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This study sought to identify the most accurate forecasting models for COVID-19-confirmed cases, deaths, and recovered patients in Pakistan. For COVID-19, time series data are available from 16 April to 15 August 2021 from the Ministry of National Health Services Regulation and Coordination’s health advice portal. Descriptive as well as time series models, autoregressive integrated moving average, exponential smoothing models (Brown, Holt, and Winters), neural networks, and Error, Trend, Seasonal (ETS) models were applied. The analysis was carried out using the R coding language. The descriptive analysis shows that the average number of confirmed cases, COVID-19-related deaths, and recovered patients reported each day were 2,916, 69.43, and 2,772, respectively. The highest number of COVID-19 confirmed cases and fatalities per day, however, were recorded on April 17, 2021 and April 27, 2021, respectively. ETS (M, N, M), neural network, nonlinear autoregressive (NNAR) (3, 1, 2), and NNAR (8, 1, 4) forecasting models were found to be the best among all other competing models for the reported confirmed cases, deaths, and recovered patients, respectively. COVID-19-confirmed outbreaks, deaths, and recovered patients were predicted to rise on average by around 0.75, 5.08, and 19.11% daily. These statistical results will serve as a guide for disease management and control.

This study aims to determine the primary determination of FDI inflow to Egypt using machine learning algorithms and the ARIMA model and get an accurate prediction of FDI inflow to Egypt during the current decade (2020-2030) and approved that the gradient boosting model is the most accurate algorithms. Also, we find stability in economic indicators in Egypt during the current decade using the ARIMA model. The last step approved that the primary determinant of FDI inflow to Egypt is the Human Development Index, followed by population size, gross domestic product per capita, lending rate, and gross domestic product value., Author(s): Mohamed F. Abd El-Aal (corresponding author) [1]; Ali Algarni (corresponding author) [2]; Aisha Fayomi [3]; RAahayu Abdul Rahman [4]; Khudir Alrashidi [5] 1. Introduction Developed and developing countries seek [...]

In this article, we highlight a new three-parameter heavy-tailed lifetime distribution that aims to extend the modeling possibilities of the Lomax distribution. It is called the extended Lomax distribution. The considered distribution naturally appears as the distribution of a transformation of a random variable following the logweighted power distribution recently introduced for percentage or proportion data analysis purposes. As a result, its cumulative distribution has the same functional basis as that of the Lomax distribution, but with a novel special logarithmic term depending on several parameters. The modulation of this logarithmic term reveals new types of asymetrical shapes, implying a modeling horizon beyond that of the Lomax distribution. In the first part, we examine several of its mathematical properties, such as the shapes of the related probability and hazard rate functions; stochastic comparisons; manageable expansions for various moments; and quantile properties. In particular, based on the quantile functions, various actuarial measures are discussed. In the second part, the distribution’s applicability is investigated with the use of the maximum likelihood estimation method. The behavior of the obtained parameter estimates is validated by a simulation work. Insurance claim data are analyzed. We show that the proposed distribution outperforms eight well-known distributions, including the Lomax distribution and several extended Lomax distributions. In addition, we demonstrate that it gives preferable inferences from these competitor distributions in terms of risk measures. [ABSTRACT FROM AUTHOR]

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Mycobacterium tuberculosis (Mtb) coinfection has been observed in a number of nations and it is connected with severe illness and death. The paper studies a reaction–diffusion within-host Mtb/SARS-CoV-2 coinfection model with immunity. This model explores the connections between uninfected epithelial cells, latently Mtb-infected epithelial cells, productively Mtb-infected epithelial cells, SARS-CoV-2-infected epithelial cells, free Mtb particles, free SARS-CoV-2 virions, and CTLs. The basic properties of the model's solutions are verified. All equilibrium points with the essential conditions for their existence are calculated. The global stability of these equilibria is established by adopting compatible Lyapunov functionals. The theoretical outcomes are enhanced by implementing numerical simulations. It is found that the equilibrium points mirror the single infection and coinfection states of SARS-CoV-2 with Mtb. The threshold conditions that determine the movement from the monoinfection to the coinfection state need to be tested when developing new treatments for coinfected patients. The impact of the diffusion coefficients should be monitored at the beginning of coinfection as it affects the initial distribution of particles in space. [ABSTRACT FROM AUTHOR]

Recently, vitamin D deficiency has been considered the most common nutritional deficiency all over the world that affects all age groups. The current study aimed to evaluate the level of awareness of the general population in Taif city, Saudi Arabia, toward vitamin D deficiency, vitamin D sources, and toxicity. A cross-sectional study was conducted. A validated questionnaire was distributed to the participants through social media platforms. The study included 431 participants. About half of the participants (46.6%) reported having a vitamin D deficiency history, and 13.5% of the participants were healthcare providers. Only 103 participants (23.9%) showed a good awareness level regarding vitamin D's role, sources, and toxicity. A better awareness regarding the benefits of vitamin D was indicated in females (p-value<0.001), participants with a university degree (p-value<0.001), and healthcare professionals (HCPs) (p-value<0.001). Respondents from the medical field, hold a university degree, and with a history of vitamin D deficiency had a significantly higher awareness level regarding the sources of vitamin D (p-value<0.001, p-value=0.028, and p-value=0.024, respectively). In addition, being one of the medical staff and holding a university degree were significant factors that had a favorable effect on the awareness level regarding the toxicity of vitamin D (pvalue< 0.001, p-value=0.009 respectively). The study demonstrated a low level of awareness among the general population toward vitamin D deficiency. Further studies are recommended to fill the awareness gap and to improve the awareness of vitamin D, its benefits, sources, and toxicity. [ABSTRACT FROM AUTHOR]

Unit distributions are typically used in probability theory and statistics to illustrate useful quantities with values between zero and one. In this paper, we investigated an appropriate transformation to propose the unit-exponentiated half-logistic distribution (UEHLD), which is also beneficial for modelling data on the unit interval. This distribution's mathematical features are supplied, including moments, probability-weighted moments, incomplete moments, various entropy measures, and stress–strength reliability. Using well-known estimation techniques such as the maximum likelihood, maximum product of spacing, and Bayesian inference, the estimators of the parameters relevant to the proposed distribution were determined. A comprehensive simulation analysis is provided to examine the performance of parameter estimation approaches on finite samples. The proposed distribution was realistically applied to data on economic growth and data on the tensile strength of polyester fibers to provide an explanation. Furthermore, the analysis of COVID-19 data from Britain as a medical statistical dataset is provided. The experimental results demonstrate that the suggested UEHLD yields a better comparison with some new unit distributions, as well as other unbounded distributions. [ABSTRACT FROM AUTHOR]

In this study, we introduce a new compounded model called the complementary Bell–Weibull model and use it to address the problem of a group acceptance sampling plan predicted on a truncated life test. The median lifespan is used as a quality index to obtain the design constraints, namely sample size and approval number, under a predefined consumerś risk and test termination period. Additionally, two real data applications are presented, and unknown parameters are estimated using the maximum likelihood approach. [ABSTRACT FROM AUTHOR]

The accuracy of the statistical learning model depends on the learning technique used which in turn depends on the dataset's values. In most research studies, the existence of missing values (MVs) is a vital problem. In addition, any dataset with MVs cannot be used for further analysis or with any data driven tool especially when the percentage of MVs are high. In this paper, the authors propose a novel algorithm for dealing with MVs depending on the feature selection (FS) of similarity classifier with fuzzy entropy measure. The proposed algorithm imputes MVs in cumulative order. The candidate feature to be manipulated is selected using similarity classifier with Parkash's fuzzy entropy measure. The predictive model to predict MVs within the candidate feature is the Bayesian Ridge Regression (BRR) technique. Furthermore, any imputed features will be incorporated within the BRR equation to impute the MVs in the next chosen incomplete feature. The proposed algorithm was compared against some practical state-of-the-art imputation methods by conducting an experiment on four medical datasets which were gathered from several databases repository with MVs generated from the three missingness mechanisms. The evaluation metrics of mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2 score) were used to measure the performance. The results exhibited that performance vary depending on the size of the dataset, amount of MVs and the missingness mechanism type. Moreover, compared to other methods, the results showed that the proposed method gives better accuracy and less error in most cases. [ABSTRACT FROM AUTHOR]

The compounding approach is used to introduce a new family of distributions called exponentiated Bell G, analogy to exponentiated G Poisson. Several essential properties of the proposed family are obtained. The special model called exponentiated Bell exponential (EBellE) is presented along with properties. Furthermore, the risk theory related measures including value-at-risk and expected-shortfall are also computed for the special model. Group acceptance sampling plan is designed when a lifetime of a product or item follows an EBellE model taking median as a quality parameter. The parameters of the proposed model are estimated by considering maximum likelihood approach along with simulation analysis. The usefulness of the proposed model is illustrated by practical means which yield better fits as compared to several exponential related extended models. [ABSTRACT FROM AUTHOR]

A robust generalisation of the Gumbel distribution is proposed in this article. This family of distributions is based on the T-X paradigm. From a list of special distributions that have evolved as a result of this family, three separate models are also mentioned in this article. A linear combination of generalised exponential distributions can be used to characterise the density of a new family, which is critical in assessing some of the family's properties. The statistical features of this family are determined, including exact formulations for the quantile function, ordinary and incomplete moments, generating function, and order statistics. The model parameters are estimated using the maximum likelihood method. Further, one of the unique models has been systematically studied. Along with conventional skewness measures, MacGillivray skewness is also used to quantify the skewness measure. The new probability distribution also enables us to determine certain critical risk indicators, both numerically and graphically. We use a simulated assessment of the suggested distribution, as well as apply three real-world data sets in modelling the proposed model, in order to ensure its authenticity and superiority. [ABSTRACT FROM AUTHOR]

Forecasting has been playing an important role in different fields of life, i.e., in decision-making activities of management, to predict uncertain events within an organization, in weather forecasting, in flood forecasting, etc. Stakeholders involved in betting market take advantage of tennis forecasting directly or indirectly. Winning probability calculated using forecasting models helps the bettors in deciding whether to place a bet or not. Keeping in view the importance of tennis forecasting, the Bradley-Terry model is used to model men's professional tennis for predicting match outcomes in tennis matches of men's singles. Model coefficients are estimated using data from January 2019 to September 2020 of 3439 matches. Ratings for each player are calculated using model coefficients. Player rankings are then calculated using these ratings. Comparison of model rankings with ATP rankings has shown satisfactory results. Winning probability for each player is calculated using model coefficients and ratings. These probability predictions are evaluated against four measures of performance. The results reveal that surface on which a game is played on contributes significantly towards a player's performance. Due to this impact of the surface, our model has produced superior player rankings for certain players who had been ranked very low in official ATP rankings. According to most of the performance measures, the model has shown good results for clay court data which are closely followed by hard court data. To calculate return on investment, model results are compared with the bookmakers' average odds and best available odds. It has been found that return on investment for a fitted model is highest in the case of clay court data in comparison to bookmaker's average odds and best odds. [ABSTRACT FROM AUTHOR]

In this paper, the maximum likelihood and Bayesian estimation methods are considered to estimate the unknown parameters, reliability, and hazard rate functions of the Nadarajah–Haghighi (NH) distribution based on adaptive type‐I progressive hybrid censoring (ATIPHC) scheme. The Bayesian estimation is obtained under the assumption of independent gamma priors. The Bayes estimators cannot be obtained in closed form, therefore Lindley's approximation and Markov chain Monte Carlo (MCMC) method are used to solve this problem. The approximate confidence (ACIs) and credible intervals are also considered. To compare the efficiency of the different proposed estimators, a simulation study is considered and the performance of the different estimators are compared using mean square error (MSE) and interval length criterion. Finally, one real data set is analyzed in order to show how these mentioned estimators can be applied in practice. The simulation and real data analysis showed that the Bayes estimates have the smallest MSEs and the BCIs have the smallest lengths compared to their conventional counterparts. [ABSTRACT FROM AUTHOR]

The challenge of estimating the parameters for the inverse Weibull (IW) distribution employing progressive censoring Type-I (PCTI) will be addressed in this study using Bayesian and non-Bayesian procedures. To address the issue of censoring time selection, qauntiles from the IW lifetime distribution will be implemented as censoring time points for PCTI. Focusing on the censoring schemes, maximum likelihood estimators (MLEs) and asymptotic confidence intervals (ACI) for unknown parameters are constructed. Under the squared error (SEr) loss function, Bayes estimates (BEs) and concomitant maximum posterior density credible interval estimations are also produced. The BEs are assessed using two methods: Lindley's approximation (LiA) technique and the Metropolis-Hasting (MH) algorithm utilizing Markov Chain Monte Carlo (MCMC). The theoretical implications of MLEs and BEs for specified schemes of PCTI samples are shown via a simulation study to compare the performance of the different suggested estimators. Finally, application of two real data sets will be employed. [ABSTRACT FROM AUTHOR]

Recently, Internet of Things (IoT) and cloud computing environments become commonly employed in several healthcare applications by the integration of monitoring things such as sensors and medical gadgets for observing remote patients. For availing of improved healthcare services, the huge count of data generated by IoT gadgets from the medicinal field can be investigated in the CC environment rather than relying on limited processing and storage resources. At the same time, earlier identification of chronic kidney disease (CKD) becomes essential to reduce the mortality rate significantly. This study develops an ensemble of deep learning based clinical decision support systems (EDL-CDSS) for CKD diagnosis in the IoT environment. The goal of the EDL-CDSS technique is to detect and classify different stages of CKD using the medical data collected by IoT devices and benchmark repositories. In addition, the EDL-CDSS technique involves the design of Adaptive Synthetic (ADASYN) technique for outlier detection process. Moreover, an ensemble of three models, namely, deep belief network (DBN), kernel extreme learning machine (KELM), and convolutional neural network with gated recurrent unit (CNN-GRU), are performed. Finally, quasi-oppositional butterfly optimization algorithm (QOBOA) is used for the hyperparameter tuning of the DBN and CNN-GRU models. A wide range of simulations was carried out and the outcomes are studied in terms of distinct measures. A brief outcomes analysis highlighted the supremacy of the EDL-CDSS technique on exiting approaches. [ABSTRACT FROM AUTHOR]

Physical health plays an important role in overall well-being of the human beings. It is the most observed dimension of health among others such as social, intellectual, emotional, spiritual and environmental dimensions. Due to exponential increase in the development of wireless communication techniques, Internet of Things (IoT) has effectively penetrated different aspects of human lives. Healthcare is one of the dynamic domains with ever-growing demands which can be met by IoT applications. IoT can be leveraged through several health service offerings such as remote health and monitoring services, aided living, personalized treatment, and so on. In this scenario, Deep Learning (DL) models are employed in proficient disease diagnosis. The current research work presents a new IoT-based physical health monitoring and management method using optimal Stacked Sparse Denoising Autoencoder (SSDA) technique i.e., OSSDA. The proposed model utilizes a set of IoT devices to collect the data from patients. Imbalanced class problem poses serious challenges during disease diagnosis process. So, the OSSDA model includes SyntheticMinority Over-Sampling Technique (SMOTE) to generate artificialminority class instances to balance the class distribution. Further, the hyperparameter settings of the OSSDA model exhibit heavy influence upon the classification performance of SSDA technique. The number of hidden layers, sparsity, and noise count are determined by Sailfish Optimizer (SFO). In order to validate the effectiveness and performance of the proposed OSSDA technique, a set of experiments was conducted on diabetes and heart disease datasets. The simulation results portrayed a proficient diagnostic outcome from OSSDA technique over other methods. The proposed method achieved the highest accuracy values i.e., 0.9604 and 0.9548 on the applied heart disease and diabetes datasets respectively. [ABSTRACT FROM AUTHOR]

This document is a correction notice for a paper titled "A new extended gumbel distribution: Properties and application." The correction addresses errors in the funding statement and author contributions. The correct funding statement acknowledges the Deanship Scientific Research (DSR), King Abdulaziz University, Jeddah for their technical and financial support. The correct author contributions are listed, including the roles of each author in the conceptualization, data curation, formal analysis, investigation, methodology, project administration, resources, software, supervision, validation, visualization, and writing of the paper. The correction is reported by Aisha Fayomi, Sadaf Khan, Muhammad Hussain Tahir, Ali Algarni, Farrukh Jamal, and Reman Abu-Shanab. [Extracted from the article]

This article presents the expected Bayesian (E‐Bayesian) estimation of the scale parameter, reliability and failure rate functions of two‐parameter bathtub‐shaped lifetime distribution under type‐II censoring data with. Squared error loss function and gamma distribution as a conjugate prior distribution for the unknown parameter are used to obtain the E‐Bayesian estimators. Also, three different prior distributions for the hyperparameters for the E‐Bayesian estimators are considered. Some properties of the E‐Bayesian estimators are studied. Using minimum mean square error criteria, a simulation study is conducted to compare the performance of the E‐Bayesian estimators and the corresponding Bayes and maximum likelihood estimators. A real data set is analysed to show the applicability of the different proposed estimators. The numerical results show that the E‐Bayesian estimators perform better than the classical and Bayesian estimators. [ABSTRACT FROM AUTHOR]

The quality of the products coming from different lines of production requires some tests called comparative life tests. For lines having the same facility, the lifetime of the product is distributed by Burr XII, the lifetime distribution, and units are tested under type-I generalized hybrid censoring scheme. The observed censoring data are used under maximum likelihood and the Bayes method to estimate the model parameters. The theoretical results are discussed and assessed through data analysis and Monte Carlo simulation study. Finally, we reported some brief comments obtained from numerical computation. [ABSTRACT FROM AUTHOR]

In this study, an extension of the generalized Lindley distribution using the Marshall-Olkin method and its own sub-models is presented. This new model for modelling survival and lifetime data is flexible. Several statistical properties and characterizations of the subject distribution along with its reliability analysis are presented. Statistical inference for the new family such as the Maximum likelihood estimators and the asymptotic variance covariance matrix of the unknown parameters are discussed. A simulation study is considered to compare the efficiency of the different estimators based on mean square error criterion. Finally, a real data set is analyzed to show the flexibility of our proposed model compared with the fit attained by some other competitive distributions. [ABSTRACT FROM AUTHOR]

In this article, we apply a statistical model for forecasting the quantum entanglement between a two‐qubit and optical field in binomial distribution. We explore the link between the atomic Fisher information, quantum entropy, and the statistical properties of the field. The qubit‐qubit entanglement is investigated through concurrence during the interaction time. The dynamics of the statistical quantities will be forecasted using the time series and neural network models. The effect of the field distribution parameter (number of successes) is examined by the time series models and artificial neural network. We compare the accuracy of both modes from the perspective of the dynamic of the quantum entropy and atomic Fisher information. A statistical description for the data has been obtained and is discussed to show the statistical technique analysis the data of statistical quantities. The results obtained have several applications and are related with quantum statistics and quantum information processing. [ABSTRACT FROM AUTHOR]

Probability distributions have been in use for modeling of random phenomenon in various areas of life. Generalization of probability distributions has been the area of interest of several authors in the recent years. Several situations arise where joint modeling of two random phenomenon is required. In such cases the bivariate distributions are needed. Development of the bivariate distributions necessitates certain conditions, in a field where few work has been performed. This paper deals with a bivariate beta-inverse Weibull distribution. The marginal and conditional distributions from the proposed distribution have been obtained. Expansions for the joint and conditional density functions for the proposed distribution have been obtained. The properties, including product, marginal and conditional moments, joint moment generating function and joint hazard rate function of the proposed bivariate distribution have been studied. Numerical study for the dependence function has been implemented to see the effect of various parameters on the dependence of variables. Estimation of the parameters of the proposed bivariate distribution has been done by using the maximum likelihood method of estimation. Simulation and real data application of the distribution are presented. [ABSTRACT FROM AUTHOR]

The generalization of the Anderson-Darling (AD) test under neutrosophic statistics is presented in this paper. We present the designing and operating procedure of neutrosophic Anderson-Darling when the quality of interest follows the neutrosophic normal distribution. The application of the proposed test is given using data from the renewable energy field. From the analysis of the data, it is concluded that the proposed test is effective and information to be applied when the data is recorded from the complex system in the renewable energy field. [ABSTRACT FROM AUTHOR]

Products come from different lines with the same facility are tested under comparative life tests which known with the jointly censoring scheme. In this paper, two sets of products under the same facility have Weibull lifetime distributions are selected to test under Type- I generalized hybrid censoring scheme (GHCS). The observed censoring data are used to build the maximum likelihood (ML) estimators as well as approximate confidence intervals for the model parameters. Also, Bayes estimators with the help of MCMC methods are discussed. The analysis of simulated data set with Monte Carlo simulation study is used to illustrate and compare the theoretical results. Finally, a brief comment is summarized in concluding section. [ABSTRACT FROM AUTHOR]

In this article we present a scheme of the interaction between a single qubit (SQ) and radiation field (RF) follows the even "binomial distribution" (BD). The density matrix of the SQ-RF system is given in terms of the number of photon transition and RF distribution parameters. The photon statistics and topographical properties of the RF are quantified by the evolution of Mandel parameter and tomographic entropy respectively. We explore the relation between the tomographic entropy and Wehrl entropy of the RF of the estimators of model parameters in the even BD. The results show that the SQ-RF correlation is connected by the tomographical and statistical properties of the proposed system. [ABSTRACT FROM AUTHOR]

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For the first time, ten frequentist estimation methods are considered on stress-strength reliability R = P(Y < X) when X and Y are two independent Weibull distributions with the same shape parameter. The start point to estimate the parameter R is the maximum likelihood method. Other than the maximum likelihood method, a nine frequentist estimation methods are used to estimate R, namely: least square, weighted least square, percentile, maximum product of spacing, minimum spacing absolute distance, minimum spacing absolute-log distance, method of Cramér-von Mises, Anderson-Darling and Right-tail Anderson-Darling. We also consider two parametric bootstrap confidence intervals of R. We compare the efficiency of the different proposed estimators by conducting an extensive Mont Carlo simulation study. The performance and the finite sample properties of the different estimators are compared in terms of relative biases and relative mean squared errors. The Mont Carlo simulation study revels that the percentile and maximum product of spacing methods are highly competitive with the other methods for small and large sample sizes. To show the applicability and the importance of the proposed estimators, we analyze one real data set. [ABSTRACT FROM AUTHOR]

Motivated by an accelerated degradation test (ADT) on the power gain of microwave power amplifiers, in this article we propose a model-ranking approach for the estimation of some important reliability characteristics. Different degradation models and statistical lifetime distributions are applied to model the data obtained from the ADT. We study the effect of model misspecification in estimating the reliability characteristics when the behavior of the degradation process and the underlying degradation-data-generating mechanism are unknown. We then propose a model-ranking approach with weighted estimation procedures when multiple candidate models are under consideration. Through a Monte Carlo simulation study, we show that the proposed approach is robust and insensitive to model misspecification. Finally, the ADT data from the motivating example are used to illustrate the proposed methodologies. [ABSTRACT FROM AUTHOR]

In this article we estimate the nonlocal correlation between a two qubits and OF (optical field) initially started in the even deformed binomial distribution (EDBS). Explicit forms of the EDBS as the initial states of the RF are presented. We investigate the dynamical and statistical properties of the linear entropy as a quantifier of the correlation and purity of the proposed system. Also, the correlation between the two qubits is quantified by the negativity. Some statistical estimations of the RF are investigated by of Mandel parameter and compared by the two types of correlation The relation between the qubit inversion, two qubits-RF nonlocal correlation, negativity and Mandel parameter is obtained. The effective of Stark shift on the dynamical operators for the RF distribution is examined. The results are shown the a high amount of field-qubit nonlocal is reached for a high value of deformation parameter. Also, the statistical properties of the field is Poissonian or sub-Poissonian photon distribution. [ABSTRACT FROM AUTHOR]

Time to failure in the lifetime experiments, common that units or individuals are failure with more than one caused of failure defined by competing risks problem. In this paper, we consider the problem of competing risks model when the failure time of units or individuals have Nadarajaha and Haghighi lifetime distribution [1] based on type-II censoring schemes. The point and interval estimates of model parameters are discussed with maximum likelihood and Bayes methods. In Bayesian approach, the MCMC method is employed. The theoretical results are discussed through real life data analysis and simulation study. Finally, we discussed the numerical results in some brief comments. [ABSTRACT FROM AUTHOR]

In this paper, Bayesian and non Bayesian methods are adapted to determined interval and point estimations under Type-I hybrid Progressively censored for generalized Gompertz distribution. The special case from MCMC adjust Metropolis-Hastings within Gibbs sampling in the case Bayesian procedure is used to solve the double integrations. Numerical examples including generation data sets are shown to make a conclusion on methods estimations used here. [ABSTRACT FROM AUTHOR]

This paper, discusses the problem of partially step-stress ALTs (accelerated life tests) form Rayleigh competing risks model. Type-II censored scheme is used in obtaining the observed censoring data. The method of MLE (maximum likelihood estimation) of the model parameters for point and approximate confidence intervals are considered. Also, bootstrap confidence intervals of model parameters are discussed. Simulation study is adopted to assess and compare our proposed method. Finally, some comment to illustrate the behavior of numerical results. [ABSTRACT FROM AUTHOR]

A new extension of exponential distribution, named as the Type I half logistic exponential distribution is introduced in this paper. Explicit expressions for the moments, probability weighted, quantile function, mean deviation, order statistics, and Renyi entropy are investigated. Parameter estimates of the new distribution are obtained based on maximum likelihood procedure. Two real data sets are employed to show the usefulness of the new distribution. [ABSTRACT FROM AUTHOR]