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Your search keyword '"MDZINISO, NONHLE"' showing total 8 results
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8 results on '"MDZINISO, NONHLE"'

1. A Comparison of Student Engagement Across Three Teaching Modalities in an Introductory Statistics Course

Author

Chhetri, Sher B., Toussaint, Mario, Mdziniso, Nonhle C., Hillman, Rebecca A., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Arai, Kohei, editor

Published
2024
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3. Cubic Rank Transmuted Lindley Distribution with Applications.

Author

CHHETRI, SHER, MDZINISO, NONHLE, and BALL, CORY

Subjects
*MAXIMUM likelihood statistics, *GENERATING functions
Abstract

In this work, we propose a three-parameter generalized Lindley distribution using the cubic rank transmutation map approach by Granzotto et al. (2017). We derive expressions for several mathematical properties including moments and moment generating function, mean deviation, probability weighted moments, quantile function, reliability analysis, and order statistics. We conducted a simulation study to assess the performance of the maximum likelihood estimation procedure for estimating model parameters. The flexibility of the proposed model is illustrated by analyzing two real data sets. [ABSTRACT FROM AUTHOR]

Published
2022
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4. Parametric Analysis of Renal Failure Data using the Exponentiated Odd Weibull Distribution

Author

Cooray, Kahadawala, Mdziniso, Nonhle Channon, Cooray, Kahadawala, and Mdziniso, Nonhle Channon

Abstract

In this article, we analyze renal failure data from patients with mesangioproliferative glomerulonephritis (MPGN) which was modeled by [1] non-parametrically using the Kaplan-Meier curve. In their work, they showed that the clinical variables, large increase serum creatinine (LISC) and systolic blood pressure >160 mmHg (SBP>160), and morphological variables, benign nephrosclerosis present (BNP) and interstitial score group 5-6 (IS5-6) were part of the variables which indicated progression to end-stage renal failure (ESRF). Though survival curves associated with these variables may be difficult to model by existing parametric distributions in literature. Therefore, we introduce a four-parameter Odd Weibull extension, the exponentiated Odd Weibull (EOW) distribution which is very versatile in modeling lifetime data that its hazard function exhibits ten different hazard shapes as well as various density shapes. Basic properties of the EOW distribution are presented. In the presence of random censoring, a small simulation study is conducted to assess the coverage probabilities of the estimated parameters of the EOW distribution using the maximum likelihood method. Our results show that the EOW distribution is very convenient and reliable to analyze the MPGN data since it provides an excellent fit for the variables LISC, SBP>160, BNP, and IS5-6. Furthermore, advantages of using the EOW distribution over the Kaplan-Meier curve are discussed. Comparisons of the EOW distribution with other Weibull-related distributions are also presented.

Published
2018

7. Odd Pareto families of distributions for modeling loss payment data.

Author

Mdziniso, Nonhle Channon and Cooray, Kahadawala

Subjects
*FIRE insurance claims, *PARETO distribution, *MAXIMUM likelihood statistics, *DENSITY functionals, *HAZARD function (Statistics)
Abstract

A three-parameter generalization of the Pareto distribution is presented with density function having a flexible upper tail in modeling loss payment data. This generalized Pareto distribution will be referred to as theOdd Paretodistribution since it is derived by considering the distributions of the odds of the Pareto and inverse Pareto distributions. Basic properties of the Odd Pareto distribution (OP) are studied. Model parameters are estimated using both modified and regular maximum likelihood methods. Simulation studies are conducted to compare the OP with the exponentiated Pareto, Burr, and Kumaraswamy distributions using two different test statistics based on the ml method. Furthermore, two examples from the Norwegian fire insurance claims data-set are provided to illustrate the upper tail flexibility of the distribution. Extensions of the Odd Pareto distribution are also considered to improve the fitting of data. [ABSTRACT FROM PUBLISHER]

Published
2018
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