A Flexible Sine-Transformed Extension of the Nakagami Distribution
Main Article Content
Abstract
A central problem in distribution theory is the construction of flexible models that retain analytical tractability while improving shape adaptability in applications. We propose a sine-transformed extension of the Nakagami distribution by applying a trigonometric mapping to the baseline cumulative distribution function. The resulting sine-transformed Nakagami model preserves the original two-parameter structure, admits closed-form expressions for both the distribution and density functions, and yields tractable reliability measures. We establish key structural properties, including validity, smoothness, and well-defined support; in addition, a hazard modulation representation relative to the classical Nakagami model is derived; and obtain explicit upper-tail asymptotics showing that the proposed transformation preserves the light-tailed class while modifying survival decay in a controlled and interpretable manner. Series representations are developed to facilitate the computation of moments and other related functionals. Parameters are estimated by maximum likelihood using numerical optimization, and finite-sample performance is evaluated through Monte Carlo simulation studies. Applications to real data illustrate that the sine-transformed Nakagami distribution provides an improved goodness of fit compared with the classical Nakagami model for reliability-type data.
Article Details
References
- A. Ahmad, A.A. Rather, O.A. Alqasem, M.E. Bakr, G.T. Mekiso, O.S. Balogun, E. Hussam, A.M. Gemeay, Introducing Novel Arc Cosine-Ψ Class of Distribution with Theory and Data Evaluation Related to Coronavirus, Sci. Rep. 15 (2025), 13069. https://doi.org/10.1038/s41598-025-95084-w.
- C. Alexander, G.M. Cordeiro, E.M.M. Ortega, J.M. Sarabia, Generalized Beta-Generated Distributions, Comput. Stat. Data Anal. 56 (2012), 1880–1897. https://doi.org/10.1016/j.csda.2011.11.015.
- A. Alzaatreh, C. Lee, F. Famoye, A New Method for Generating Families of Continuous Distributions, METRON 71 (2013), 63–79. https://doi.org/10.1007/s40300-013-0007-y.
- A. Alzaatreh, C. Lee, F. Famoye, Family of Generalized Gamma Distributions: Properties and Applications, Hacet. J. Math. Stat. 45 (2016), 869–886. https://doi.org/10.15672/hjms.20156610980.
- D.K. Bhaumik, K. Kapur, R.D. Gibbons, Testing Parameters of a Gamma Distribution for Small Samples, Technometrics 51 (2009), 326–334. https://doi.org/10.1198/tech.2009.07038.
- M. Bourguignon, R.B. Silva, G.M. Cordeiro, The Weibull-G Family of Probability Distributions, J. Data Sci. 12 (2014), 53–68. https://doi.org/10.6339/jds.201401_12(1).0004.
- G.M. Cordeiro, M. de Castro, A New Family of Generalized Distributions, J. Stat. Comput. Simul. 81 (2011), 883–898. https://doi.org/10.1080/00949650903530745.
- N. Eugene, C. Lee, F. Famoye, Beta-Normal Distribution and Its Applications, Commun. Stat. Theory Methods 31 (2002), 497–512. https://doi.org/10.1081/STA-120003130.
- E. Gómez-Déniz, L. Gómez-Déniz, A New Derivation of the Nakagami-m Distribution as a Composite of the Rayleigh Distribution, Wirel. Netw. 30 (2024), 3051–3060. https://doi.org/10.1007/s11276-024-03713-5.
- A.S. Gvozdarev, α-Fluctuating Nakagami-m Fading Model for Wireless Communications, Sensors 25 (2025), 3430. https://doi.org/10.3390/s25113430.
- C. Lee, F. Famoye, O. Olumolade, Beta-Weibull Distribution: Some Properties and Applications to Censored Data, J. Mod. Appl. Stat. Methods 6 (2007), 173–186. https://doi.org/10.22237/jmasm/1177992960.
- A.W. Marshall, I. Olkin, A New Method for Adding a Parameter to a Family of Distributions with Application to the Exponential and Weibull Families, Biometrika 92 (2005), 505–505. https://doi.org/10.1093/biomet/92.2.505.
- A.A. Mir, S.U. Rasool, S.P. Ahmad, A.A. Bhat, T.M. Jawa, N. Sayed-Ahmed, A.H. Tolba, A Robust Framework for Probability Distribution Generation: Analyzing Structural Properties and Applications in Engineering and Medicine, Axioms 14 (2025), 281. https://doi.org/10.3390/axioms14040281.
- G.S. Mudholkar, D.K. Srivastava, Exponentiated Weibull Family for Analyzing Bathtub Failure-Rate Data, IEEE Trans. Reliab. 42 (1993), 299–302. https://doi.org/10.1109/24.229504.
- M. Nakagami, The m-Distribution—A General Formula of Intensity Distribution of Rapid Fading, in: Statistical Methods in Radio Wave Propagation, Elsevier, pp. 3–36, (1960). https://doi.org/10.1016/B978-0-08-009306-2.50005-4.
- M.D. Nichols, W.J. Padgett, A Bootstrap Control Chart for Weibull Percentiles, Qual. Reliab. Eng. Int. 22 (2006), 141–151. https://doi.org/10.1002/qre.691.
- P.E. Oguntunde, O.S. Balogun, H.I. Okagbue, S.A. Bishop, The Weibull-Exponential Distribution: Its Properties and Applications, J. Appl. Sci. 15 (2015), 1305–1311. https://doi.org/10.3923/jas.2015.1305.1311.
- D.O. Oramulu, N. Alsadat, A. Kumar, M.M. Bahloul, O.J. Obulezi, Sine Generalized Family of Distributions: Properties, Estimation, Simulations and Applications, Alexandria Eng. J. 109 (2024), 532–552. https://doi.org/10.1016/j.aej.2024.09.001.
- R.L. Smith, J.C. Naylor, A Comparison of Maximum Likelihood and Bayesian Estimators for the Three-Parameter Weibull Distribution, Appl. Stat. 36 (1987), 358–369. https://doi.org/10.2307/2347795.
- L. Tomy, L. Dominic, Review on Distributions Generated Using Trigonometric Functions, Aust. J. Stat. 53 (2024), 102–119. https://doi.org/10.17713/ajs.v53i3.1827.