نتایج جستجو برای: kumaraswamy distribution
تعداد نتایج: 608533 فیلتر نتایج به سال:
In this paper, we introduce the inverse exponentiated Lomax power series (IELoPS) class of distributions, obtained by compounding and distributions. The IELoPS contains some significant new flexible lifetime distributions that possess powerful physical explications applied in areas like industrial biological studies. comprises as a subclass well several compounded For proposed class, characteri...
The length biased truncated Lomax distribution is introduced in this study as a weighted form of the distribution. distribution’s essential distributional features are investigated. In case complete and type-II censored data, maximum likelihood method provided for estimating population parameter. model parameter asymptotic confidence interval calculated. To demonstrate pattern estimate, sample ...
One of the important features generalized distribution is its ability and flexibility to model real-life data in several applied fields such as medicine, engineering, survival analysis, among others. In this paper, a flexible four-parameter Lomax extension called alpha-power power-Lomax (APPLx) introduced. The APPLx analytically tractable, it can be used quite effectively for analysis. Key math...
شخصی به نام کوماراسوامی(kumaraswamy) یک توزیع برای فرایندهای تصادفی دو طرف کران دار، با کاربرد هیدرولوژیکی معرفی کرد که بسیار شبیه توزیع بتا است ولی مزیت هایی بیش از توزیع بتا دارد.توزیع او به نام kw-g تعمیم یافت که در آن به جای g توزیع های پایه و معروفی مانند نرمال، وایبل، گاما، گامبل، گوسین معکوس و...جایگزین می شودو توزیع های ویژه ی kw با نمادهای kw-gi ،kw-gu ،kw-ga ، kw-n و ... را تولید می کند.
In this paper, we try to contribute the distribution theory literature by incorporating a new bounded distribution, called unit generalized inverse Weibull (UGIWD) in (0, 1) intervals transformation method. The proposed exhibits increasing and bathtub shaped hazard rate function. We derive some basic statistical properties of distribution. Based on complete sample, model parameters are obtained...
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