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  • The unifed distribution
    J. Stat. Distrib. App. Pub Date : 2019-11-05
    Oscar Alberto Quijano Xacur

    We introduce a new distribution with support on (0,1) called unifed. It can be used as the response distribution for a GLM and it is suitable for data aggregation. We make a comparison to the beta regression. A link to an R package for working with the unifed is provided.

  • Failure time regression with continuous informative auxiliary covariates.
    J. Stat. Distrib. App. Pub Date : 2015-11-26
    Lipika Ghosh,Jiancheng Jiang,Yanqing Sun,Haibo Zhou

    In this paper we use Cox's regression model to fit failure time data with continuous informative auxiliary variables in the presence of a validation subsample. We first estimate the induced relative risk function by kernel smoothing based on the validation subsample, and then improve the estimation by utilizing the information on the incomplete observations from non-validation subsample and the auxiliary

  • Missing data approaches for probability regression models with missing outcomes with applications.
    J. Stat. Distrib. App. Pub Date : 2014-01-01
    Li Qi,Yanqing Sun

    In this paper, we investigate several well known approaches for missing data and their relationships for the parametric probability regression model Pβ (Y|X) when outcome of interest Y is subject to missingness. We explore the relationships between the mean score method, the inverse probability weighting (IPW) method and the augmented inverse probability weighted (AIPW) method with some interesting

  • On Burr III Marshal Olkin family: development, properties, characterizations and applications
    J. Stat. Distrib. App. Pub Date : 2019-08-23
    Fiaz Ahmad Bhatti; G. G. Hamedani; Mustafa C. Korkmaz; Gauss M. Cordeiro; Haitham M. Yousof; Munir Ahmad

    In this paper, a flexible family of distributions with unimodel, bimodal, increasing, increasing and decreasing, inverted bathtub and modified bathtub hazard rate called Burr III-Marshal Olkin-G (BIIIMO-G) family is developed on the basis of the T-X family technique. The density function of the BIIIMO-G family is arc, exponential, left- skewed, right-skewed and symmetrical shaped. Descriptive measures

  • The linearly decreasing stress Weibull (LDSWeibull): a new Weibull-like distribution
    J. Stat. Distrib. App. Pub Date : 2019-08-20
    Roger W. Barnard; Chamila Perera; James G. Surles; A. Alexandre Trindade

    Motivated by an engineering pullout test applied to a steel strip embedded in earth, we show how the resulting linearly decreasing force leads naturally to a new distribution, if the force under constant stress is modeled via a three-parameter Weibull. We term this the LDSWeibull distribution, and show that inference on the parameters of the underlying Weibull can be made upon collection of data from

  • Meta analysis of binary data with excessive zeros in two-arm trials
    J. Stat. Distrib. App. Pub Date : 2019-07-24
    Saman Muthukumarana; David Martell; Ram Tiwari

    We present a novel Bayesian approach to random effects meta analysis of binary data with excessive zeros in two-arm trials. We discuss the development of likelihood accounting for excessive zeros, the prior, and the posterior distributions of parameters of interest. Dirichlet process prior is used to account for the heterogeneity among studies. A zero inflated binomial model with excessive zero parameters

  • On (p1,…,pk)-spherical distributions
    J. Stat. Distrib. App. Pub Date : 2019-06-12
    Wolf-Dieter Richter

    The class of (p1,…,pk)-spherical probability laws and a method of simulating random vectors following such distributions are introduced using a new stochastic vector representation. A dynamic geometric disintegration method and a corresponding geometric measure representation are used for generalizing the classical χ2-, t- and F-distributions. Comparing the principles of specialization and marginalization

  • A new class of survival distribution for degradation processes subject to shocks
    J. Stat. Distrib. App. Pub Date : 2019-06-11
    Mei-Ling Ting Lee; G. A. Whitmore

    Many systems experience gradual degradation while simultaneously being exposed to a stream of random shocks of varying magnitudes that eventually cause failure when a shock exceeds the residual strength of the system. In this paper, we present a family of stochastic processes, called shock-degradation processes, that describe this failure mechanism. In our failure model, system strength follows a geometric

  • A new extended normal regression model: simulations and applications
    J. Stat. Distrib. App. Pub Date : 2019-06-08
    Maria C.S. Lima; Gauss M. Cordeiro; Edwin M.M. Ortega; Abraão D.C. Nascimento

    Various applications in natural science require models more accurate than well-known distributions. In this context, several generators of distributions have been recently proposed. We introduce a new four-parameter extended normal (EN) distribution, which can provide better fits than the skew-normal and beta normal distributions as proved empirically in two applications to real data. We present Monte

  • High-dimensional star-shaped distributions
    J. Stat. Distrib. App. Pub Date : 2019-06-06
    Wolf-Dieter Richter

    Stochastic representations of star-shaped distributed random vectors having heavy or light tail density generating function g are studied for increasing dimensions along with corresponding geometric measure representations. Intervals are considered where star radius variables take values with high probability, and the derivation of values of distribution functions of g-robust statistics is proved to

  • Multiclass analysis and prediction with network structured covariates
    J. Stat. Distrib. App. Pub Date : 2019-06-06
    Li-Pang Chen; Grace Y. Yi; Qihuang Zhang; Wenqing He

    Technological advances associated with data acquisition are leading to the production of complex structured data sets. The recent development on classification with multiclass responses makes it possible to incorporate the dependence structure of predictors. The available methods, however, are hindered by the restrictive requirements. Those methods basically assume a common network structure for predictors

  • A unified complex noncentral Wishart type distribution inspired by massive MIMO systems
    J. Stat. Distrib. App. Pub Date : 2019-04-15
    Johannes T. Ferreira; Andriëtte Bekker

    The eigenvalue distributions from a complex noncentral Wishart matrix S=XHX has been the subject of interest in various real world applications, where X is assumed to be complex matrix variate normally distributed with nonzero mean M and covariance Σ. This paper focuses on a weighted analytical representation of S to alleviate the restriction of normality; thereby allowing the choice of X to be complex

  • Particle swarm based algorithms for finding locally and Bayesian D-optimal designs
    J. Stat. Distrib. App. Pub Date : 2019-04-08
    Yu Shi; Zizhao Zhang; Weng Kee Wong

    When a model-based approach is appropriate, an optimal design can guide how to collect data judiciously for making reliable inference at minimal cost. However, finding optimal designs for a statistical model with several possibly interacting factors can be both theoretically and computationally challenging, and this issue is rarely discussed in the literature. We propose nature-inspired metaheuristic

  • Admissible Bernoulli correlations
    J. Stat. Distrib. App. Pub Date : 2019-03-08
    Mark Huber; Nevena Marić

    A multivariate symmetric Bernoulli distribution has marginals that are uniform over the pair {0,1}. Consider the problem of sampling from this distribution given a prescribed correlation between each pair of variables. Not all correlation structures can be attained. Here we completely characterize the admissible correlation vectors as those given by convex combinations of simpler distributions. This

  • On p-generalized elliptical random processes
    J. Stat. Distrib. App. Pub Date : 2019-03-07
    Klaus Müller; Wolf-Dieter Richter

    We introduce rank-k-continuous axis-aligned p-generalized elliptically contoured distributions and study their properties such as stochastic representations, moments, and density-like representations. Applying the Kolmogorov existence theorem, we prove the existence of random processes having axis-aligned p-generalized elliptically contoured finite dimensional distributions with arbitrary location

  • Parameters of stochastic models for electroencephalogram data as biomarkers for child's neurodevelopment after cerebral malaria.
    J. Stat. Distrib. App. Pub Date : 2018-12-29
    Maria A Veretennikova,Alla Sikorskii,Michael J Boivin

    The objective of this study was to test statistical features from the electroencephalogram (EEG) recordings as predictors of neurodevelopment and cognition of Ugandan children after coma due to cerebral malaria. The increments of the frequency bands of EEG time series were modeled as Student processes; the parameters of these Student processes were estimated and used along with clinical and demographic

  • A new generalization of generalized half-normal distribution: properties and regression models
    J. Stat. Distrib. App. Pub Date : 2018-12-05
    Emrah Altun; Haitham M. Yousof; G.G. Hamedani

    In this paper, a new extension of the generalized half-normal distribution is introduced and studied. We assess the performance of the maximum likelihood estimators of the parameters of the new distribution via simulation study. The flexibility of the new model is illustrated by means of four real data sets. A new log-location regression model based on the new distribution is also introduced and studied

  • Analytical properties of generalized Gaussian distributions
    J. Stat. Distrib. App. Pub Date : 2018-12-04
    Alex Dytso; Ronit Bustin; H. Vincent Poor; Shlomo Shamai

    The family of Generalized Gaussian (GG) distributions has received considerable attention from the engineering community, due to the flexible parametric form of its probability density function, in modeling many physical phenomena. However, very little is known about the analytical properties of this family of distributions, and the aim of this work is to fill this gap. Roughly, this work consists

  • A new Weibull-X family of distributions: properties, characterizations and applications
    J. Stat. Distrib. App. Pub Date : 2018-11-03
    Zubair Ahmad; M. Elgarhy; G. G. Hamedani

    We propose a new family of univariate distributions generated from the Weibull random variable, called a new Weibull-X family of distributions. Two special sub-models of the proposed family are presented and the shapes of density and hazard functions are investigated. General expressions for some statistical properties are discussed. For the new family, three useful characterizations based on truncated

  • The transmuted geometric-quadratic hazard rate distribution: development, properties, characterizations and applications
    J. Stat. Distrib. App. Pub Date : 2018-08-13
    Fiaz Ahmad Bhatti; G. G. Hamedani; Mustafa Ç. Korkmaz; Munir Ahmad

    We propose a five parameter transmuted geometric quadratic hazard rate (TG-QHR) distribution derived from mixture of quadratic hazard rate (QHR), geometric and transmuted distributions via the application of transmuted geometric-G (TG-G) family of Afify et al.(Pak J Statist 32(2), 139-160, 2016). Some of its structural properties are studied. Moments, incomplete moments, inequality measures, residual

  • A nonparametric approach for quantile regression.
    J. Stat. Distrib. App. Pub Date : 2018-07-18
    Mei Ling Huang,Christine Nguyen

    Quantile regression estimates conditional quantiles and has wide applications in the real world. Estimating high conditional quantiles is an important problem. The regular quantile regression (QR) method often designs a linear or non-linear model, then estimates the coefficients to obtain the estimated conditional quantiles. This approach may be restricted by the linear model setting. To overcome this

  • Mean and variance of ratios of proportions from categories of a multinomial distribution
    J. Stat. Distrib. App. Pub Date : 2018-01-18
    Frantisek Duris; Juraj Gazdarica; Iveta Gazdaricova; Lucia Strieskova; Jaroslav Budis; Jan Turna; Tomas Szemes

    Ratio distribution is a probability distribution representing the ratio of two random variables, each usually having a known distribution. Currently, there are results when the random variables in the ratio follow (not necessarily the same) Gaussian, Cauchy, binomial or uniform distributions. In this paper we consider a case, where the random variables in the ratio are joint binomial components of

  • The power-Cauchy negative-binomial: properties and regression
    J. Stat. Distrib. App. Pub Date : 2018-01-08
    Muhammad Zubair; Muhammad H. Tahir; Gauss M. Cordeiro; Ayman Alzaatreh; Edwin M. M. Ortega

    We propose and study a new compounded model to extend the half-Cauchy and power-Cauchy distributions, which offers more flexibility in modeling lifetime data. The proposed model is analytically tractable and can be used effectively to analyze censored and uncensored data sets. Its density function can have various shapes such as reversed-J and right-skewed. It can accommodate different hazard shapes

  • Families of distributions arising from the quantile of generalized lambda distribution
    J. Stat. Distrib. App. Pub Date : 2017-11-22
    Mahmoud Aldeni; Carl Lee; Felix Famoye

    In this paper, the class of T-R {generalized lambda} families of distributions based on the quantile of generalized lambda distribution has been proposed using the T-R{Y} framework. In the development of the T-R{Y} framework, the support of Y and T must be the same. It is typical that the random variable Y has one type of support and T is restricted to the same support. Taking Y to be a generalized

  • Risk ratios and Scanlan’s HRX
    J. Stat. Distrib. App. Pub Date : 2017-11-15
    Hoben Thomas; Thomas P. Hettmansperger

    Risk ratios are distribution function tail ratios and are widely used in health disparities research. Let A and D denote advantaged and disadvantaged populations with cdfs F A (x) and F D (x) respectively, F A (x)≤F D (x). Consider a selection setting where those selected have x>c a critical value. Scanlan observed in empirical data that as c is lowered the failure ratio F R(c)=F D (c)/F A (c) and

  • Joint distribution of k-tuple statistics in zero-one sequences of Markov-dependent trials
    J. Stat. Distrib. App. Pub Date : 2017-11-15
    Anastasios N. Arapis; Frosso S. Makri; Zaharias M. Psillakis

    We consider a sequence of n, n≥3, zero (0) - one (1) Markov-dependent trials. We focus on k-tuples of 1s; i.e. runs of 1s of length at least equal to a fixed integer number k, 1≤k≤n. The statistics denoting the number of k-tuples of 1s, the number of 1s in them and the distance between the first and the last k-tuple of 1s in the sequence, are defined. The work provides, in a closed form, the exact

  • Describing the Flexibility of the Generalized Gamma and Related Distributions
    J. Stat. Distrib. App. Pub Date : 2017-11-01
    Matthew Matheson; Alvaro Muñoz; Christopher Cox

    The generalized gamma (GG) distribution is a widely used, flexible tool for parametric survival analysis. Many alternatives and extensions to this family have been proposed. This paper characterizes the flexibility of the GG by the quartile ratio relationship, log(Q2/Q1)/log(Q3/Q2), and compares the GG on this basis with two other three-parameter distributions and four parent distributions of four

  • Quantile regression for overdispersed count data: a hierarchical method
    J. Stat. Distrib. App. Pub Date : 2017-11-01
    Peter Congdon

    Generalized Poisson regression is commonly applied to overdispersed count data, and focused on modelling the conditional mean of the response. However, conditional mean regression models may be sensitive to response outliers and provide no information on other conditional distribution features of the response. We consider instead a hierarchical approach to quantile regression of overdispersed count

  • A useful extension of the Burr III distribution
    J. Stat. Distrib. App. Pub Date : 2017-11-01
    Gauss M. Cordeiro; Antonio E. Gomes; Cibele Q. da-Silva; Edwin M. M. Ortega

    For any continuous baseline G distribution, Zografos and Balakrishnan (Statistical Methodology 6:344–362, 2009) introduced the gamma-generated family of distributions with an extra shape parameter. Based on this family, we define a new four-parameter extension of the Burr III distribution. It can have decreasing, unimodal and decreasing-increasing-decreasing hazard rate function. We provide a comprehensive

  • Analysis of case-control data with interacting misclassified covariates
    J. Stat. Distrib. App. Pub Date : 2017-10-30
    Grace Y. Yi; Wenqing He

    Case-control studies are important and useful methods for studying health outcomes and many methods have been developed for analyzing case-control data. Those methods, however, are vulnerable to mismeasurement of variables; biased results are often produced if such a feature is ignored. In this paper, we develop an inference method for handling case-control data with interacting misclassified covariates

  • Correction to: a flexible distribution class for count data
    J. Stat. Distrib. App. Pub Date : 2017-10-16
    Kimberly F. Sellers; Andrew W. Swift; Kimberly S. Weems

    Following publication of the original article (Sellers et al., 2017), the authors reported that the typesetters had misinterpreted some of the edits included in their proof corrections, namely instances of “sp” to denote that an extra space was required. The original article has been corrected. Sellers, K.F., Swift, A.W., Weems, K.S.: A flexible distribution class for count data. J. Stat. Distrib.

  • Erlang renewal models for genetic recombination
    J. Stat. Distrib. App. Pub Date : 2017-10-15
    John P. Nolan

    Erlang renewal models, also called chi-squared models, provide a tractable model for genetic recombination that exhibits positive interference. Closed form expressions for multilocus probabilities are derived for the crossover process when it is a renewal process with the distance between crossovers modeled by a Erlang distribution. These expressions yield explicit formulas for the map functions, coincidence

  • On Poisson–Tweedie mixtures
    J. Stat. Distrib. App. Pub Date : 2017-10-02
    Vladimir V. Vinogradov; Richard B. Paris

    Poisson-Tweedie mixtures are the Poisson mixtures for which the mixing measure is generated by those members of the family of Tweedie distributions whose support is non-negative. This class of non-negative integer-valued distributions is comprised of Neyman type A, back-shifted negative binomial, compound Poisson-negative binomial, discrete stable and exponentially tilted discrete stable laws. For

  • A permutation test for comparing rotational symmetry in three-dimensional rotation data sets
    J. Stat. Distrib. App. Pub Date : 2017-09-29
    Melissa A. Bingham; Marissa L. Scray

    Although there have been fairly recent advances regarding inference for three-dimensional rotation data, there are still many areas of interest yet to be explored. One such area involves comparing the rotational symmetry of 3-D rotations. In this paper, nonparametric inference is used to test if F 1=F 2, where F i is the degree of rotational symmetry of distribution i, through a permutation test. The

  • A flexible distribution class for count data
    J. Stat. Distrib. App. Pub Date : 2017-09-26
    Kimberly F. Sellers; Andrew W. Swift; Kimberly S. Weems

    The Poisson, geometric and Bernoulli distributions are special cases of a flexible count distribution, namely the Conway-Maxwell-Poisson (CMP) distribution – a two-parameter generalization of the Poisson distribution that can accommodate data over- or under-dispersion. This work further generalizes the ideas of the CMP distribution by considering sums of CMP random variables to establish a flexible

  • Statistical reasoning in dependent p-generalized elliptically contoured distributions and beyond
    J. Stat. Distrib. App. Pub Date : 2017-09-20
    Wolf-Dieter Richter

    First, likelihood ratio statistics for checking the hypothesis of equal variances of two-dimensional Gaussian vectors are derived both under the standard $\left (\sigma ^{2}_{1},\sigma ^{2}_{2},\varrho \right)$ -parametrization and under the geometric (a,b,α)-parametrization where a 2 and b 2 are the variances of the principle components and α is an angle of rotation. Then, the likelihood ratio statistics

  • Rank correlation under categorical confounding
    J. Stat. Distrib. App. Pub Date : 2017-09-15
    Jean-François Plante

    Rank correlation is invariant to bijective marginal transformations, but it is not immune to confounding. Assuming a categorical confounding variable is observed, the author proposes weighted coefficients of correlation for continuous variables developed within a larger framework based on copulas. While the weighting is clear under the assumption that the dependence is the same within each group implied

  • A new diversity estimator
    J. Stat. Distrib. App. Pub Date : 2017-09-15
    Lukun Zheng; Jiancheng Jiang

    The maximum likelihood estimator (MLE) of Gini-Simpson’s diversity index (GS) is widely used but suffers from large bias when the number of species is large or infinite. We propose a new estimator of the GS index and show its unbiasedness. Asymptotic normality of the proposed estimator is established when the number of species in the population is finite and known, finite but unknown, and infinite

  • Optimal two-stage pricing strategies from the seller’s perspective under the uncertainty of buyer’s decisions
    J. Stat. Distrib. App. Pub Date : 2017-09-01
    Martín Egozcue; Jiang Wu; Ričardas Zitikis

    In Punta del Este, a resort town in Uruguay, real-estate property is in demand by both domestic and foreign buyers. There are several stages of selling residential units: before, during, and after the actual construction. Different pricing strategies are used at every stage. Our goal in this paper is to derive, under various scenarios of practical relevance, optimal strategies for setting prices within

  • Goodness of fit for the logistic regression model using relative belief
    J. Stat. Distrib. App. Pub Date : 2017-08-31
    Luai Al-Labadi; Zeynep Baskurt; Michael Evans

    A logistic regression model is a specialized model for product-binomial data. When a proper, noninformative prior is placed on the unrestricted model for the product-binomial model, the hypothesis H 0 of a logistic regression model holding can then be assessed by comparing the concentration of the posterior distribution about H 0 with the concentration of the prior about H 0. This comparison is effected

  • The Kumaraswamy transmuted Pareto distribution
    J. Stat. Distrib. App. Pub Date : 2017-08-15
    Sher B. Chhetri; Alfred A. Akinsete; Gokarna Aryal; Hongwei Long

    In this work, a new five-parameter Kumaraswamy transmuted Pareto (KwTP) distribution is introduced and studied. We discuss various mathematical and statistical properties of the distribution including obtaining expressions for the moments, quantiles, mean deviations, skewness, kurtosis, reliability and order statistics. The estimation of the model parameters is performed by the method of maximum likelihood

  • Alternative approaches for econometric modeling of panel data using mixture distributions
    J. Stat. Distrib. App. Pub Date : 2017-08-01
    Judex Hyppolite

    The economic researcher is sometimes confronted with panel datasets that come from a population made of a finite number of subpopulations. Within each subpopulation the individuals may also be heterogenous according to some unobserved characteristics. A good understanding of the behavior of the observed individuals may then require the ability to identify the groups to which they belong and to study

  • Mittag - Leffler function distribution - a new generalization of hyper-Poisson distribution
    J. Stat. Distrib. App. Pub Date : 2017-07-13
    Subrata Chakraborty; S. H. Ong

    In this paper a new generalization of the hyper-Poisson distribution is proposed using the Mittag-Leffler function. The hyper-Poisson, displaced Poisson, Poisson and geometric distributions among others are seen as particular cases. This Mittag-Leffler function distribution (MLFD) belongs to the generalized hypergeometric and generalized power series families and also arises as weighted Poisson distributions

  • A note on inconsistent families of discrete multivariate distributions
    J. Stat. Distrib. App. Pub Date : 2017-07-05
    Sugata Ghosh; Subhajit Dutta; Marc G. Genton

    We construct a d-dimensional discrete multivariate distribution for which any proper subset of its components belongs to a specific family of distributions. However, the joint d-dimensional distribution fails to belong to that family and in other words, it is ‘inconsistent’ with the distribution of these subsets. We also address preservation of this ‘inconsistency’ property for the symmetric Binomial

  • The odd log-logistic logarithmic generated family of distributions with applications in different areas
    J. Stat. Distrib. App. Pub Date : 2017-07-04
    Morad Alizadeh; S. M. T. K. MirMostafee; Edwin M. M. Ortega; Thiago G. Ramires; Gauss M. Cordeiro

    We introduce and study general mathematical properties of a new generator of continuous distributions with three extra parameters called the odd log-logistic logarithmic generated family of distributions. We present some special models and investigate the asymptotes and shapes. The new density function can be expressed as a linear combination of exponentiated densities based on the same baseline distribution

  • Recent developments on the moment problem
    J. Stat. Distrib. App. Pub Date : 2017-07-03
    Gwo Dong Lin

    We consider univariate distributions with finite moments of all positive orders. The moment problem is to determine whether or not a given distribution is uniquely determined by the sequence of its moments. There is a huge literature on this classical topic. In this survey, we will focus only on the recent developments on the checkable moment-(in)determinacy criteria including Cramér’s condition, Carleman’s

  • High quantile regression for extreme events
    J. Stat. Distrib. App. Pub Date : 2017-05-03
    Mei Ling Huang; Christine Nguyen

    For extreme events, estimation of high conditional quantiles for heavy tailed distributions is an important problem. Quantile regression is a useful method in this field with many applications. Quantile regression uses an L 1-loss function, and an optimal solution by means of linear programming. In this paper, we propose a weighted quantile regression method. Monte Carlo simulations are performed to

  • Marginalized mixture models for count data from multiple source populations.
    J. Stat. Distrib. App. Pub Date : 2017-04-07
    Habtamu K Benecha,Brian Neelon,Kimon Divaris,John S Preisser

    Mixture distributions provide flexibility in modeling data collected from populations having unexplained heterogeneity. While interpretations of regression parameters from traditional finite mixture models are specific to unobserved subpopulations or latent classes, investigators are often interested in making inferences about the marginal mean of a count variable in the overall population. Recently

  • The ubiquity of the Simpson’s Paradox
    J. Stat. Distrib. App. Pub Date : 2017-03-28
    Alessandro Selvitella

    The Simpson’s Paradox is the phenomenon that appears in some datasets, where subgroups with a common trend (say, all negative trend) show the reverse trend when they are aggregated (say, positive trend). Even if this issue has an elementary mathematical explanation, it has a deep statistical significance. In this paper, we discuss basic examples in arithmetic, geometry, linear algebra, statistics,

  • Simulation of polyhedral convex contoured distributions
    J. Stat. Distrib. App. Pub Date : 2017-03-21
    Wolf-Dieter Richter; Kay Schicker

    In low dimensions, the relatively easily implementable acceptance-rejection method for generating polyhedral convex contoured uniform distributions is compared to more sophisticated particular methods from the literature, and applied to drug combination studies. Based upon a stochastic representation, the method is extended to the general class of polyhedral convex contoured distributions of known

  • Generalized log-logistic proportional hazard model with applications in survival analysis
    J. Stat. Distrib. App. Pub Date : 2016-11-29
    Shahedul A. Khan; Saima K. Khosa

    Proportional hazard (PH) models can be formulated with or without assuming a probability distribution for survival times. The former assumption leads to parametric models, whereas the latter leads to the semi-parametric Cox model which is by far the most popular in survival analysis. However, a parametric model may lead to more efficient estimates than the Cox model under certain conditions. Only a

  • Exponentiated Marshall-Olkin family of distributions
    J. Stat. Distrib. App. Pub Date : 2016-11-05
    Cícero R. B. Dias; Gauss M. Cordeiro; Morad Alizadeh; Pedro Rafael Diniz Marinho; Hemílio Fernandes Campos Coêlho

    We study general mathematical properties of a new class of continuous distributions with three extra shape parameters called the exponentiated Marshal-Olkin family of distributions. Further, we present some special models of the new class and investigate the shapes and derive explicit expressions for the ordinary and incomplete moments, quantile and generating functions and probability weighted moments

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