Estimation of common location parameter of several heterogeneous exponential populations based on generalized order statistics

Azhad, Qazi J.; Arshad, Mohd Anuar; Misra, Amit

doi:10.1080/02664763.2020.1777395

Cited by 8 publications

(3 citation statements)

References 30 publications

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“…For detailed discussion about the order and record statistics and their application, the readers refer Chandler (1952), Arnold et al (1992), Arnold et al (1998), Ahsanullah (1995), David and Nagaraja (2003), Khan and Arshad (2016), Arshad and Baklizi (2019), , , Arshad et al (2021), Azhad et al (2021). Let X 1 , X 2 , .…”

Section: Order Statistics and Record Statisticsmentioning

confidence: 99%

A New Nadarajah-Haghighi Distribution with Applications

Kumar,

Chakraborty,

Arshad

et al. 2024

Preprint

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We propose a new Nadarajah-Haghighi (NH) distribution by applying the record transmutation function to the NH distribution for enhanced flexibility in data modeling. We discuss the shape of the probability density function and hazard function. Furthermore, we obtain quantile function, moments and moment generating function of the distribution. Moreover, we study various estimators for the parameters, such as maximum likelihood estimators, ordinary least squares and weighted least squares estimators, Anderson-Darling estimators, and Cram´er-von Mises estimators. The biases and mean squared errors of these estimators are thoroughly examined through Monte Carlo simulation studies. To demonstrate the practical application of the proposed distribution, two real data analysis are considered. The results highlight the effectiveness of the proposed distribution in modeling real-life data, with the model exhibiting superior fitting performance compared to other competing models.

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Section: Order Statistics and Record Statisticsmentioning

confidence: 99%

A New Nadarajah-Haghighi Distribution with Applications

Kumar,

Chakraborty,

Arshad

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…For more details, the readers are advised to see, Chandler (1952), Arnold et al (1992Arnold et al ( , 1998, Ahsanullah (1995), David and Nagaraja. (2003), Khan and Arshad (2016), Arshad and Baklizi (2019), Jamal (2019a, 2019b), Arshad et al (2021), Azhad et al (2021). Let X 1 , X 2 , .…”

Section: Order and Record Statisticsmentioning

confidence: 99%

Record-Based Transmuted Generalized Linear Exponential Distribution with Increasing, Decreasing and Bathtub Shaped Failure Rates

Arshad¹,

Khetan²,

Kumar³

et al. 2021

Preprint

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The linear exponential distribution is a generalization of the exponential and Rayleigh distributions. This distribution is one of the best models to fit data with increasing failure rate (IFR). But it does not provide a reasonable fit for modeling data with decreasing failure rate (DFR) and bathtub shaped failure rate (BTFR). To overcome this drawback, we propose a new record-based transmuted generalized linear exponential (RTGLE) distribution by using the technique of Balakrishnan and He (2021). The family of RTGLE distributions is more flexible to fit the data sets with IFR, DFR, and BTFR, and also generalizes several well-known models as well as some new record-based transmuted models. This paper aims to study the statistical properties of RTGLE distribution, like, the shape of the probability density function and hazard function, quantile function and its applications, moments and its generating function, order and record statistics, Rényi entropy. The maximum likelihood estimators, least squares and weighted least squares estimators, Anderson-Darling estimators, Cramér-von Mises estimators of the unknown parameters are constructed and their biases and mean squared errors are reported via Monte Carlo simulation study. Finally, the real data set based on failure time illustrates the goodness of fit and applicability of the proposed distribution; hence, suitable recommendations are forwarded.

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“…For more details, see Arnold et al [6]. For some recent works on statistical inference using record data, see for example Asgharzadeh et al [8,9], Aldallal [4], Chaturvedi and Malhotra [13], Bagheri et al [10], Basiri et al [11], Sing et al [25], Qazi et al [19], Raqab et al [20] and Aly et al [5].…”

Section: Introductionmentioning

confidence: 99%

Inference Based on New Pareto-Type Records With Applications to Precipitation and Covid-19 Data

Nik¹,

Asgharzadeh²,

Baklizi³

2023

Stat., optim. inf. comput.

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We consider estimation and prediction of future records based on observed records from the new Pareto type distribution proposed recently by Bourguignon et al. (2016), “M. Bourguignon, H. Saulo, R. N. Fernandez, A new Pareto-type distribution with applications in reliability and income data, Physica A, 457 (2016), 166-175.”. We derived several point predictors for a future record on the basis of the first n records. Two real data sets on precipitation and Covid 19 are analysed and a Monte Carlo simulation study has been performed to evaluate the statistical performance of point predictors presented in this paper.

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Estimation of common location parameter of several heterogeneous exponential populations based on generalized order statistics

Cited by 8 publications

References 30 publications

A New Nadarajah-Haghighi Distribution with Applications

A New Nadarajah-Haghighi Distribution with Applications

Record-Based Transmuted Generalized Linear Exponential Distribution with Increasing, Decreasing and Bathtub Shaped Failure Rates

Inference Based on New Pareto-Type Records With Applications to Precipitation and Covid-19 Data

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