Various properties of the new model including moments, generating function and mean deviation of the new distribution are derived. 2. 1: e mean values and MSEs of the TIHLIW distribution. The TIHLIW Distribution A simulation study is performed to evaluate the precision of the estimates from both methods. Some of its structural properties are obtained such as the ordinary and incomplete moments, quantile and generating functions, order statistics and probability weighted moments. In addition, the estimation of the stress-strength parameter is discussed. [7] M. Pararai, G. Warahena-Liyanage, and B. O. new class of generalized inverse Weibull distribution with, Weibull and related distributions with applications,”, tronic Journal of Applied Statistical Analysis, Weibull distribution: eory and application,”, A. M. Basheer, “Extended inverse Weibull distribution with, [11] A. M. Basheer, “Alpha power inverse Weibull distribution, [12] A. Marshall and I. Olkin, “A new method for adding a pa-, rameter to a family of distributions with application to the, distributions: Beyond Gram-Charlier expansions and a Skew-, Kurtotic-Normal distribution from a rank transmutation. e performance of these, proposed estimation methods is conducted via some sim-, ulations. The aim of this paper is to introduce an extension of the inverse Weibull distribution which offers a more flexible distribution for modeling lifetime data. Goodness-of-fit measures for relief times data. The new distribution is generated from the logit of a beta random variable and includes the generalized inverse Weibull geometric distribution.Various structural properties of the new distribution including explicit expressions for the moments, moment generating function, mean deviation are derived. Keller et al. the mean square errors (MSEs) for different sample sizes. The new model can be more flexible. The density function of the TIHLIW can be expressed as a linear combination of the inverse Weibull densities. [3] used the IW distribution to describe the degradation phenomena of mechanical components such as crankshaft and pistons of diesel engines. residual life (MRL) has useful applications in economics, life, insurance, biomedical sciences, demography, product, quality control, and product technology (see [19]). The Weibull distribution is one of the most popular and widely used model for failure time in life-testing and reliability theory. The inverse Weibull distribution has the ability to model failure rates which are quite common in reliability and biological studies. Here, we obtain the MGF of the IW dis-, Using the Wright generalized hypergeometric function, e MGF of the IW distribution has the form, e first incomplete moment which follows by setting. In addition, the estimation of the stress-strength parameter is discussed. © 2017 Egyptian Mathematical Society. Its associated probability density function (PDF) has the following form: Keller et al. The CDF of the three-parameter TIHLIW distribution follows, by replacing equation (1) in (3), as For these data, we compare the TIHLIW model with, some rival models, namely, the beta generalized inverse, Weibull geometric (BGIWGc) by Elbatal et al. model parameters of subject distribution. It contains several special cases, namely the Marshall-Olkin exponentiated log-logistic, Marshall-Olkin exponentiated Lomax, Marshall-Olkin Burr XII, Marshall-Olkin log-logistic, Marshall-Olkin Lomax distributions, among others, and most importantly includes all four of the most common types of hazard function: monotonically increasing or decreasing, bathtub and arc-shaped hazard functions. A real data application proves that the TIHLIW, model provides consistently better fits compared to some, is work is mainly a methodological development and has, been applied on secondary data related to the relief times of, 20 patients who received an analgesic, but if required, data, e authors declare that there are no conflicts of interest, e authors are grateful to the Deanship of Scientific Re-, search at King Saud University represented by the Research, Center at the College of Business for financially supporting, [1] A. Drapella, “e complementary weibull distribution: Un-, [4] M. S. Khan and R. King, “Modified inverse Weibull distri-, generalized inverse Weibull distribution,”. Several of its statistical properties are studied. th incomplete moment of the TIHLIW distribution. In Section 4, the. [2] and are based on the Kumaraswamy distribution. e, proposed model can be used, as a good alternative to some. Three different [30], McDonald Weibull, (McW) by Cordeiro et al. [15] proposed the extended odd Weibull-G, and Cordeiro et al. Keller et al. The special case shape == 1is anInverse Exponential distribution. A three-parameter generalized inverse Weibull distribution with decreasing and unimodal failure rate is introduced and studied. Featured on Meta Creating new Help Center documents for Review queues: Project overview We introduce a new family of continuous distributions called the odd Lomax-G class and provide four special models. The importance and flexibility of the new model is assessed using one real data set. and weighted least square methods are used to estimate the, parameters of beta distribution [20]. e importance of. Simulation results are presented to assess the performance of the proposed estimation methods. The density function has infinite negative slope at x = 0 if 0 < k < 1, infinite positive slope at x = 0 if 1 < k < 2 and null slope at x = 0 if k > 2.

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