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In this article, we propose a new model to model the spatial count data on a two-dimensional regular grid using binomial thinning operator with dependent Bernoulli counting series. The model is called "first-order spatial dependent count integer-valued autoregressive (Sp-DCINAR(1,1)) model." Some of its properties have been derived and the estimation of the parameters of the model are obtained by the Yule-Walker method, conditional least squares method and conditional maximum likelihood method. Finally, numerical results are presented together with an application to a two-dimensional practical data set. [ABSTRACT FROM AUTHOR]

A new non-linear stationary process for time series of counts is introduced. The process is composed of the survival and innovation component. The survival component is based on the generalized zero-modified geometric thinning operator, where the innovation process figures in the survival component as well. A few probability distributions for the innovation process have been discussed, in order to adjust the model for observed series with the excess number of zeros. The conditional maximum likelihood and the conditional least squares methods are investigated for the estimation of the model parameters. The practical aspect of the model is presented on some real-life data sets, where we observe data with inflation as well as deflation of zeroes so we can notice how the model can be adjusted with the proper parameter selection. [ABSTRACT FROM AUTHOR]

In this paper, estimation and prediction inference of power Muth distribution, with the progressive censoring data, are described. The maximum likelihood and Bayesian approaches of the unknown parameters are considered. Several Bayesian estimators are obtained against different symmetric and asymmetric loss functions, such as squared error, linex and general entropy. Also, the asymptotic confidence intervals and highest posterior density credible intervals of them are derived. Most focus of this paper is Bayesian prediction of the removed units in multiple stages of the progressively censored sample, so that, the Gibbs and Metropolis samplers are used, to reach this end. To compare the performance of different methods, Mont Carlo simulation is employed. Moreover, one practical data set is analyzed for illustrative purposes. [ABSTRACT FROM AUTHOR]

This article proposes a flexible additive model that adequately describes complex reliability and survival data. The proposed methodology, referred to as the flexible exponential power-Gompertz (FEPG4) distribution, is able to characterize the behavior of a complex system whose failure times have bathtub-shaped, with clear burn-in and wear-out change points and a low, yet lengthy, flat middle segment as its underlying failure rate distribution. We discuss some properties of the model. Parameter inferences are proposed under maximum likelihood and Bayesian techniques. We determine the Bayes estimators of the FEPG4 parameters and used Hamiltonian Monte Carlo for posterior simulations. Extensive simulation experiments are performed to validate the proposed estimators. For assessing the potential of the FEPG4, the model is compared with other recent bathtub distributions constructed via the same approach on devices' failure and running times (censored and non-censored case) and failure times of some devices, each with the bathtub failure rate. Seven parametric and non-parametric selection criteria and other supporting plots are utilized for comparison purposes. Findings indicate that the FEPG4 model might be the best alternative for representing device failure times, particularly when the bathtub-shaped failure rate of the available data clearly illustrates its three phases. [ABSTRACT FROM AUTHOR]

AbstractIn this study, we introduce a pliant stationary first-order integer-valued autoregressive (INAR) process with weighted negative binomial Lindley innovations. The main properties of the model are derived. The methods of conditional maximum likelihood, conditional least square and Yule-Walker are used for estimating the process parameters, while the efficiency of these three methods is evaluated through a simulation study. Finally, the practical aspect of the proposed INAR(1) process is discussed on two time series of the monthly number of criminal mischief reports in Pittsburgh and the daily COVID-19 death cases in Paraguay. [ABSTRACT FROM AUTHOR]

The fractional Lindley distribution is used to model the distribution of perturbations in count data regressions, which allow for dealing with widely dispersed data. It is obtained from the non-fractional Lindley distribution by replacing the support $\mathbb{T} = {\mathbb{R}^ + }$ T = R + by ${\mathbb{T}} = {\mathbb{N}}\backslash \{ 0\} $ T = N ∖ { 0 } and applying time scale theory, whose ambition is to unify the theories of difference equations and differential equations, integral and differential calculus, and the calculus of finite differences. It thus provides a framework for the study of dynamical systems in discrete-continuous time. Delta moments are discrete-time Laplace transforms of the frequency function of the fractional Lindley distribution. The parameter of the fractional Lindley distribution is estimated by least squares, weighted least squares, maximum likelihood, moments, and proportions. The moment estimator always exists, so that delta moments result from the nabla Laplace transform of the frequency function of the fractional Lindley distribution. The maximum likelihood estimates have the least mean-square errors. The proportion method works satisfactorily only when the mode of the distribution is null and the proportion of zeros is high. A simulation allows for quantifying the mean-square errors associated with the estimators. A count regression based on the fractional Lindley distribution with data on the total number of stays after hospital admission among U.S. residents aged 65 and over shows that the Akaike information criteria is significantly lower than with the uniform Poisson and Poisson regressions. [ABSTRACT FROM AUTHOR]

This paper is devoted to the properties and applications of a new discrete model on Z . This model represents the difference of two independent Poisson-Lindley random variables with the same common parameter. The properties, such as, distribution function, moments, quantile function and order statistics are studied. Among its features, it is revealed to be useful for the analysis of overdispersed data sets and also has a flexible probability mass function which is unimodal in nature. Estimation of its parameter by the method of maximum likelihood is obtained. Efficiency of this model with several pertinent competitive models is proved by applying the proposed model to a data set on the change of the stock price. Thanks to its discrete nature, the changes in stock price represent a set of discrete values which suits the nature of the proposed model. [ABSTRACT FROM AUTHOR]

This paper makes a new contribution to distribution theory and application. In it, we develop and investigate a pliant three-parameter lifetime distribution based on a cosine weighting, called the cos-Lindley distribution. The idea of this weighting is to inject the oscillating behavior of the cosine function to flexibilize the functional capabilities of the Lindley distribution. The presence of a periodic hazard rate function elevates the new distribution in terms of applicability to real-world data sets. The main properties of the cos-Lindley distribution, such as the moments and moment generating function, are investigated. A maximum likelihood approach is considered for the study of the estimation of the model parameters. Three real-world data sets on the service times of the windshield and the lifetimes of fatigue fracture of Kevlar are taken into account to demonstrate the utility and robustness of the model. In particular, it is shown that the cos-Lindley model can outperform the Lindley, modified Lindley, and new exponential trigonometric models. [ABSTRACT FROM AUTHOR]

In this paper, we propose a tractable Kumaraswamy Bell exponential (KwBE) distribution as a submodel of the Kumaraswamy Bell-G family of distributions. Several well-established properties are obtained for the KwBE distribution, such as the linear functional representation, $r$ r th moment, incomplete moment, moment generating function using Wright generalized hyper-geometric function, conditional moment and Réyni entropy. Based on the KwBE model, a group acceptance sampling plan (GASP) for the truncated life test is presented using median life as a quality index. Moreover, the essential design parameters are derived by defining the consumer risk and the test termination duration. The comparative study of GASP with ordinary sampling plan (OSP) is performed. A simulation study is performed to highlight the behavior of the estimates. On the inferential side, the associated parameters are estimated using a well-established maximum likelihood estimation method. The detailed model's comparison analysis, graphical as well as numerical evidence to real-data applications, supports the theoretical work. [ABSTRACT FROM AUTHOR]

In the present paper, we concentrate on an INAR(1) model with flexible binomial-discrete Poisson Lindley innovations (BDPLINAR(1)), which describes several attractive properties. The applicability of the proposed process is evaluated by the daily counts of the COVID-19 data sets that indicate the superiority of the BDPLINAR(1) model among some competitor models. The model adequacy checking using Pearson residuals indicates that the BDPLINAR(1) model is appropriate for modeling the COVID-19 data. Several forecasting approaches, such as the classic, mode, probability function, and modified Sieve Bootstrap methods, are considered for the COVID-19 data under the BDPLINAR(1) model. [ABSTRACT FROM AUTHOR]

Line transect survey methodology is a commonly used method for estimating the population abundance. Despite recent advances in this regard, parametric models are still widely used among biometricians, mainly because of their simplicity. In this paper, a new two-parameter detection model satisfying the shoulder conditions is proposed for modeling line transect data. We discuss its properties of interest, including the shapes of the model and the corresponding probability density function, moments, and the related sub-detection model. Maximum likelihood estimation of the parameters is considered. Subsequently, an application is carried out to the proposed model based on a practical data set of perpendicular distances. It is compared with some classical and recent models based on the evaluation of some goodness-of-fit statistics. As results, the variance-covariance matrix, confidence intervals of the parameters and estimated population abundance of the data set are obtained under the proposed detection model. [ABSTRACT FROM AUTHOR]

This paper deals with a new two-parameter lifetime distribution with increasing, decreasing and constant hazard rate. This distribution allows the occurrence of zero values and involves the exponential, linear exponential and other combinations of Weibull distributions as submodels. Many statistical properties of the distribution are derived. Maximum likelihood estimation of the parameters and a bias corrective approach is investigated with a simulation study for performance of the estimators. Four real data sets are analyzed for illustrative purposes and it is noted that the distribution is a highly alternative to the gamma, Weibull, Lognormal and exponentiated exponential distributions. [ABSTRACT FROM AUTHOR]

In this paper, the Bayes estimation procedure for the parameters and survival characteristics (survival and hazard functions) of the Hjorth distribution has been proposed with progressively type-II censored data. The Bayes estimators are derived with gamma prior and evaluated under squared error loss function. It is known that the censored observations create the complexity in Bayes estimation procedures. Therefore, two approximation techniques, namely Tierney–Kadane approximation method and Markov Chain Monte Carlo method have been used to compute the approximate Bayes estimators. The proposed estimates are compared with the usual maximum likelihood estimators through Monte Carlo simulations. Lastly, a medical data set has been considered to show the applicability of the proposed model as well study in real life scenario. [ABSTRACT FROM AUTHOR]

This paper describes the point and interval estimation of the unknown parameters of Kumaraswamy (Ku) distribution under the adaptive Type-II hybrid progressive censored samples. First, we obtain the maximum likelihood estimation (MLE) of the parameters using Newton-Raphson (NR) method, expectation maximization (EM) and stochastic EM (SEM) algorithms. In addition, we derive the asymptotic distribution of the parameters and the asymptotic confidence intervals. Moreover, two bootstrap confidence intervals are achieved. Second, the Bayesian estimation of the parameters is approximated by using the Markov Chain Monte Carlo (MCMC) algorithm and Lindley's method due to the lack of explicit forms. Furthermore, the highest posterior density (HPD) credible intervals of the parameters are derived. Finally, the different proposed estimations have been compared by the simulation studies and a practical data set is analyzed to illustrative aims. [ABSTRACT FROM AUTHOR]

In this paper, we develop a new general class of skew distributions with flexibility properties on the tails. Moreover, such class can provide heavy and light tails. Some of its mathematical properties are studied, including the quantile function, the moments, the moment generating function and the mean of deviations. New skew distributions are derived and used to construct new models capturing asymmetry inherent to data. The estimation of the class parameters is investigated by the method of maximum likelihood and the performance of the estimators is assessed by a simulation study. Applications of the proposed distribution are explored for two climate data sets. The first data set concerns the annual heat wave index and the second data set involves temperature and precipitation measures from the meteorological station located at Schiphol, Netherlands. Data fitting results show that our models perform better than the competitors. [ABSTRACT FROM AUTHOR]

A new class of unimodal asymmetric distributions is introduced to the unit interval and these distributions are useful for modeling data of percentages, proportions and fractions. Therefore, we propose the unit-half-normal distribution as a contribution to the earlier path and investigate some of its mathematical properties. The maximum likelihood estimator is obtained with a comprehensive inference. This new class of distributions belongs to the exponential family, hence the uniformly minimum variance unbiased estimator of the distribution parameter is obtained. The distribution represents a power alternative to the unit interval distributions, namely the beta, Kumaraswamy and other recent ones. We investigate a small simulation study to analyze the behavior of the obtained estimators for different sample sizes. Moreover, we illustrate the goodness of fit of the proposed model for image data. Lastly, we describe a procedure of incorporating covariates into regression analysis of the proposed distribution. [ABSTRACT FROM AUTHOR]

In this paper, a new two-parameter discrete distribution is introduced. It belongs to the family of the weighted geometric distribution (GD), with the feature of using a particular trigonometric weight. This configuration adds an oscillating property to the former GD which can be helpful in analyzing the data with over-dispersion, as developed in this study. First, we present the basic statistical properties of the new distribution, including the cumulative distribution function, hazard rate function and moment generating function. Estimation of the related model parameters is investigated using the maximum likelihood method. A simulation study is performed to illustrate the convergence of the estimators. Applications to two practical datasets are given to show that the new model performs at least as well as some competitors. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new class of (probability) distributions, based on a cosine-sine transformation, obtained by compounding a baseline distribution with cosine and sine functions. Some of its properties are explored. A special focus is given to a particular cosine-sine transformation using the exponential distribution as baseline. Estimations of parameters of a particular cosine-sine exponential distribution are performed via the maximum likelihood estimation method. A simulation study investigates the performances of these estimates. Applications are given for four real data sets, showing a better fit in comparison to some existing distributions based on some goodness-of-fit tests. [ABSTRACT FROM AUTHOR]

This paper presents a new stationary integer-valued bilinear time series model of the first order by mixing the thinning and Pegram operators. Some statistical properties of the model are obtained, involving the conditional moments, autocovariance and spectral density function. Estimation of the model parameters is discussed using the Yule-Walker and conditional least squares methods with a simulation study for evaluating the performance of those estimators. Applicability of the process is investigated using a practical count data set with comparing the model to a competitive bilinear model using some marginal distributions of innovations. Issue of forecasting data is discussed under the proposed model. [ABSTRACT FROM AUTHOR]

Development and application of probability models in data analysis are of major importance for all sciences. Therefore, we introduce a new model called a power log-Dagum distribution defined on the entire real line. The model contains many new sub-models: power logistic, linear log-Dagum, linear logistic and log-Dagum distributions among them. Some properties of the model including three different estimation procedures are justified. The model exhibits various shapes for the density and hazard rate functions. Moreover, the estimation procedures are compared using simulation studies. Finally, the model with others are fitted to three data sets, and it shows a better fit than the compared distributions defined on the real line. [ABSTRACT FROM AUTHOR]

A new discrete distribution involving geometric and discrete Pareto as special cases is introduced. The distribution possesses many interesting properties like decreasing hazard rate, zero vertex uni-modality, over-dispersion, infinite divisibility and compound Poisson representation, which makes the proposed distribution well suited for count data modeling. Other issues including closure property under minima, comparison of its distribution tail with other distributions via actuarial indices are discussed. The method of proportion and maximum likelihood method are presented for parameter estimation. Finally the performance of the proposed distribution over other classical and newly proposed infinitely divisible distributions are discussed. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new three parameter lifetime distribution for modelling lifetime data. Among its interesting properties, it has the feature of possessing a periodic hazard rate function. Some of its mathematical properties are studied, including moments, moment generating function, mean deviations and order statistics. The estimation of its parameter is investigated using the maximum-likelihood estimation. A real-life data set is considered to illustrate the usefulness and the applicability of the proposed distribution. [ABSTRACT FROM AUTHOR]

A mixed integer-valued autoregressive model of order one, based on the binomial and the generalized binomial thinning operator is introduced. Geometric marginal distribution is considered. Properties of the model are analysed, unknown parameters are estimated and some numerical results of the estimates are obtained. Finally, model is applied on two real data sets and compared to some relevant models. [ABSTRACT FROM AUTHOR]

In this article, we propose an extension of the Maxwell distribution, so-called the extended Maxwell distribution. This extension is evolved by using the Maxwell-X family of distributions and Weibull distribution. We study its fundamental properties such as hazard rate, moments, generating functions, skewness, kurtosis, stochastic ordering, conditional moments and moment generating function, hazard rate, mean and variance of the (reversed) residual life, reliability curves, entropy, etc. In estimation viewpoint, the maximum likelihood estimation of the unknown parameters of the distribution and asymptotic confidence intervals are discussed. We also obtain expected Fisher’s information matrix as well as discuss the existence and uniqueness of the maximum likelihood estimators. The EMa distribution and other competing distributions are fitted to two real datasets and it is shown that the distribution is a good competitor to the compared distributions. [ABSTRACT FROM AUTHOR]

Developing statistical methods to model hydrologic events is always interesting for both statisticians and hydrologists, because of its importance in hydraulic structures design and water resource planning. Because of this, a flexible 3-parameter generalization of the exponential distribution is introduced based on the binomial exponential 2 (BE2) distribution [2]. The proposed distribution involving the exponential, gamma and BE2 distributions as submodels; and it exhibits decreasing, increasing and bathtub-shaped hazard rates, so it turns out to be quite flexible for analyzing non-negative real life data. Some statistical properties, parameters estimation and information matrix of the distribution are investigated. The proposed distribution, Gumbel, generalized Logistic and other distributions are utilized to model and fit two hydrologic data sets. The distribution is shown to be more appropriate to the data than the compared distributions using the selection criteria: average scaled absolute error, Akaike information criterion, Bayesian information criterion and Kolmogorov–Smirnov statistics. As a result, some hydrologic parameters of the data are obtained such as return level, conditional mean, mean deviation about the return level and therth moments of order statistics. [ABSTRACT FROM PUBLISHER]

The Poisson distribution is extended over the set of all integers. The motivation comes from the many reflected versions of the gamma distribution, the continuous analog of the Poisson distribution, defined over the entire real line. Various mathematical properties of the extended Poisson distribution are derived. Estimation procedures by the methods of moments and maximum likelihood are also derived with their performance assessed by simulation. Finally, a real data application is illustrated. [ABSTRACT FROM AUTHOR]

A new stationary first-order autoregressive process with Lindley marginal distribution, denoted as LAR(1) is introduced. We derive the probability function for the innovation process. We consider many properties of this process, involving spectral density, some multi-step ahead conditional measures, run probabilities, stationary solution, uniqueness and ergodicity. We estimate the unknown parameters of the process using three methods of estimation and investigate properties of the estimators with some numerical results to illustrate them. Some applications of the process are discussed to two real data sets and it is shown that the LAR(1) model fits better than other known non Gaussian AR(1) models. [ABSTRACT FROM PUBLISHER]

A new one-parameter discrete distribution is introduced. Its mathematical properties and estimation procedures are derived. Four real data sets are used to show that the new model performs at least as well as the traditional one-parameter discrete models and other newly proposed two-parameter discrete models. [ABSTRACT FROM PUBLISHER]