new three parameter lifetime distribution, an extension of classical Pareto distribution is generated. The different structural properties of the new distribution are studied. The model parameters are estimated by the method of maximum likelihood and Bayesian procedure. When all the three parameters of the distribution are unknown, the Bayes estimators cannot be obtained in a closed form and hence, the Lindley’s approximation under squared error loss function is used to compute the Bayes estimators. A Monte Carlo simulation study is also conducted to compare the performance of these estimators using mean square error. The application of the new distribution for modelling earthquake insurance and reliability data are illustrated using two real data sets

Odd Generalized N-H Generated Family of Distributions With Application to Exponential Model

A new family of distributions called the odd generalized N-H is introduced and studied. Four new special models are presented. Some mathematical properties of the odd generalized N-H family are studied. Explicit expressions for the moments, probability weighted, quantile function, mean deviation, order statistics and Rényi entropy are investigated. Characterizations based on the truncated moments, hazard function and conditional expectations are presented for the generated family. Parameter estimates of the family are obtained based on maximum likelihood procedure. Two real data sets are employed to show the usefulness of the new family

Alpha Power-Kumaraswamy Distribution with An Application on Survival Times of Cancer Patients

The aim of the study is to obtain the alpha power Kumaraswamy (APK) distribution. Some main statistical properties of the APK distribution are investigated including survival, hazard rate and quantile functions, skewness, kurtosis, order statistics. The hazard rate function of the proposed distribution could be useful to model data sets with bathtub hazard rates. We provide a real data application and show that the APK distribution is better than the other compared distributions fort the right-skewed data sets

A New Variant of Rama Distribution with Simulation Study and Application to Blood Cancer Data

In this paper, we propose a new lifetime distribution with flexibility in modeling than its parent distribution. The new distribution is a variant of the Rama distribution having a positive shift parameter. We call the proposed distribution Shifted Rama (SR) distribution. Mathematical and statistical characteristics such as crude moments, central moment, coefficient of variation, index of dispersion, conditional moment, mean residual life function, mean deviation, Bonferroni and Lorenz curve, and the order statistics are derived. Furthermore, reliability measures like survival function, hazard function have been derived. Estimation techniques namely; the maximum likelihood, least squares, weighted least squares, maximum product spacing, Cramer-von- Mises, Anderson-Darling and the right-tailed Anderson-Darling estimations are used. To demonstrate the applicability of the distribution, a numerical example was the blood cancer data from Ministry Hospital in Saudi Arabia. Based on the results, the proposed distribution performed better than the competing distributions. Simulation of the Estimates of the parameters based on the classical methods considered are obtained, and result showed that the maximum likelihood estimator gave the best classical estimates of the parameters compared to other methods considered

Newdistns: An R Package for New Families of Distributions

The contributed R package Newdistns written by the authors is introduced. This package computes the probability density function, cumulative distribution function, quantile function, random numbers and some measures of inference for nineteen families of distributions. Each family is flexible enough to encompass a large number of structures. The use of the package is illustrated using a real data set. Also robustness of random number generation is checked by simulation

A New Inverse Rayleigh Distribution with Applications of COVID-19 Data: Properties, Estimation Methods and Censored Sample

This paper aims at modelling the COVID-19 spread in the United Kingdom and the United States of America, by specifying an optimal statistical univariate model. A new lifetime distribution with three-parameters is introduced by a combination of inverse Rayleigh distribution and odd Weibull family of distributions to formulate the odd Weibull inverse Rayleigh (OWIR) distribution. Some of the mathematical properties of the OWIR distribution are discussed as linear representation, quantile, moments, function of moment production, hazard rate, stress-strength reliability, and order statistics. Maximum likelihood, maximum product spacing, and Bayesian estimation method are applied to estimate the unknown parameters of OWIR distribution. The parameters of the OWIR distribution are estimated under the progressive type-II censoring scheme with random removal. A numerical result of a Monte Carlo simulation is obtained to assess the use of estimation methods

A New Inverse Rayleigh Distribution with Applications of COVID-19 Data: Properties, Estimation Methods and Censored Sample

This paper aims at modelling the COVID-19 spread in the United Kingdom and the United States of America, by specifying an optimal statistical univariate model. A new lifetime distribution with three-parameters is introduced by a combination of inverse Rayleigh distribution and odd Weibull family of distributions to formulate the odd Weibull inverse Rayleigh (OWIR) distribution. Some of the mathematical properties of the OWIR distribution are discussed as linear representation, quantile, moments, function of moment production, hazard rate, stress-strength reliability, and order statistics. Maximum likelihood, maximum product spacing, and Bayesian estimation method are applied to estimate the unknown parameters of OWIR distribution. The parameters of the OWIR distribution are estimated under the progressive type-II censoring scheme with random removal. A numerical result of a Monte Carlo simulation is obtained to assess the use of estimation methods

Statistical Contributions to Operational Risk Modeling

In this dissertation, we focus on statistical aspects of operational risk modeling. Specifically, we are interested in understanding the effects of model uncertainty on capital reserves due to data truncation and in developing better model selection tools for truncated and shifted parametric distributions. We first investigate the model uncertainty question which has been unanswered for many years because researchers, practitioners, and regulators could not agree on how to treat the data collection threshold in operational risk modeling. There are several approaches under consideration—the empirical approach, the “naive” approach, the shifted approach, and the truncated approach—for fitting the loss severity distribution. Since each approach is based on a different set of assumptions, different probability models emerge. Thus, model uncertainty arises. When possible we investigate such model uncertainty analytically using asymptotic theorems of mathematical statistics and several parametric distributions commonly used for operational risk modeling, otherwise we rely on Monte Carlo simulations. The effect of model uncertainty on risk measurements is quantified by evaluating the probability of each approach producing conservative capital allocations based on the value-at-risk measure. These explorations are further illustrated using a real data set for legal losses in a business unit. After clarifying some prevailing misconceptions around the model uncertainty issue in operational risk modeling, we then employ standard (Akaike Information Criterion, AIC, and Bayesian Information Criterion, BIC) and introduce new model selection tools for truncated and shifted parametric models. We find that the new criteria, which are based on information complexity and asymptotic mean curvature of the model likelihood, are more effective at distinguishing between the competing models than AIC and BIC