Statistical Inference under Censored Data for the New Exponential-X Fréchet Distribution: Simulation and Application to Leukemia Data

Alzeley, Omar; Almetwally, Ehab M.; Gemeay, Ahmed M.; Alshanbari, Huda M.; Hussam, Eslam; Abu‐Moussa, Mahmoud H.

doi:10.1155/2021/2167670

Cited by 16 publications

(4 citation statements)

References 46 publications

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“…globalclimatemonitor.org/) using latitude (10.9922) e performance of the HMF distribution is compared with the classical Fréchet distribution and eight (8) modifications of the classical Fréchet distribution. ese eight (8) distributions include the Burr X Fréchet (BRXFR) [2], the odd Lomax Fréchet (OLXF) [3], the Poisson-Fréchet (POF) [4], the new exponential-X Fréchet (NEXF) [5], the Weibull Fréchet (WFR) [6], the modified Fréchet-Rayleigh distribution (MFRD) [9], the Marshall-Olkin Fréchet distribution (MOF) [12], and the modified Fréchet (MF) [16].…”

Section: Applicationsmentioning

confidence: 99%

“…Several extensions of the Fréchet distribution have been proposed in the literature aimed at making it more exible in modelling both monotonic and non-monotonic datasets. Some extensions of the Fréchet distribution include the Burr X Fréchet (BRXFR) [2], the odd Lomax Fréchet (OLXF) [3], the Poisson-Fréchet (POF) [4], the new exponential-X Fréchet (NEXF) [5], the Weibull Fréchet (WFR) [6], extended Poisson Fréchet distribution (P-BX-Fr) [7], the Burr XII Fréchet (BrXIIFr) [8], the modi ed Fréchet-Rayleigh distribution (MFRD) [9], truncated Weibull Fréchet distribution (TWFr) [10,11], the Marshall-Olkin Fréchet distribution (MOF) [12], the gamma extended Fréchet distribution (GEF) [13], the Lehmann type II Fréchet Poisson distribution (LFP) [14], the exponential transmuted Fréchet distribution (ETF) [15], the modi ed Fréchet (MF) [16], the generalised truncated Fréchet generated family distributions (TGFr-G) [17], and the double truncated transmuted Fréchet distribution (DTTF) [18]. Not long ago, [19] proposed a new family of mixture distribution using the weighted harmonic means of two survival functions and called it the harmonic mixture-G (HMG) family.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Harmonic Mixture Fréchet Distribution: Properties and Applications to Lifetime Data

Ocloo

Brew

Nasiru³

et al. 2022

International Journal of Mathematics and Mathematical Sciences

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In this study, we propose a four-parameter probability distribution called the harmonic mixture Fréchet. Some useful expansions and statistical properties such as moments, incomplete moments, quantile functions, entropy, mean deviation, median deviation, mean residual life, moment-generating function, and stress-strength reliability are presented. Estimators for the parameters of the harmonic mixture Fréchet distribution are derived using the estimation techniques such as the maximum-likelihood estimation, the ordinary least-squares estimation, the weighted least-squares estimation, the Cramér–von Mises estimation, and the Anderson–Darling estimation. A simulation study was conducted to assess the biases and mean square errors of the estimators. The new distribution was applied to three-lifetime datasets and compared with the classical Fréchet distribution and eight (8) other extensions of the Fréchet distribution.

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Section: Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Harmonic Mixture Fréchet Distribution: Properties and Applications to Lifetime Data

Ocloo

Brew

Nasiru³

et al. 2022

International Journal of Mathematics and Mathematical Sciences

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“…Kumaraswamy Inverted Topp-Leone (KITL) (Hassan et al [10]), odd Weibull inverse Topp-Leone (OWITL) (Almetwally [27]), new exponential-X Fre ´chet (NEF) (Alzeley et al [28]), Modified Kies INH (MKINH), and Weibull Lomax (WL) (Tahir et al [29]). As a result, we conclude that OLINH best suits and fit the COVID-19 vaccination rate data set.…”

Section: Plos Onementioning

confidence: 99%

Modeling of COVID-19 vaccination rate using odd Lomax inverted Nadarajah-Haghighi distribution

Almongy

Almetwally²,

Ahmad³

et al. 2022

PLoS ONE

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Since the spread of COVID-19 pandemic in early 2020, modeling the related factors became mandatory, requiring new families of statistical distributions to be formulated. In the present paper we are interested in modeling the vaccination rate in some African countries. The recorded data in these countries show less vaccination rate, which will affect the spread of new active cases and will increase the mortality rate. A new extension of the inverted Nadarajah-Haghighi distribution is considered, which has four parameters and is obtained by combining the inverted Nadarajah-Haghighi distribution and the odd Lomax-G family. The proposed distribution is called the odd Lomax inverted Nadarajah-Haghighi (OLINH) distribution. This distribution owns many virtuous characteristics and attractive statistical properties, such as, the simple linear representation of density function, the flexibility of the hazard rate curve and the odd ratio of failure, in addition to other properties related to quantile, the rth-moment, moment generating function, Rényi entropy, and the function of ordered statistics. In this paper we address the problem of parameter estimation from frequentest and Bayesian approach, accordingly a comparison between the performance of the two estimation methods is implemented using simulation analysis and some numerical techniques. Finally different goodness of fit measures are used for modeling the COVID-19 vaccination rate, which proves the suitability of the OLINH distribution over other competitive distributions.

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“…In recent times, the prediction of the current pandemic of COVID-19 outbreak is a test for data experts as inadequate information is available on the initial growing curve, and the epidemiological properties of the virus to be fully elucidated. There has been a renewed interest in using time series models to predict the epidemics, namely, SARS, Ebola, influenza, and dengue (3)(4)(5)(6)(7)(8)(9)(10). These studies have shown an increasing curiosity in applying time series models as valuable tools in estimating and predicting epidemics.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Prediction of COVID-19 Using Novel Hybrid Ensemble Empirical Mode Decomposition and Error Trend Seasonal Model

Khan

Ali

Iqbal

et al. 2022

Front. Public Health

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In this article, a new hybrid time series model is proposed to predict COVID-19 daily confirmed cases and deaths. Due to the variations and complexity in the data, it is very difficult to predict its future trajectory using linear time series or mathematical models. In this research article, a novel hybrid ensemble empirical mode decomposition and error trend seasonal (EEMD-ETS) model has been developed to forecast the COVID-19 pandemic. The proposed hybrid model decomposes the complex, nonlinear, and nonstationary data into different intrinsic mode functions (IMFs) from low to high frequencies, and a single monotone residue by applying EEMD. The stationarity of each IMF component is checked with the help of the augmented Dicky–Fuller (ADF) test and is then used to build up the EEMD-ETS model, and finally, future predictions have been obtained from the proposed hybrid model. For illustration purposes and to check the performance of the proposed model, four datasets of daily confirmed cases and deaths from COVID-19 in Italy, Germany, the United Kingdom (UK), and France have been used. Similarly, four different statistical metrics, i.e., root mean square error (RMSE), symmetric mean absolute parentage error (sMAPE), mean absolute error (MAE), and mean absolute percentage error (MAPE) have been used for a comparison of different time series models. It is evident from the results that the proposed hybrid EEMD-ETS model outperforms the other time series and machine learning models. Hence, it is worthy to be used as an effective model for the prediction of COVID-19.

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Statistical Inference under Censored Data for the New Exponential-X Fréchet Distribution: Simulation and Application to Leukemia Data

Cited by 16 publications

References 46 publications

Harmonic Mixture Fréchet Distribution: Properties and Applications to Lifetime Data

Harmonic Mixture Fréchet Distribution: Properties and Applications to Lifetime Data

Modeling of COVID-19 vaccination rate using odd Lomax inverted Nadarajah-Haghighi distribution

Short-Term Prediction of COVID-19 Using Novel Hybrid Ensemble Empirical Mode Decomposition and Error Trend Seasonal Model

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