scholarly journals Discrete Pseudo Lindley Distribution: Properties, Estimation and Application on INAR(1) Process

2021 ◽  
Vol 26 (4) ◽  
pp. 76
Author(s):  
Muhammed Rasheed Irshad ◽  
Christophe Chesneau ◽  
Veena D’cruz ◽  
Radhakumari Maya
Keyword(s):  
Generating Function ◽  
Simulation Study ◽  
Probability Generating Function ◽  
Autoregressive Process ◽  
Discrete Version ◽  
Unknown Parameters ◽  
Lindley Distribution ◽  
First Order ◽  
Mathematical Properties

In this paper, we introduce a discrete version of the Pseudo Lindley (PsL) distribution, namely, the discrete Pseudo Lindley (DPsL) distribution, and systematically study its mathematical properties. Explicit forms gathered for the properties such as the probability generating function, moments, skewness, kurtosis and stress–strength reliability made the distribution favourable. Two different methods are considered for the estimation of unknown parameters and, hence, compared with a broad simulation study. The practicality of the proposed distribution is illustrated in the first-order integer-valued autoregressive process. Its empirical importance is proved through three real datasets.

scholarly journals New class of Lindley distributions: properties and applications

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Duha Hamed ◽  
Ahmad Alzaghal
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Simulation Study ◽  
Likelihood Estimation ◽  
Statistical Properties ◽  
Data Sets ◽  
Lifetime Data ◽  
Unknown Parameters ◽  
Lindley Distribution ◽  
New Class

AbstractA new generalized class of Lindley distribution is introduced in this paper. This new class is called the T-Lindley{Y} class of distributions, and it is generated by using the quantile functions of uniform, exponential, Weibull, log-logistic, logistic and Cauchy distributions. The statistical properties including the modes, moments and Shannon’s entropy are discussed. Three new generalized Lindley distributions are investigated in more details. For estimating the unknown parameters, the maximum likelihood estimation has been used and a simulation study was carried out. Lastly, the usefulness of this new proposed class in fitting lifetime data is illustrated using four different data sets. In the application section, the strength of members of the T-Lindley{Y} class in modeling both unimodal as well as bimodal data sets is presented. A member of the T-Lindley{Y} class of distributions outperformed other known distributions in modeling unimodal and bimodal lifetime data sets.

scholarly journals A New Family of Discrete Distributions with Mathematical Properties, Characterizations, Bayesian and Non-Bayesian Estimation Methods

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1648
Author(s):  
Mohamed Aboraya ◽  
Haitham M. Yousof ◽  
G.G. Hamedani ◽  
Mohamed Ibrahim
Keyword(s):  
Bayesian Estimation ◽  
Generating Function ◽  
Bayesian Methods ◽  
Probability Generating Function ◽  
Real Data ◽  
Discrete Distributions ◽  
Estimation Methods ◽  
Squared Error Loss Function ◽  
New Family ◽  
Mathematical Properties

In this work, we propose and study a new family of discrete distributions. Many useful mathematical properties, such as ordinary moments, moment generating function, cumulant generating function, probability generating function, central moment, and dispersion index are derived. Some special discrete versions are presented. A certain special case is discussed graphically and numerically. The hazard rate function of the new class can be “decreasing”, “upside down”, “increasing”, and “decreasing-constant-increasing (U-shape)”. Some useful characterization results based on the conditional expectation of certain function of the random variable and in terms of the hazard function are derived and presented. Bayesian and non-Bayesian methods of estimation are considered. The Bayesian estimation procedure under the squared error loss function is discussed. Markov chain Monte Carlo simulation studies for comparing non-Bayesian and Bayesian estimations are performed using the Gibbs sampler and Metropolis–Hastings algorithm. Four applications to real data sets are employed for comparing the Bayesian and non-Bayesian methods. The importance and flexibility of the new discrete class is illustrated by means of four real data applications.

scholarly journals Quartile Method Estimation of Two-Parameter Exponential Distribution Data with Outliers

2016 ◽  
Vol 5 (5) ◽  
pp. 12
Author(s):  
Entisar A. Elgmati ◽  
Nadia B. Gregni
Keyword(s):  
Maximum Likelihood ◽  
Exponential Distribution ◽  
Simulation Study ◽  
Maximum Likelihood Method ◽  
Likelihood Method ◽  
Distribution Data ◽  
Unknown Parameters ◽  
First Order ◽  
Minimum Statistics ◽  
Two Parameter

Several methods have been used to estimate the unknown parameters in the two-parameter exponential distribution. Here we have considered two of these methods, maximum likelihood method and median-first order statistics method. However, in the presence of outliers these methods are not valid. In this paper we propose two approaches that deal with this situation. The idea is based on using first and third quartile instead of the minimum statistics. We investigated the parameters estimate using these methods through simulation study. The new method gives similar results under the normal situation and much better results when the data has outliers.

scholarly journals Discreteweighted exponential distribution: Properties and applications

Filomat ◽  
2018 ◽  
Vol 32 (8) ◽  
pp. 3043-3056 ◽  
Author(s):  
Mahdi Rasekhi ◽  
Omid Chatrabgoun ◽  
Alireza Daneshkhah
Keyword(s):  
Exponential Distribution ◽  
Generating Function ◽  
Probability Generating Function ◽  
Moment Generating Function ◽  
Strength Parameter ◽  
Discrete Version ◽  
Lifetime Distributions ◽  
Reliability Indices ◽  
Rate Functions ◽  
Maximum Likelihood Estimations

In this paper, we propose a new lifetime model as a discrete version of the continuous weighted exponential distribution which is called discrete weighted exponential distribution (DWED). This model is a generalization of the discrete exponential distribution which is originally introduced by Chakraborty (2015). We present various statistical indices/properties of this distribution including reliability indices, moment generating function, probability generating function, survival and hazard rate functions, index of dispersion, and stress-strength parameter. We first present a numerical method to compute the maximum likelihood estimations (MLEs) of the models parameters, and then conduct a simulation study to further analyze these estimations. The advantages of the DWED are shown in practice by applying it on two real world applications and compare it with some other well-known lifetime distributions.

Estimation theory for growth and immigration rates in a multiplicative process

1972 ◽  
Vol 9 (02) ◽  
pp. 235-256 ◽  
Author(s):  
C. C. Heyde ◽  
E. Seneta
Keyword(s):  
Basic Assumption ◽  
Probability Generating Function ◽  
Autoregressive Process ◽  
Estimation Theory ◽  
Strong Law ◽  
First Order ◽  
Watson Process ◽  
Branching Process With Immigration ◽  
Historical Antecedents ◽  
Strongly Consistent

This paper deals with the simple Galton-Watson process with immigration, {Xn } with offspring probability generating function (p.g.f.) F(s) and immigration p.g.f. B(s), under the basic assumption that the process is subcritical (0 < m ≡ F'(1–) < 1), and that 0 < λ ≡ B'(1–) < ∞, 0 < B(0) < 1, together with various other moment assumptions as needed. Estimation theory for the rates m and λ on the basis of a single terminated realization of the process {Xn } is developed, in that (strongly) consistent estimators for both m and λ are obtained, together with associated central limit theorems in relation to m and μ ≡ λ(1–m)–1 Following this, historical antecedents are analysed, and some examples of application of the estimation theory are discussed, with particular reference to the continuous-time branching process with immigration. The paper also contains a strong law for martingales; and discusses relation of the above theory to that of a first order autoregressive process.

Estimation theory for growth and immigration rates in a multiplicative process

1972 ◽  
Vol 9 (2) ◽  
pp. 235-256 ◽  
Author(s):  
C. C. Heyde ◽  
E. Seneta
Keyword(s):  
Basic Assumption ◽  
Probability Generating Function ◽  
Autoregressive Process ◽  
Estimation Theory ◽  
Strong Law ◽  
First Order ◽  
Watson Process ◽  
Branching Process With Immigration ◽  
Historical Antecedents ◽  
Strongly Consistent

This paper deals with the simple Galton-Watson process with immigration, {Xn} with offspring probability generating function (p.g.f.) F(s) and immigration p.g.f. B(s), under the basic assumption that the process is subcritical (0 < m ≡ F'(1–) < 1), and that 0 < λ ≡ B'(1–) < ∞, 0 < B(0) < 1, together with various other moment assumptions as needed. Estimation theory for the rates m and λ on the basis of a single terminated realization of the process {Xn} is developed, in that (strongly) consistent estimators for both m and λ are obtained, together with associated central limit theorems in relation to m and μ ≡ λ(1–m)–1 Following this, historical antecedents are analysed, and some examples of application of the estimation theory are discussed, with particular reference to the continuous-time branching process with immigration. The paper also contains a strong law for martingales; and discusses relation of the above theory to that of a first order autoregressive process.

On the absorption probabilities and mean time for absorption for discrete Markov chains

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nikolaos Halidias
Keyword(s):  
Markov Chain ◽  
Random Walk ◽  
Markov Chains ◽  
Generating Function ◽  
Discrete Time ◽  
Probability Generating Function ◽  
The Mean ◽  
Mean Time ◽  
Absorption Probabilities

Abstract In this note we study the probability and the mean time for absorption for discrete time Markov chains. In particular, we are interested in estimating the mean time for absorption when absorption is not certain and connect it with some other known results. Computing a suitable probability generating function, we are able to estimate the mean time for absorption when absorption is not certain giving some applications concerning the random walk. Furthermore, we investigate the probability for a Markov chain to reach a set A before reach B generalizing this result for a sequence of sets A 1 , A 2 , … , A k {A_{1},A_{2},\dots,A_{k}} .

scholarly journals Period-Life of a Branching Process with Migration and Continuous Time

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 868
Author(s):  
Khrystyna Prysyazhnyk ◽  
Iryna Bazylevych ◽  
Ludmila Mitkova ◽  
Iryna Ivanochko
Keyword(s):  
Random Process ◽  
Generating Function ◽  
Continuous Time ◽  
Branching Process ◽  
Probability Generating Function ◽  
Time Interval ◽  
The Moment ◽  
First Time ◽  
Critical Branching Process ◽  
Number Of Particles

The homogeneous branching process with migration and continuous time is considered. We investigated the distribution of the period-life τ, i.e., the length of the time interval between the moment when the process is initiated by a positive number of particles and the moment when there are no individuals in the population for the first time. The probability generating function of the random process, which describes the behavior of the process within the period-life, was obtained. The boundary theorem for the period-life of the subcritical or critical branching process with migration was found.

Sign in / Sign up

Export Citation Format

Share Document