Class Of Bivariate Distributions Research Articles

Bivariate Kumaraswamy Distribution Based on Conditional Hazard Functions: Properties and Application

A new class of bivariate distributions is deduced by specifying its conditional hazard functions (hfs) which are Kumaraswamy distribution. The interest of this model is positively, negatively, or zero correlated. Properties and local measures of dependence of the bivariate Kumaraswamy conditional hazard (BKCH) distribution are studied. The estimation of type parameters is considered by used the maximum likelihood and pseudolikelihood of the new class. A simulation study was performed to inspect the bias and mean squared error of the maximum likelihood estimators. Finally, an application is obtained to clarify our results with the maximum likelihood and pseudolikelihood. Also, the results are used to compare BKCH distribution with bivariate exponential conditionals (BEC) and bivariate Lindley conditionals hazard (BLCH) distributions.

On positivity of orthogonal series and its applications in probability

We give necessary and sufficient conditions for an orthogonal series to converge in the mean-squares to a nonnegative function. We present many examples and applications, in analysis and probability. In particular, we give necessary and sufficient conditions for a Lancaster-type of expansion sum _{nge 0}c_{n}alpha _{n}(x)beta _{n}(y) with two sets of orthogonal polynomials left{ alpha _{n}right} and left{ beta _{n}right} to converge in means-squares to a nonnegative bivariate function. In particular, we study the properties of the set C(alpha ,beta ) of the sequences left{ c_{n}right} , for which the above-mentioned series converge to a nonnegative function and give conditions for the membership to it. Further, we show that the class of bivariate distributions for which a Lancaster type expansion can be found, is the same as the class of distributions having all conditional moments in the form of polynomials in the conditioning random variable.

The Novel Bivariate Distribution: Statistical Properties and Real Data Applications

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.

A copula-based method of classifying individuals into binary disease categories using dependent biomarkers

Classification of a disease often depends on more than one test, and the tests can be interrelated. Under the incorrect assumption of independence, the test result using dependent biomarkers can lead to a conflicting disease classification. We develop a copula-based method for this purpose that takes dependency into account and leads to a unique decision. We first construct the joint probability distribution of the biomarkers considering Frank’s, Clayton’s and Gumbel’s copulas. We then develop the classification method and perform a comprehensive simulation. Using simulated data sets, we study the statistical properties of joint probability distributions and determine the joint threshold with maximum classification accuracy. Our simulation study results show that parameter estimates for the copula-based bivariate distributions are not biased. We observe that the thresholds for disease classification converge to a stationary distribution across different choices of copulas. We also observe that the classification accuracy decreases with the increasing value of the dependence parameter of the copulas. Finally, we illustrate our method with a real data example, where we identify the joint threshold of Apolipoprotein B to Apolipoprotein A1 ratio and total cholesterol to high-density lipoprotein ratio for the classification of myocardial infarction. We conclude, the copula-based method works well in identifying the joint threshold of two dependent biomarkers for an outcome classification. Our method is flexible and allows modeling broad classes of bivariate distributions that take dependency into account. The threshold may allow clinicians to classify uniquely individuals at risk of developing the disease and plan for early intervention.

From Symmetry to Asymmetry on the Disc Manifold: Modeling of Marion Island Data

The joint modeling of angular and linear observations is crucial as data of this nature are prevalent in multiple disciplines, for example the joint modeling of wind direction and another climatological variable such as wind speed or air temperature, the direction an animal moves and the distance moved, or wave direction and wave height. Hence, there is a need for developing flexible distributions on the hyper-disc, which has support of the interior of the hyper-sphere, as it allows for modeling the combination of angular and linear observations. This paper addresses this need by developing flexible distributions for the disc that have the ability to capture any inherent bimodality present in the data. A new class of bivariate distributions is proposed which has support on the unit disc in two dimensions that includes, as a special case, the existing Möbius distribution on the disc. This class is obtained by expressing the density function in a general form using a measurable function termed as generator. Special cases of this generator are considered to demonstrate the flexibility. By applying a conformal mapping to the generator function a new Möbius distribution class emanates. This class of bivariate distributions on the disc is the first to account for bimodality and skewness present in the data. The flexible behavior of the proposed models in terms of bimodality and skewness is graphically demonstrated. Preliminary evidential analysis of the wind data observed at Marion Island reveals the absence of unimodality in the data. The fit of the proposed models, which account for bimodality, to the Marion Island wind data were evaluated analytically and visually.

On Bivariate Generalized Exponential-Power Series Class of Distributions

In this paper, we introduce a new class of bivariate distributions by compounding the bivariate generalized exponential and power-series distributions. This new class contains some new sub-models such as the bivariate generalized exponential distribution, the bivariate generalized exponential-poisson, -logarithmic, -binomial and -negative binomial distributions. We derive different properties of the new class of distributions. The EM algorithm is used to determine the maximum likelihood estimates of the parameters. We illustrate the usefulness of the new distributions by means of an application to a real data set.

Correlated endpoints: simulation, modeling, and extreme correlations

Modeling and simulation of correlated random variables are important for evaluating operating characteristics of experimental designs in various applications, of which clinical trials with multiple endpoints provide an important example. There exist efficient algorithms to address the problem of generating multivariate distributions with given marginals and correlation structure. For model fitting as well as for simulation, it is important to know the feasible range of pairwise correlations, which can be much narrower than the interval $$[-\,1,+\,1]$$. We provide closed-form expressions for extreme correlations for several classes of bivariate distributions that involve both discrete and continuous endpoints, as well as an algorithm for the construction of such distributions in the discrete case.

On Bivariate Generalized Linear Failure Rate-Power Series Class of Distributions

Recently, it has been observed that the bivariate generalized linear failure rate distribution can be used quite effectively to analyze lifetime data in two dimensions. This paper introduces a more general class of bivariate distributions. We refer to this new class of distributions as bivariate generalized linear failure rate-power series model. This new class of bivariate distributions contains several lifetime models such as: generalized linear failure rate-power series, bivariate generalized linear failure rate, and bivariate generalized linear failure rate geometric distributions as special cases among others. The construction and characteristics of the proposed bivariate distribution are presented along with estimation procedures for the model parameters based on maximum likelihood. The marginal and conditional laws are also studied. We present an application to the real data set, where our model provides a better fit than other models.

Archimedean-based Marshall-Olkin Distributions and Related Dependence Structures

A new class of bivariate distributions is introduced that extends the Generalized Marshall-Olkin distributions of Li and Pellerey (2011). Their dependence structure is studied through the analysis of the copula functions that they induce. These copulas, that include as special cases the Generalized Marshall-Olkin copulas and the Scale Mixture of Marshall-Olkin copulas (see Li, 2009),are obtained through suitable distortions of bivariate Archimedean copulas: this induces asymmetry, and the corresponding Kendall's tau as well as the tail dependence parameters are studied.

An Extension of the Freund's Bivariate Distribution to Model Load-Sharing Systems

SYNOPTIC ABSTRACTSeveral authors have considered the analysis of load-sharing parallel systems. The main characteoristics of such a two-component system is that after the failure of one component, the surviving component has to shoulder extra load, and hence, it is more likely to fail earlier than would be expected under the original model. In other cases, the failure of one component may release extra resources to the other component, thus delaying the system failure. Freund introduced a bivariate extension of the exponential distribution, which is applicable to two-component load-sharing systems. It is based on the assumption that the lifetime distributions of the components are exponential random variables before and after the change. In this article, we introduce a new class of bivariate distribution using the proportional hazard model. It is observed that the bivariate model proposed by Freund is a particular case of our model. We study different properties of the proposed model. Different statistical inferences have also been developed. We have considered four different special cases: when the base distributions are exponential, Weibull, linear failure rate, and Pareto III distributions. One data analysis has been performed for illustrative purposes. Finally, we propose some generalizations.

Construction of two new general classes of bivariate distributions based on stochastic orders

In this paper, based on the concepts of stochastic orders, we propose two new general classes of bivariate distributions. The usual stochastic order and likelihood ratio order are applied to construct the classes. The joint distributions in each class are derived. It will be seen that the obtained formulas for the joint distributions are very simple and easy to apply. Then, the relationships between the classes are discussed and characterized. We illustrate the practical usefulness of the proposed classes by showing that a number of new families of bivariate distributions can be generated from the classes. Furthermore, to illustrate practical relevance, we apply several developed models to analyze a real bivariate failure time data set.

Baker- Lin-Huang Type Bivariate Distributions Based on Order Statistics

Baker (2008) introduced a new class of bivariate distributions based on distributions of order statistics from two independent samples of size n. Lin and Huang (2010) discovered an important property of Baker’s distribution and showed that the Pearson’s correlation coefficient for this distribution converges to maximum attainable value, i.e., the correlation coefficient of the Fréchet upper bound, as n increases to infinity. Bairamov and Bayramoglu (2013) investigated a new class of bivariate distributions constructed by using Baker’s model and distributions of order statistics from dependent random variables, allowing higher correlation than that of Baker’s distribution. In this article, a new class of Baker’s type bivariate distributions with high correlation are constructed based on distributions of order statistics by using an arbitrary continuous copula instead of the product copula.

On Marshall–Olkin type distribution with effect of shock magnitude

In classical Marshall–Olkin type shock models and their modifications a system of two or more components is subjected to shocks that arrive from different sources at random times and destroy the components of the system. With a distinctive approach to the Marshall–Olkin type shock model, we assume that if the magnitude of the shock exceeds some predefined threshold, then the component, which is subjected to this shock, is destroyed; otherwise it survives. More precisely, we assume that the shock time and the magnitude of the shock are dependent random variables with given bivariate distribution. This approach allows to meet requirements of many real life applications of shock models, where the magnitude of shocks is an important factor that should be taken into account. A new class of bivariate distributions, obtained in this work, involve the joint distributions of shock times and their magnitudes. Dependence properties of new bivariate distributions have been studied. For different examples of underlying bivariate distributions of lifetimes and shock magnitudes, the joint distributions of lifetimes of the components are investigated. The multivariate extension of the proposed model is also discussed.

On construction of general classes of bivariate distributions

In this study, taking into account the physics of failure (death) and the interrelationship between the items involved, we propose a general methodology for constructing new ‘classes of bivariate distributions’. The approach is based on the stochastic modeling of the residual lifetime of an item after the failure of the other item. We derive the joint and the corresponding marginal distributions in a class constructed from the modeling of conditional failure rate. It is shown that the proposed new class includes several well-known bivariate distributions as special bivariate models. As illustrated, a number of new families of bivariate distributions are generated from the new class proposed in this paper. Furthermore, we briefly discuss the relationship to Freund’s bivariate exponential distribution.

Dependence properties of bivariate distributions with proportional (reversed) hazards marginals

This paper considers two classes of bivariate distributions having proportional (reversed) hazard rates models as their marginals. Various dependence properties of the proposed models are studied through their copulas.

Improving series and parallel systems through mixtures of duplicated dependent components

We discuss suitable conditions such that the lifetime of a series or of a parallel system formed by two components having nonindependent lifetimes may be stochastically improved by replacing the lifetimes of each of the components by an independent mixture of the individual components' lifetimes. We also characterize the classes of bivariate distributions where this phenomenon arises through a new weak dependence notion. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011

A multivariate extension of Sarhan and Balakrishnan’s bivariate distribution and its ageing and dependence properties

A new class of bivariate distributions (NBD) was recently introduced by Sarhan and Balakrishnan [A.M. Sarhan, N. Balakrishnan, A new class of bivariate distributions and its mixture, J. Multivariate Anal. 98 (2007) 1508–1527]. In this note, we give the joint survival function of a multivariate extension of the NBD, which is not an absolutely continuous multivariate distribution, and its marginal and extreme order statistics distributions are also derived. The multivariate ageing and dependence properties of the proposed n -dimensional distribution are also discussed, and then we analyze the stochastic ageing of its marginals and its minimum and maximum order statistics.

Modified Sarhan–Balakrishnan singular bivariate distribution

Recently Sarhan and Balakrishnan [2007. A new class of bivariate distribution and its mixture. Journal of Multivariate Analysis 98, 1508–1527] introduced a new bivariate distribution using generalized exponential and exponential distributions. They discussed several interesting properties of this new distribution. Unfortunately, they did not discuss any estimation procedure of the unknown parameters. In this paper using the similar idea as of Sarhan and Balakrishnan [2007. A new class of bivariate distribution and its mixture. Journal of Multivariate Analysis 98, 1508–1527], we have proposed a singular bivariate distribution, which has an extra shape parameter. It is observed that the marginal distributions of the proposed bivariate distribution are more flexible than the corresponding marginal distributions of the Marshall–Olkin bivariate exponential distribution, Sarhan–Balakrishnan's bivariate distribution or the bivariate generalized exponential distribution. Different properties of this new distribution have been discussed. We provide the maximum likelihood estimators of the unknown parameters using EM algorithm. We reported some simulation results and performed two data analysis for illustrative purposes. Finally we propose some generalizations of this bivariate model.

Bivariate odds ratio and association measures

In this paper, we consider a class of bivariate distributions by forming the odds of failure of a two component system. The properties of this odds function and the association between the two variables are investigated by studying the local dependence function and the association measure defined by Clayton (Biometrika 65:141–151, 1978) and Oakes (J Am Stat Assoc 84:487–493, 1989). We also study the effect of the association parameter on the failure rate of a series system and the regression mean residual life function of a parallel system. Some stochastic comparisons with respect to the association parameter are also studied.

Form-invariance under weighted sampling

In this paper, form-invariant weighted distributions are considered in an exponential family. The class of bivariate distribution with invariant property is identified under exponential weight function. The class includes some of the custom bivariate models. The form-invariant multivariate normal distributions are obtained under a quadratic weight function.