MLE of Bell type (univariate continuous) distributions {MLE}

MLE of Bell type (univariate continuous) distributions

Description

MLE of Bell type (univariate continuous) distributions.

Usage

bell.mle(x, a, b, k, lambda, distr = "BB12", method = "B")

Arguments

x

A vector with continuous valued data.

a

Initial value for the strictly positive scale parameter of the baseline distribution.

b

Initial value for the strictly positive shape parameter of the baseline distribution.

k

Initial value for the strictly positive shape parameter of the baseline distribution.

lambda

Initial value for the strictly positive parameter of the Bell distribution.

distr

The distribution to fit, "BB12" stands for the Bell Burr-12, "BBX" for the Bell Burr-10, "BE" for the Bell exponential, "BEW" for the Bell exponentiated Weibull, "BEE" for the Bell exponentiated exponential, "BF" for the Bell Fisk distribution, "BL" for the Bell Lomax, "BW" for the Bell Weibull distribution, "CBB12" for the complementary Bell Burr-12, "CBBX" for the complementary Bell Burr-X distribution, "CBE" for the complementary Bell exponential distribution, "CBEW" for the complementary Bell exponentiated Weibul distribution, "CBEE" for the complementary Bell extended exponentia distribution, "CBF" for the complementary Bell Fisk distribution, "CBL" for the complementary Bell Lomax distribution, and "CBW" for the complementary Bell Weibull distribution.

method

The procedure for optimising the log-likelihood function after setting the initial values of the parameters and data vector for which the Bell-based distributions are fitted. It could be "Nelder-Mead," "BFGS," "CG," "L-BFGS-B," or "SANN." "BFGS" is set as the default.

Details

These functions facilitate the fitting of Bell-based extended distributions, including the Bell Burr-12(a, b, k, lambda), Bell Burr-10(a, lambda), Bell exponential(a, lambda), Bell exponentiated Weibull(a, b, k, lambda), Bell extended exponential(a, b, lambda), Bell Fisk(a, b, lambda), Bell Lomax(a, b, lambda), Bell Weibull(a, b, lambda), complementary Bell Burr-12(a, b, k, lambda), complementary Bell Burr-10(a, lambda), complementary Bell exponential(a, lambda), complementary Bell exponentiated Weibull(a, b, k, lambda), complementary Bell extended exponential(a, b, lambda), complementary Bell Fisk(a, b, lambda), complementary Bell Lomax(a, b, lambda), and complementary Bell Weibull(a, b, lambda).

Value

A list including:

param

The parameters of the distribution.

loglik

The log-likelihood value.

Author(s)

Muhammad Imran.

R implementation and documentation: Muhammad Imran [e-mail].

References

Fayomi A., Tahir M. H., Algarni A., Imran M. and Jamal F. (2022). A new useful exponential model with applications to quality control and actuarial data. Computational Intelligence and Neuroscience, 2022.

Alanzi, A. R., Imran M., Tahir M. H., Chesneau C., Jamal F. Shakoor S. and Sami, W. (2023). Simulation analysis, properties and applications on a new Burr XII model based on the Bell-X functionalities. AIMS Mathematics, 8(3): 6970–7004.

Algarni A. (2022). Group Acceptance Sampling Plan Based on New Compounded Three- Parameter Weibull Model. Axioms, 11(9): 438.

Kleiber, C. and Kotz, S. (2003). Statistical size distributions in economics and actuarial sciences. John Wiley & Sons.

Zimmer W. J., Keats J. B. and Wang F. K. (1998). The Burr XII distribution in reliability analysis. Journal of Quality Technology, 30(4): 386–394.

Nadarajah S., Cordeiro G. M. and Ortega E. M. (2013). The exponentiated Weibull distribution: a survey. Statistical Papers, 54: 839–877.

Nadarajah S. (2011). The exponentiated exponential distribution: a survey. Advances in Statistical Analysis, 95: 219–251.

See Also

disc.mle

Examples

x <- rgamma(1000, 3, 5)
# Fitting of the Bell Burr-12 (BB12) distribution
bell.mle(x, a = 2.1, b = 1.3, k = 0.02, lambda = 1.2, distr = "BB12", method = "B")
# Fitting of the Bell exponential (BE) distribution
bell.mle(x, a = 2.1, lambda = 0.5 ,distr = "BE", method = "B")