scholarly journals A discrete Ramos-Louzada distribution for asymmetric and over-dispersed data with leptokurtic-shaped: Properties and various estimation techniques with inference

2022 ◽  
Vol 7 (2) ◽  
pp. 1726-1741
Author(s):  
Ahmed Sedky Eldeeb ◽  
◽  
Muhammad Ahsan-ul-Haq ◽  
Mohamed. S. Eliwa ◽  
◽  
...  
Keyword(s):  
Count Data ◽  
Discrete Distribution ◽  
Real Data ◽  
Mass Function ◽  
Probability Mass Function ◽  
Data Sets ◽  
Estimation Techniques ◽  
Proposed Model ◽  
Probability Mass ◽  
Von Mises

<abstract> <p>In this paper, a flexible probability mass function is proposed for modeling count data, especially, asymmetric, and over-dispersed observations. Some of its distributional properties are investigated. It is found that all its statistical and reliability properties can be expressed in explicit forms which makes the proposed model useful in time series and regression analysis. Different estimation approaches including maximum likelihood, moments, least squares, Andersonӳ-Darling, Cramer von-Mises, and maximum product of spacing estimator, are derived to get the best estimator for the real data. The estimation performance of these estimation techniques is assessed via a comprehensive simulation study. The flexibility of the new discrete distribution is assessed using four distinctive real data sets ԣoronavirus-flood peaks-forest fire-Leukemia? Finally, the new probabilistic model can serve as an alternative distribution to other competitive distributions available in the literature for modeling count data.</p> </abstract>

scholarly journals Geometric Regression for Modelling Count Data on the Time-to-First Antenatal Care Visit

2020 ◽  
Vol 23 (1) ◽  
pp. 35-57
Author(s):  
Zainab Mohammed Darwish Al-Balushi ◽  
◽  
M. Mazharul Islam ◽  
Keyword(s):  
Antenatal Care ◽  
Regression Model ◽  
Count Data ◽  
Negative Binomial ◽  
Geometric Distribution ◽  
Discrete Distribution ◽  
Mass Function ◽  
Probability Mass Function ◽  
Antenatal Care Visit ◽  
Care Visit

Geometric distribution belongs to the family of discrete distribution that deals with the count of trail needed for first occurrence or success of any event. However, little attention has been paid in applying the GLM for the geometric distribution, which has a very simple form for its probability mass function with a single parameter. In this study, an attempt has been made to introduce geometric regression for modelling the count data. We have illustrated the suitability of the geometric regression model for analyzing the count data on time to first antenatal care visit that displayed under-dispersion, and the results were compared with Poisson and negative binomial regressions. We conclude that the geometric regression model may provide a flexible model for fitting count data sets which may present over-dispersion or under-dispersion, and the model may serve as an alternative model to the very familiar Poisson and negative binomial models for modelling count data.

scholarly journals The Novel Bivariate Distribution: Statistical Properties and Real Data Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
B. I. Mohammed ◽  
Abdulaziz S. Alghamdi ◽  
Hassan M. Aljohani ◽  
Md. Moyazzem Hossain
Keyword(s):  
Negative Binomial ◽  
Real Data ◽  
Information Criterion ◽  
Mass Function ◽  
Bivariate Distribution ◽  
Statistical Properties ◽  
Model Parameters ◽  
Probability Mass Function ◽  
New Class ◽  
Probability Mass

This article proposes a novel class of bivariate distributions that are completely defined by stating their conditionals as Poisson exponential distributions. Numerous statistical properties of this distribution are also examined here, including the conditional probability mass function (PMF) and moments of the new class. The techniques of maximum likelihood and pseudolikelihood are used to estimate the model parameters. Additionally, the effectiveness of the bivariate Poisson exponential conditional (BPEC) distribution is compared to that of the bivariate Poisson conditional (BPC), the bivariate Poisson (BP), the bivariate Poisson–Lindley (BPL), and the bivariate negative binomial (BNB) distributions using a real-world dataset. The findings of Akaike information criterion (AIC) and Bayesian information criterion (BIC) reveal that the BPEC distribution performs better than the other distributions considered in this study. As a result, the authors claim that this distribution may be used to fit dependent and overspread count data.

scholarly journals A New Discrete Distribution Arising from a Generalised Random Game and Its Asymptotic Properties

2021 ◽  
pp. 11-20
Author(s):  
R. Frühwirth ◽  
R. Malina ◽  
W. Mitaroff
Keyword(s):  
Numerical Study ◽  
Asymptotic Properties ◽  
Discrete Distribution ◽  
Rayleigh Distribution ◽  
Mass Function ◽  
Cumulative Distribution ◽  
Probability Mass Function ◽  
Expected Gain ◽  
Probability Mass ◽  
Analytic Expression

The rules of a game of dice are extended to a ``hyper-die'' with \(n\in\mathbb{N}\) equally probable faces, numbered from 1 to \(n\). We derive recursive and explicit expressions for the probability mass function and the cumulative distribution function of the gain \(G_n\) for arbitrary values of \(n\). A numerical study suggests the conjecture that for \(n \to \infty\) the expectation of the scaled gain \(\mathbb{E}[{H_n}]=\mathbb{E} [{G_n/\sqrt{n}\,}]\) converges to \(\sqrt{\pi/\,2}\). The conjecture is proved by deriving an analytic expression of the expected gain \(\mathbb{E} [{G_n}]\). An analytic expression of the variance of the gain \(G_n\) is derived by a similar technique. Finally,  it is proved that \(H_n\) converges weakly to the Rayleigh distribution with scale parameter~1.

A new one-parameter discrete distribution with associated regression and integer-valued autoregressive models

2020 ◽  
Vol 70 (4) ◽  
pp. 979-994
Author(s):  
Emrah Altun
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Count Data ◽  
Discrete Distribution ◽  
Real Data ◽  
Autoregressive Process ◽  
Important Application ◽  
Statistical Properties ◽  
Data Sets ◽  
Application Fields

AbstractThis study introduces the Poisson-Bilal distribution and its associated two models for modeling the over-dispersed count data sets. The Poisson-Bilal distribution has tractable properties and explicit forms for its statistical properties. A new over-dispersed count regression model and integer-valued autoregressive process with flexible innovation distribution are defined and studied comprehensively. Two real data sets are analyzed to prove empirically the importance of proposed models. Empirical findings show that the Poisson-Bilal distribution has important application fields in time series and regression modeling.

scholarly journals Nonparametric estimation for probability mass function with Disake: an R package for discrete associated kernel estimators

2015 ◽  
Vol Volume 19 - 2015 - Special... ◽  
Author(s):  
W.E. Wansouwé ◽  
C.C. Kokonendji ◽  
D.T. Kolyang
Keyword(s):  
Kernel Smoothing ◽  
Simulated Data ◽  
Kernel Estimator ◽  
Real Data ◽  
R Package ◽  
Mass Function ◽  
Kernel Estimators ◽  
Probability Mass Function ◽  
Probability Mass ◽  
Discrete Associated Kernel

International audience Kernel smoothing is one of the most widely used nonparametric data smoothing techniques. We introduce a new R package, Disake, for computing discrete associated kernel estimators for probability mass function. When working with a kernel estimator, two choices must be made: the kernel function and the smoothing parameter. The Disake package focuses on discrete associated kernels and also on cross-validation and local Bayesian techniques to select the appropriate bandwidth. Applications on simulated data and real data show that the binomial kernel is appropriate for small or moderate count data while the empirical estimator or the discrete triangular kernel is indicated for large samples.

scholarly journals Statistical Inferences on Odd Fréchet Power Function Distribution

2021 ◽  
Author(s):  
Muhammad Ahsan ul Haq ◽  
Mohammed Albassam ◽  
Muhammad Aslam ◽  
Sharqa Hashmi
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Real Data ◽  
Estimation Methods ◽  
Data Sets ◽  
Statistical Inferences ◽  
Proposed Model ◽  
Power Function Distribution ◽  
Anderson Darling ◽  
Von Mises

This article introduces a new unit distribution namely odd Fréchet power (OFrPF) distribution. Numerous properties of the proposed model including reliability analysis, moments, and Rényi Entropy for the proposed distribution. The parameters of the OFrPF distribution are obtained using different approaches such as maximum likelihood, least squares, weighted least squares, percentile, Cramer-von Mises, Anderson-Darling. Furthermore, a simulation was performed to study the performance of the suggested model. We also perform a simulation study to analyze the performances of estimation methods derived. The applications are used to show the practicality of OFrPF distribution using two real data sets. OFrPF distribution performed better than other competitive models.

scholarly journals A New Skewed Discrete Model: Properties, Inference, and Applications

2021 ◽  
pp. 799-816
Author(s):  
Ahmed Z. Afify ◽  
Mahmoud Elmorshedy ◽  
M. S. Eliwa
Keyword(s):  
Biological Sciences ◽  
Residual Life ◽  
Risk Measures ◽  
Discrete Distribution ◽  
Real Data ◽  
Mass Function ◽  
Rate Function ◽  
Estimation Methods ◽  
Mean Residual Life ◽  
Probability Mass Function

In this paper, a new probability discrete distribution for analyzing over-dispersed count data encountered in biological sciences was proposed. The new discrete distribution, with one parameter, has a log-concave probability mass function and an increasing hazard rate function, for all choices of its parameter. Several properties of the proposed distribution including the mode, moments and index of dispersion, mean residual life, mean past life, order statistics and L- moment statistics have been established. Two actuarial or risk measures were derived. The numerical computations for these measures are conducted for several parametric values of the model parameter. The parameter of the introduced distribution is estimated using eight frequentist estimation methods. Detailed Monte Carlo simulations are conducted to explore the performance of the studied estimators. The performance of the proposed distribution has been examined by three over-dispersed real data sets from biological sciences.

An alternative distribution to Lindley and Power Lindley distributions with characterizations, different estimation methods and data applications

2020 ◽  
Vol 70 (4) ◽  
pp. 953-978
Author(s):  
Mustafa Ç. Korkmaz ◽  
G. G. Hamedani
Keyword(s):  
Hazard Function ◽  
Mixture Distribution ◽  
Real Data ◽  
Quantile Function ◽  
Estimation Methods ◽  
Data Sets ◽  
Unknown Parameters ◽  
Lorenz Curves ◽  
Proposed Model ◽  
New Distribution

AbstractThis paper proposes a new extended Lindley distribution, which has a more flexible density and hazard rate shapes than the Lindley and Power Lindley distributions, based on the mixture distribution structure in order to model with new distribution characteristics real data phenomena. Its some distributional properties such as the shapes, moments, quantile function, Bonferonni and Lorenz curves, mean deviations and order statistics have been obtained. Characterizations based on two truncated moments, conditional expectation as well as in terms of the hazard function are presented. Different estimation procedures have been employed to estimate the unknown parameters and their performances are compared via Monte Carlo simulations. The flexibility and importance of the proposed model are illustrated by two real data sets.

scholarly journals Transition models for count data: a flexible alternative to fixed distribution models

2021 ◽  
Author(s):  
Moritz Berger ◽  
Gerhard Tutz
Keyword(s):  
Count Data ◽  
Regression Models ◽  
Negative Binomial ◽  
Real Data ◽  
Distribution Models ◽  
Explanatory Variables ◽  
Excess Zeros ◽  
Proposed Model ◽  
Transition Models ◽  
Fixed Distribution

AbstractA flexible semiparametric class of models is introduced that offers an alternative to classical regression models for count data as the Poisson and Negative Binomial model, as well as to more general models accounting for excess zeros that are also based on fixed distributional assumptions. The model allows that the data itself determine the distribution of the response variable, but, in its basic form, uses a parametric term that specifies the effect of explanatory variables. In addition, an extended version is considered, in which the effects of covariates are specified nonparametrically. The proposed model and traditional models are compared in simulations and by utilizing several real data applications from the area of health and social science.

scholarly journals Modified Recursions for a Class of Compound Distributions

1996 ◽  
Vol 26 (2) ◽  
pp. 213-224 ◽  
Author(s):  
Karl-Heinz Waldmann
Keyword(s):  
Distribution Function ◽  
Cumulative Distribution Function ◽  
Mass Function ◽  
Cumulative Distribution ◽  
Probability Mass Function ◽  
Initial Value ◽  
Compound Distributions ◽  
Claim Frequency ◽  
Probability Mass ◽  
Stop Loss

AbstractRecursions are derived for a class of compound distributions having a claim frequency distribution of the well known (a,b)-type. The probability mass function on which the recursions are usually based is replaced by the distribution function in order to obtain increasing iterates. A monotone transformation is suggested to avoid an underflow in the initial stages of the iteration. The faster increase of the transformed iterates is diminished by use of a scaling function. Further, an adaptive weighting depending on the initial value and the increase of the iterates is derived. It enables us to manage an arbitrary large portfolio. Some numerical results are displayed demonstrating the efficiency of the different methods. The computation of the stop-loss premiums using these methods are indicated. Finally, related iteration schemes based on the cumulative distribution function are outlined.

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