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A generalization to the loginverse Weibull distribution and its applications in cancer research J. Stat. Distrib. App. Pub Date : 20211212
Kumar, C. Satheesh, Nair, Subha R.In this paper we consider a generalization of a logtransformed version of the inverse Weibull distribution. Several theoretical properties of the distribution are studied in detail including expressions for its probability density function, reliability function, hazard rate function, quantile function, characteristic function, raw moments, percentile measures, entropy measures, median, mode etc. Certain

Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models J. Stat. Distrib. App. Pub Date : 20210806
Nguyen, Hien Duy, Nguyen, TrungTin, Chamroukhi, Faicel, McLachlan, Geoffrey JohnMixture of experts (MoE) models are widely applied for conditional probability density estimation problems. We demonstrate the richness of the class of MoE models by proving denseness results in Lebesgue spaces, when inputs and outputs variables are both compactly supported. We further prove an almost uniform convergence result when the input is univariate. Auxiliary lemmas are proved regarding the

Structural properties of generalised Planck distributions J. Stat. Distrib. App. Pub Date : 20210801
Pakes, Anthony G.A family of generalised Planck (GP) laws is defined and its structural properties explored. Sometimes subject to parameter restrictions, a GP law is a randomly scaled gamma law; it arises as the equilibrium law of a perturbed version of the Feller mean reverting diffusion; the density functions can be decreasing, unimodal or bimodal; it is infinitely divisible. It is argued that the GP law is not a

New class of Lindley distributions: properties and applications J. Stat. Distrib. App. Pub Date : 20210719
Duha Hamed, Ahmad AlzaghalA new generalized class of Lindley distribution is introduced in this paper. This new class is called the TLindley{Y} class of distributions, and it is generated by using the quantile functions of uniform, exponential, Weibull, loglogistic, logistic and Cauchy distributions. The statistical properties including the modes, moments and Shannon’s entropy are discussed. Three new generalized Lindley

Tolerance intervals in statistical software and robustness under model misspecification J. Stat. Distrib. App. Pub Date : 20210718
Kyung Serk Cho, Hon Keung Tony NgA tolerance interval is a statistical interval that covers at least 100ρ% of the population of interest with a 100(1−α)% confidence, where ρ and α are prespecified values in (0, 1). In many scientific fields, such as pharmaceutical sciences, manufacturing processes, clinical sciences, and environmental sciences, tolerance intervals are used for statistical inference and quality control. Despite the

Combining assumptions and graphical network into gene expression data analysis J. Stat. Distrib. App. Pub Date : 20210708
Demba Fofana, E. O. George, Dale BowmanAnalyzing gene expression data rigorously requires taking assumptions into consideration but also relies on using information about network relations that exist among genes. Combining these different elements cannot only improve statistical power, but also provide a better framework through which gene expression can be properly analyzed. We propose a novel statistical model that combines assumptions

A comparison of zeroinflated and hurdle models for modeling zeroinflated count data J. Stat. Distrib. App. Pub Date : 20210624
Cindy Xin FengCounts data with excessive zeros are frequently encountered in practice. For example, the number of health services visits often includes many zeros representing the patients with no utilization during a followup time. A common feature of this type of data is that the count measure tends to have excessive zero beyond a common count distribution can accommodate, such as Poisson or negative binomial

A general stochastic model for bivariate episodes driven by a gamma sequence J. Stat. Distrib. App. Pub Date : 20210412
Charles K. Amponsah, Tomasz J. Kozubowski, Anna K. PanorskaWe propose a new stochastic model describing the joint distribution of (X,N), where N is a counting variable while X is the sum of N independent gamma random variables. We present the main properties of this general model, which include marginal and conditional distributions, integral transforms, moments and parameter estimation. We also discuss in more detail a special case where N has a heavy tailed

A flexible multivariate model for highdimensional correlated count data J. Stat. Distrib. App. Pub Date : 20210316
Alexander D. Knudson, Tomasz J. Kozubowski, Anna K. Panorska, A. Grant SchisslerWe propose a flexible multivariate stochastic model for overdispersed count data. Our methodology is built upon mixed Poisson random vectors (Y1,…,Yd), where the {Yi} are conditionally independent Poisson random variables. The stochastic rates of the {Yi} are multivariate distributions with arbitrary nonnegative margins linked by a copula function. We present basic properties of these mixed Poisson

Generalized fiducial inference on the mean of zeroinflated Poisson and Poisson hurdle models J. Stat. Distrib. App. Pub Date : 20210306
Yixuan Zou, Jan Hannig, Derek S. YoungZeroinflated and hurdle models are widely applied to count data possessing excess zeros, where they can simultaneously model the process from how the zeros were generated and potentially help mitigate the effects of overdispersion relative to the assumed count distribution. Which model to use depends on how the zeros are generated: zeroinflated models add an additional probability mass on zero, while

Multivariate distributions of correlated binary variables generated by paircopulas J. Stat. Distrib. App. Pub Date : 20210305
Huihui Lin, N. Rao ChagantyCorrelated binary data are prevalent in a wide range of scientific disciplines, including healthcare and medicine. The generalized estimating equations (GEEs) and the multivariate probit (MP) model are two of the popular methods for analyzing such data. However, both methods have some significant drawbacks. The GEEs may not have an underlying likelihood and the MP model may fail to generate a multivariate

On two extensions of the canonical Feller–Spitzer distribution J. Stat. Distrib. App. Pub Date : 20210304
Vladimir Vladimirovich Vinogradov, Richard Bruce ParisWe introduce two extensions of the canonical Feller–Spitzer distribution from the class of Bessel densities, which comprise two distinct stochastically decreasing oneparameter families of positive absolutely continuous infinitely divisible distributions with monotone densities, whose upper tails exhibit a power decay. The densities of the members of the first class are expressed in terms of the modified

A new trivariate model for stochastic episodes J. Stat. Distrib. App. Pub Date : 20210226
Francesco Zuniga, Tomasz J. Kozubowski, Anna K. PanorskaWe study the joint distribution of stochastic events described by (X,Y,N), where N has a 1inflated (or deflated) geometric distribution and X, Y are the sum and the maximum of N exponential random variables. Models with similar structure have been used in several areas of applications, including actuarial science, finance, and weather and climate, where such events naturally arise. We provide basic

A flexible univariate moving average timeseries model for dispersed count data J. Stat. Distrib. App. Pub Date : 20210221
Kimberly F. Sellers, Ali Arab, Sean Melville, Fanyu CuiAlOsh and Alzaid (1988) consider a Poisson moving average (PMA) model to describe the relation among integervalued time series data; this model, however, is constrained by the underlying equidispersion assumption for count data (i.e., that the variance and the mean equal). This work instead introduces a flexible integervalued moving average model for count data that contain over or underdispersion

Spatiotemporal analysis of flood data from South Carolina J. Stat. Distrib. App. Pub Date : 20201126
Haigang Liu, David B. Hitchcock, S. Zahra SamadiTo investigate the relationship between flood gage height and precipitation in South Carolina from 2012 to 2016, we built a conditional autoregressive (CAR) model using a Bayesian hierarchical framework. This approach allows the modelling of the main spatiotemporal properties of water height dynamics over multiple locations, accounting for the effect of river network, geomorphology, and forcing rainfall

Affinetransformation invariant clustering models J. Stat. Distrib. App. Pub Date : 20201028
HsinHsiung Huang, Jie YangWe develop a cluster process which is invariant with respect to unknown affine transformations of the feature space without knowing the number of clusters in advance. Specifically, our proposed method can identify clusters invariant under (I) orthogonal transformations, (II) scalingcoordinate orthogonal transformations, and (III) arbitrary nonsingular linear transformations corresponding to models

Distributions associated with simultaneous multiple hypothesis testing J. Stat. Distrib. App. Pub Date : 20201019
Chang Yu, Daniel ZeltermanWe develop the distribution for the number of hypotheses found to be statistically significant using the rule from Simes (Biometrika 73: 751–754, 1986) for controlling the familywise error rate (FWER). We find the distribution of the number of statistically significant pvalues under the null hypothesis and show this follows a normal distribution under the alternative. We propose a parametric distribution

New families of bivariate copulas via unit weibull distortion J. Stat. Distrib. App. Pub Date : 20201006
Fadal A.A. Aldhufairi, Jungsywan H. SepanskiThis paper introduces a new family of bivariate copulas constructed using a unit Weibull distortion. Existing copulas play the role of the base or initial copulas that are transformed or distorted into a new family of copulas with additional parameters, allowing more flexibility and better fit to data. We present a general form for the new bivariate copula function and its conditional and density distributions

Generalized logistic distribution and its regression model J. Stat. Distrib. App. Pub Date : 20200907
Mohammad A. Aljarrah, Felix Famoye, Carl LeeA new generalized asymmetric logistic distribution is defined. In some cases, existing three parameter distributions provide poor fit to heavy tailed data sets. The proposed new distribution consists of only three parameters and is shown to fit a much wider range of heavy left and right tailed data when compared with various existing distributions. The new generalized distribution has logistic, maximum

The sphericalDirichlet distribution J. Stat. Distrib. App. Pub Date : 20200905
Jose H. GuardiolaToday, data mining and gene expressions are at the forefront of modern data analysis. Here we introduce a novel probability distribution that is applicable in these fields. This paper develops the proposed sphericalDirichlet distribution designed to fit vectors located at the positive orthant of the hypersphere, as it is often the case for data in these fields, avoiding unnecessary probability mass

Item fit statistics for Rasch analysis: can we trust them? J. Stat. Distrib. App. Pub Date : 20200828
Marianne MüllerTo compare fit statistics for the Rasch model based on estimates of unconditional or conditional response probabilities. Using person estimates to calculate fit statistics can lead to problems because the person estimates are biased. Conditional response probabilities given the total person score could be used instead. Data sets are simulated which fit the Rasch model. Type I error rates are calculated

Exact distributions of statistics for making inferences on mixed models under the default covariance structure J. Stat. Distrib. App. Pub Date : 20200817
Samaradasa Weerahandi, ChingRay YuAt this juncture when mixed models are heavily employed in applications ranging from clinical research to business analytics, the purpose of this article is to extend the exact distributional result of Wald (Ann. Math. Stat. 18: 586–589, 1947) to handle models involving a number of variance components.Due to the unavailability of exact distributional results for underlying statistics, currently available

A new discrete pareto type (IV) model: theory, properties and applications J. Stat. Distrib. App. Pub Date : 20200801
Indranil GhoshDiscrete analogue of a continuous distribution (especially in the univariate domain) is not new in the literature. The work of discretizing continuous distributions begun with the paper by Nakagawa and Osaki (1975) to the best of the knowledge of the author. Since then several authors proposed discrete analogues of known continuous models. In this paper, we propose and study a discrete analogue of

Density deconvolution for generalized skewsymmetric distributions J. Stat. Distrib. App. Pub Date : 20200723
Cornelis J. PotgieterThe density deconvolution problem is considered for random variables assumed to belong to the generalized skewsymmetric (GSS) family of distributions. The approach is semiparametric in that the symmetric component of the GSS distribution is assumed known, and the skewing function capturing deviation from the symmetric component is estimated using a deconvolution kernel approach. This requires the

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.

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