Pearson Differential Equation Research Articles

Comparison of the alternative parameter estimators of Pearson distributions by robustness criteria

Pearson's differential equation is used for fitting a distribution to a data set. The differential equation has some alternative moment-based estimators (depending on the transformation to data). The estimator used when no transformation is made on the data set has 4 elements, and the estimators that require any transformation have 3 elements. We describe all elements of the estimators by corresponding vectors. One of the factors affecting the preference of an estimator is robustness. We use covariance matrix, bias, relative efficiency and influence function as our robustness criteria. Our aim is to compare the performance of the estimators of the differential equation for some specific distributions (namely Type I, Type IV, Type VI and Type III). 10,000 samples with specific sizes were selected with replacement. Also, we evaluated the performance of the estimators over real-life data. Considering the results, there is no best estimator in all criteria. Depending on the criterion to be based, the estimator to be preferred varies.

The use of the Pearson differential equation to test energetic distributions in space physics as Kappa distributions; implication for Tsallis nonextensive entropy: II

The use of the Pearson differential equation to test energetic distributions in space physics as Kappa distributions; implication for Tsallis nonextensive entropy: II

Energetic particle distributions in space physics represented by the Pearson differential equation for the kappa distribution

Energetic particle distributions in space physics represented by the Pearson differential equation for the kappa distribution

On the modified Burr IV model for hydrological events: development, properties, characterizations and applications

We introduce a new distribution for modeling extreme events about frequency analysis called modified Burr IV (MBIV) distribution. We derive the MBIV distribution on the basis of the generalized Pearson differential equation. The proposed model turns out to be flexible: its density function can be symmetrical, right-skewed, left-skewed, J and bimodal shaped. Its hazard rate has shapes such as bathtub and modified bathtub, increasing, decreasing, and increasing-decreasing-increasing. To show the importance of the MBIV distribution, we establish various mathematical properties such as random number generator, sub-models, moments related properties, inequality measures, reliability measures, uncertainty measures and characterizations. We utilize the maximum likelihood estimation technique to estimate the model parameters. We assess the behavior of the maximum likelihood estimators (MLEs) of the MBIV parameters via a simulation study. Five data sets related to frequency analysis are considered to elucidate the significance of the MBIV distribution. We show that the MBIV model is the best model to analyze data for hydrological events, motivating its high level of adaptability in the applied setting.

On modified Burr III-power distribution: Development, properties, characterizations, and applications

In this paper, a flexible distribution with increasing, bathtub and inverted bathtub hazard rate called Modified Burr III-Power (MBIII-Power) is developed on the basis of the generalized Pearson differential equation. The density function of MBIII-Power is arc, exponential and positively skewed shaped. Descriptive measures such as quantiles, moments, incomplete moments, inequality measures, residual life functions and reliability measures are theoretically established. The MBIII-Power distribution is characterized via different techniques. Parameters of MBIII-Power distribution are estimated using maximum likelihood method. The simulation study is performed to illustrate the performance of the maximum likelihood estimates (MLEs). Potential use of MBIII-Power distribution is demonstrated by its application to two data sets: serum-reversal time (in days) of children born from HIV-infected mothers and failure times of device data.

On Generalized Log Burr Xii Distribution

In this paper, we present flexible generalized log Burr XII (GLBXII) distribution developed on basis of generalized log Pearson differential equation. GLBXII distribution is also obtained from compounding mixture of distributions. Some structural and mathematical properties including moments, inequality measures, uncertainty measures and reliability measures are theoretically established. Characterizations of GLBXII distribution are studied through different techniques. Parameters of GLBXII distribution are estimated using maximum likelihood method. Goodness of fit of probability distribution through different methods is studied.

On Modified Extended Exponential Power Life Testing Distribution: Development, Properties, Characterizations and Application

In this paper, a flexible modified extended exponential power life testing (MEEPLT) distribution is proposed. The MEEPLT distribution has increasing, decreasing and bathtub hazard rate function. The MEEPLT density is arc, left skewed, right-skewed and symmetrical shaped. The MEEPLT distribution is developed on the basis of the generalized Pearson differential equation. Some structural and mathematical properties including descriptive measures on the basis of quantiles, moments, order statistics and reliability measures are theoretically established. Characterizations of MEEPLT distribution are also studied via different techniques. Parameters of the MEEPLT distribution are estimated using maximum likelihood method. The simulation study for performance of the MLEs of the MEEPLT distribution is carried out. Goodness of fit of this distribution through different methods is studied.

A New Family of Distributions Based on the Generalized Pearson Differential Equation with Some Applications

Recently, a generalization of the Pearson differential equation has appeared in the literature, from which a vast majority of continuous probability density functions (pdf’s) can be generated, known as the generalized Pearson system of continuous probability distributions. This paper derives a new family of distributions based on the generalized Pearson differential equation, which is a natural generalization of the generalized inverse Gaussian distribution. Some characteristics of the new distribution are obtained.Plots for the cumulative distribution function, pdf and hazard function, tableswith percentiles and with values of skewness and kurtosis are provided. Itis observed that the new distribution is skewed to the right and bears mostof the properties of skewed distributions. As a motivation, the statistical applicationsof the results to a problem of forestry have been provided. It is found that our newly proposed model fits better than gamma, log-normal and inverse Gaussian distributions. Since many researchers have studied the use of the generalized inverse Gaussian distributions in the fields of biomedicine, demography, environmental and ecological sciences, finance, lifetime data, reliability theory, traffic data, etc., we hope the findings of the paper will be useful for the practitioners in various fields of theoretical and applied sciences.

Complete solution of the electrostatic equilibrium problem for classical weights

In this paper we complete the solution of the electrostatic equilibrium problem for all classical weight functions. In particular we solve this problem for classical generalized Bessel, Jacobi on (0, +∞) and pseudo-Jacobi weights. In addition we present an elementary unified way of dealing with the electrostatic equilibrium problem, which depends on the weights satisfying the Pearson differential equation.

Inequalities of Chernoff type for finite and infinite sequences of classical orthogonal polynomials

In this paper we present two-sided estimates of Chernoff type for the weighted L w 2 L_{w}^{2} -distance of a smooth function to the k k -dimensional space of all polynomials of degree less than k k , whenever the weight function w w solves the Pearson differential equation and generates a finite or infinite sequence of classical orthogonal polynomials. These inequalities are simple corollaries of a unified general theorem, which is the main result of the paper.

A semiclassical perspective on multivariate orthogonal polynomials

Differential properties for orthogonal polynomials in several variables are studied. We consider multivariate orthogonal polynomials whose gradients satisfy some quasi-orthogonality conditions. We obtain several characterizations for these polynomials including the analogues of the semiclassical Pearson differential equation, the structure relation and a differential-difference equation.

Classical orthogonal polynomials in two variables: a matrix approach

Classical orthogonal polynomials in two variables are defined as the orthogonal polynomials associated to a two-variable moment functional satisfying a matrix analogue of the Pearson differential equation. Furthermore, we characterize classical orthogonal polynomials in two variables as the polynomial solutions of a matrix second order partial differential equation.

Weak classical orthogonal polynomials in two variables

Orthogonal polynomials in two variables constitute a very old subject in approximation theory. Usually they are studied as solutions of second-order partial differential equations. In this work, we study two-variable orthogonal polynomials associated with a moment functional satisfying the two-variable analogue of the Pearson differential equation. From this approach, we derive the extension of some of the usual characterizations of the classical orthogonal polynomials in one variable.

On a probability function useful in size modelling

A probability function has been derived as a solution to a generalized Pearson differential equation. Some statistical properties of the function are investigated. An interesting relationship between the present distribution and the inverse Gaussian distribution is stated. It is demonstrated that the function is more suitable than other probability densities in some applications of size models.

On a system of discreate distributions

Using the difference equation analogous to Pearson's differential equation, a system of discreate distributions is developed. This is found to contain many of the distributions in common usage. The choice of distribution usually depends on only the first three moments. Some examples are given.