ON ESTIMATION OF STRESS-STRENGTH RELIABILITY USING LOWER RECORD VALUES FROM ODD GENERALIZED EXPONENTIAL - EXPONENTIAL DISTRIBUTION

This research paper aims to find the estimated values closest to the true values of the reliability functionunder lower record values and to know how to obtain these estimated values using point estimation methodsor interval estimation methods. This helps researchers later in obtaining values of the reliability function intheory and then applying them to reality which makes it easier for the researcher to access the missing datafor long periods such as weather. We evaluated the stress–strength model of reliability based on point andinterval estimation for reliability under lower records by using Odd Generalize Exponential–Exponentialdistribution (OGEE) which has an important role in the lifetime of data. After that, we compared theestimated values of reliability with the real values of it. We analyzed the data obtained by the simulationmethod and the real data in order to reach certain results. The Numerical results for estimated values ofreliability supported with graphical illustrations. The results of both simulated data and real data gave us thesame coverage.

Inferences for generalized Topp-Leone distribution under dual generalized order statistics with applications to Engineering and COVID-19 data

This article accentuates the estimation of a two-parameter generalized Topp-Leone distribution using dual generalized order statistics (dgos). In the part of estimation, we obtain maximum likelihood (ML) estimates and approximate confidence intervals of the model parameters using dgos, in particular, based on order statistics and lower record values. The Bayes estimate is derived with respect to a squared error loss function using gamma priors. The highest posterior density credible interval is computed based on the MH algorithm. Furthermore, the explicit expressions for single and product moments of dgos from this distribution are also derived. Based on order statistics and lower records, a simulation study is carried out to check the efficiency of these estimators. Two real life data sets, one is for order statistics and another is for lower record values have been analyzed to demonstrate how the proposed methods may work in practice.

Nonlinear Programming to Determine Best Weighted Coefficient of Balanced LINEX Loss Function Based on Lower Record Values

Majority research studies in the literature determine the weighted coefficients of balanced loss function by suggesting some arbitrary values and then conducting comparison study to choose the best. However, this methodology is not efficient because there is no guarantee ensures that one of the chosen values is the best. This encouraged us to look for mathematical method that gives and guarantees the best values of the weighted coefficients. The proposed methodology in this research is to employ the nonlinear programming in determining the weighted coefficients of balanced loss function instead of the unguaranteed old methods. In this research, we consider two balanced loss functions including balanced square error (BSE) loss function and balanced linear exponential (BLINEX) loss function to estimate the parameter and reliability function of inverse Rayleigh distribution (IRD) based on lower record values. Comparisons are made between Bayesian estimators (SE, BSE, LINEX, and BLINEX) and maximum likelihood estimator via Monte Carlo simulation. The evaluation was done based on absolute bias and mean square errors. The outputs of the simulation showed that the balanced linear exponential (BLINEX) loss function has the best performance. Moreover, the simulation verified that the balanced loss functions are always better than corresponding loss function.

On generalizations of the optimal choice problem

The article is dedicated to some generalizations of the classical optimal choice problem (the fastidious bride problem, the secretary problem). Let there be a sequence of n identically distributed random variables on the interval [0, 1]. Getting consistently observed values of these variables, we should stop at some moment on one of them, accepting it as the initial point for counting upper or lower record values. In the optimal choice problem and its generalizations, it is needed to make the correct choice of the initial point of counting records, in order to guess the place of the last record (the classical optimal choice problem) or to maximize the expected sum of upper and/or lower record values or the expected total number of upper and/or lower records, obtained by this procedure. A review of results on the uniform distribution of the random variables and some new results concerning the exponential distribution are presented.

Estimation of R for geometric distribution under lower record values

In this paper, the estimation of the stress-strength model R=P(Y<X), based on lower record values is derived when both X and Y are independent and identical random variables with geometric distribution. Estimating R with maximum likelihood estimator and Bayes estimator with non-informative prior information based on mean square errors and LINIX loss functions for geometric distribution are obtained. The confidence intervals of R are constructed by using exact, bootstrap and Bayesian methods. Finally, different methods have been used for illustrative purpose by using simulation. The main results are obtained and introduced through a set of tables and figures with discussions.

Recurrence Relations for Marginal and Joint Moment Generating Functions of Topp-Leone Generated Exponential Distribution based on Record Values and its Characterization

The exact expressions and some recurrence relations are derived for marginal and joint moment generating functions of kth lower record values from Topp-Leone Generated (TLG) Exponential distribution. This distribution is characterized by using the recurrence relation of the marginal moment generating function of kth lower record values.