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The unifed distribution J. Stat. Distrib. App. Pub Date : 20191105
Oscar Alberto Quijano XacurWe 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 : 20151126
Lipika Ghosh,Jiancheng Jiang,Yanqing Sun,Haibo ZhouIn 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 nonvalidation subsample and the auxiliary

Missing data approaches for probability regression models with missing outcomes with applications. J. Stat. Distrib. App. Pub Date : 20140101
Li Qi,Yanqing SunIn this paper, we investigate several well known approaches for missing data and their relationships for the parametric probability regression model Pβ (YX) 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 : 20190823
Fiaz Ahmad Bhatti; G. G. Hamedani; Mustafa C. Korkmaz; Gauss M. Cordeiro; Haitham M. Yousof; Munir AhmadIn this paper, a flexible family of distributions with unimodel, bimodal, increasing, increasing and decreasing, inverted bathtub and modified bathtub hazard rate called Burr IIIMarshal OlkinG (BIIIMOG) family is developed on the basis of the TX family technique. The density function of the BIIIMOG family is arc, exponential, left skewed, rightskewed and symmetrical shaped. Descriptive measures

The linearly decreasing stress Weibull (LDSWeibull): a new Weibulllike distribution J. Stat. Distrib. App. Pub Date : 20190820
Roger W. Barnard; Chamila Perera; James G. Surles; A. Alexandre TrindadeMotivated 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 threeparameter 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 twoarm trials J. Stat. Distrib. App. Pub Date : 20190724
Saman Muthukumarana; David Martell; Ram TiwariWe present a novel Bayesian approach to random effects meta analysis of binary data with excessive zeros in twoarm 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 : 20190612
WolfDieter RichterThe 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 Fdistributions. 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 : 20190611
MeiLing Ting Lee; G. A. WhitmoreMany 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 shockdegradation 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 : 20190608
Maria C.S. Lima; Gauss M. Cordeiro; Edwin M.M. Ortega; Abraão D.C. NascimentoVarious applications in natural science require models more accurate than wellknown distributions. In this context, several generators of distributions have been recently proposed. We introduce a new fourparameter extended normal (EN) distribution, which can provide better fits than the skewnormal and beta normal distributions as proved empirically in two applications to real data. We present Monte

Highdimensional starshaped distributions J. Stat. Distrib. App. Pub Date : 20190606
WolfDieter RichterStochastic representations of starshaped 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 grobust statistics is proved to

Multiclass analysis and prediction with network structured covariates J. Stat. Distrib. App. Pub Date : 20190606
LiPang Chen; Grace Y. Yi; Qihuang Zhang; Wenqing HeTechnological 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 : 20190415
Johannes T. Ferreira; Andriëtte BekkerThe 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 Doptimal designs J. Stat. Distrib. App. Pub Date : 20190408
Yu Shi; Zizhao Zhang; Weng Kee WongWhen a modelbased 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 natureinspired metaheuristic

Admissible Bernoulli correlations J. Stat. Distrib. App. Pub Date : 20190308
Mark Huber; Nevena MarićA 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 pgeneralized elliptical random processes J. Stat. Distrib. App. Pub Date : 20190307
Klaus Müller; WolfDieter RichterWe introduce rankkcontinuous axisaligned pgeneralized elliptically contoured distributions and study their properties such as stochastic representations, moments, and densitylike representations. Applying the Kolmogorov existence theorem, we prove the existence of random processes having axisaligned pgeneralized 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 : 20181229
Maria A Veretennikova,Alla Sikorskii,Michael J BoivinThe 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 halfnormal distribution: properties and regression models J. Stat. Distrib. App. Pub Date : 20181205
Emrah Altun; Haitham M. Yousof; G.G. HamedaniIn this paper, a new extension of the generalized halfnormal 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 loglocation regression model based on the new distribution is also introduced and studied

Analytical properties of generalized Gaussian distributions J. Stat. Distrib. App. Pub Date : 20181204
Alex Dytso; Ronit Bustin; H. Vincent Poor; Shlomo ShamaiThe 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 WeibullX family of distributions: properties, characterizations and applications J. Stat. Distrib. App. Pub Date : 20181103
Zubair Ahmad; M. Elgarhy; G. G. HamedaniWe propose a new family of univariate distributions generated from the Weibull random variable, called a new WeibullX family of distributions. Two special submodels 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 geometricquadratic hazard rate distribution: development, properties, characterizations and applications J. Stat. Distrib. App. Pub Date : 20180813
Fiaz Ahmad Bhatti; G. G. Hamedani; Mustafa Ç. Korkmaz; Munir AhmadWe propose a five parameter transmuted geometric quadratic hazard rate (TGQHR) distribution derived from mixture of quadratic hazard rate (QHR), geometric and transmuted distributions via the application of transmuted geometricG (TGG) family of Afify et al.(Pak J Statist 32(2), 139160, 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 : 20180718
Mei Ling Huang,Christine NguyenQuantile 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 nonlinear 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 : 20180118
Frantisek Duris; Juraj Gazdarica; Iveta Gazdaricova; Lucia Strieskova; Jaroslav Budis; Jan Turna; Tomas SzemesRatio 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 powerCauchy negativebinomial: properties and regression J. Stat. Distrib. App. Pub Date : 20180108
Muhammad Zubair; Muhammad H. Tahir; Gauss M. Cordeiro; Ayman Alzaatreh; Edwin M. M. OrtegaWe propose and study a new compounded model to extend the halfCauchy and powerCauchy 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 reversedJ and rightskewed. It can accommodate different hazard shapes

Families of distributions arising from the quantile of generalized lambda distribution J. Stat. Distrib. App. Pub Date : 20171122
Mahmoud Aldeni; Carl Lee; Felix FamoyeIn this paper, the class of TR {generalized lambda} families of distributions based on the quantile of generalized lambda distribution has been proposed using the TR{Y} framework. In the development of the TR{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 : 20171115
Hoben Thomas; Thomas P. HettmanspergerRisk 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 ktuple statistics in zeroone sequences of Markovdependent trials J. Stat. Distrib. App. Pub Date : 20171115
Anastasios N. Arapis; Frosso S. Makri; Zaharias M. PsillakisWe consider a sequence of n, n≥3, zero (0)  one (1) Markovdependent trials. We focus on ktuples 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 ktuples of 1s, the number of 1s in them and the distance between the first and the last ktuple 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 : 20171101
Matthew Matheson; Alvaro Muñoz; Christopher CoxThe 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 threeparameter distributions and four parent distributions of four

Quantile regression for overdispersed count data: a hierarchical method J. Stat. Distrib. App. Pub Date : 20171101
Peter CongdonGeneralized 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 : 20171101
Gauss M. Cordeiro; Antonio E. Gomes; Cibele Q. daSilva; Edwin M. M. OrtegaFor any continuous baseline G distribution, Zografos and Balakrishnan (Statistical Methodology 6:344–362, 2009) introduced the gammagenerated family of distributions with an extra shape parameter. Based on this family, we define a new fourparameter extension of the Burr III distribution. It can have decreasing, unimodal and decreasingincreasingdecreasing hazard rate function. We provide a comprehensive

Analysis of casecontrol data with interacting misclassified covariates J. Stat. Distrib. App. Pub Date : 20171030
Grace Y. Yi; Wenqing HeCasecontrol studies are important and useful methods for studying health outcomes and many methods have been developed for analyzing casecontrol 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 casecontrol data with interacting misclassified covariates

Correction to: a flexible distribution class for count data J. Stat. Distrib. App. Pub Date : 20171016
Kimberly F. Sellers; Andrew W. Swift; Kimberly S. WeemsFollowing 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 : 20171015
John P. NolanErlang renewal models, also called chisquared 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 : 20171002
Vladimir V. Vinogradov; Richard B. ParisPoissonTweedie mixtures are the Poisson mixtures for which the mixing measure is generated by those members of the family of Tweedie distributions whose support is nonnegative. This class of nonnegative integervalued distributions is comprised of Neyman type A, backshifted negative binomial, compound Poissonnegative binomial, discrete stable and exponentially tilted discrete stable laws. For

A permutation test for comparing rotational symmetry in threedimensional rotation data sets J. Stat. Distrib. App. Pub Date : 20170929
Melissa A. Bingham; Marissa L. ScrayAlthough there have been fairly recent advances regarding inference for threedimensional rotation data, there are still many areas of interest yet to be explored. One such area involves comparing the rotational symmetry of 3D 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 : 20170926
Kimberly F. Sellers; Andrew W. Swift; Kimberly S. WeemsThe Poisson, geometric and Bernoulli distributions are special cases of a flexible count distribution, namely the ConwayMaxwellPoisson (CMP) distribution – a twoparameter generalization of the Poisson distribution that can accommodate data over or underdispersion. 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 pgeneralized elliptically contoured distributions and beyond J. Stat. Distrib. App. Pub Date : 20170920
WolfDieter RichterFirst, likelihood ratio statistics for checking the hypothesis of equal variances of twodimensional 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 : 20170915
JeanFrançois PlanteRank 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 : 20170915
Lukun Zheng; Jiancheng JiangThe maximum likelihood estimator (MLE) of GiniSimpson’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 twostage pricing strategies from the seller’s perspective under the uncertainty of buyer’s decisions J. Stat. Distrib. App. Pub Date : 20170901
Martín Egozcue; Jiang Wu; Ričardas ZitikisIn Punta del Este, a resort town in Uruguay, realestate 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 : 20170831
Luai AlLabadi; Zeynep Baskurt; Michael EvansA logistic regression model is a specialized model for productbinomial data. When a proper, noninformative prior is placed on the unrestricted model for the productbinomial 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 : 20170815
Sher B. Chhetri; Alfred A. Akinsete; Gokarna Aryal; Hongwei LongIn this work, a new fiveparameter 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 : 20170801
Judex HyppoliteThe 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 hyperPoisson distribution J. Stat. Distrib. App. Pub Date : 20170713
Subrata Chakraborty; S. H. OngIn this paper a new generalization of the hyperPoisson distribution is proposed using the MittagLeffler function. The hyperPoisson, displaced Poisson, Poisson and geometric distributions among others are seen as particular cases. This MittagLeffler 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 : 20170705
Sugata Ghosh; Subhajit Dutta; Marc G. GentonWe construct a ddimensional discrete multivariate distribution for which any proper subset of its components belongs to a specific family of distributions. However, the joint ddimensional 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 loglogistic logarithmic generated family of distributions with applications in different areas J. Stat. Distrib. App. Pub Date : 20170704
Morad Alizadeh; S. M. T. K. MirMostafee; Edwin M. M. Ortega; Thiago G. Ramires; Gauss M. CordeiroWe introduce and study general mathematical properties of a new generator of continuous distributions with three extra parameters called the odd loglogistic 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 : 20170703
Gwo Dong LinWe 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 : 20170503
Mei Ling Huang; Christine NguyenFor 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 1loss 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 : 20170407
Habtamu K Benecha,Brian Neelon,Kimon Divaris,John S PreisserMixture 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 : 20170328
Alessandro SelvitellaThe 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 : 20170321
WolfDieter Richter; Kay SchickerIn low dimensions, the relatively easily implementable acceptancerejection 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 loglogistic proportional hazard model with applications in survival analysis J. Stat. Distrib. App. Pub Date : 20161129
Shahedul A. Khan; Saima K. KhosaProportional 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 semiparametric 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 MarshallOlkin family of distributions J. Stat. Distrib. App. Pub Date : 20161105
Cícero R. B. Dias; Gauss M. Cordeiro; Morad Alizadeh; Pedro Rafael Diniz Marinho; Hemílio Fernandes Campos CoêlhoWe study general mathematical properties of a new class of continuous distributions with three extra shape parameters called the exponentiated MarshalOlkin 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