Some gamma distributions

Three new generalizations of the standard gamma distribution introduced by the author are reviewed. Various properties are derived for each distribution, including its hazard rate function and moments. An application is illustrated to drought data.     

Some bivariate uniform distributions

Bivariate uniform distributions with dependent components are readily derived by distribution function transformations of the components of non-uniform dependent continuous bivariate random variables (X,Y). Contour plots of joint density functions show the various, and varying, forms of dependence which can arise from different distributional forms for (X,Y) and aids the choice of bivariate uniform distributions as empirical models.     

The simulation of spherical distributions in the Fisher-Bingham family

The problem of generating pseudo-random unit vectors from the Fisher-Bingham distribution is considered. Attention is focussed on a subfamily known as FB_6' which includes many of the spherical distributions of practical interest, including those of Dimroth-Watson, FB_4' FB₅ and Bingham type. The rejection procedures suggested here for these distributions should prove adequate for most practical purposes. Possibilities for simulating from the general Fisher-Bingham distribution are also mentioned briefly.     

Some properties of discrete bathtub-shaped distributions

The existence and the usefulness of discrete bathtub-shaped and upside down bathtub-shaped distributions have been demonstrated in some papers of recent origin. However, the general properties of these two classes of distributions do not seem to have been discussed. This article proposes to study some reliability properties of such distributions. We investigate the closure properties with reference to convolution, mixing, series and parallel systems, etc. and existence of bounds on reliability functions, moment properties, and convergence.     

Some characterizations of distributions of the exponential-type

We consider a particular subclass of the two-parameter exponential family with natural parameters γ₁, γ₂ and characterize those distributions of the family having a ratio of the mean value and the variance that is a linear function of γ₁ by the form of the moment generating function. As special cases we find the normal and the gamma distributions.     

Some bivariate families of lagrangian probability distributions

The bivariate Lagrange expansion, given by Poincare (1986), has been explained and slightly modified which gives bivariate Lagrangian probability models. A generalized bivariate Lagrangian Poisson distribution with six parameters has been obtained and studied. Also, the bivariate Lagrangian binomial, bivariate Lagrangian negative binomial and bivariate Lagrangian logarithmic series distribution have been obtained.     

Some characterizations of distributions by truncated moments

In this paper we extend GUPTA'S (1975) resLilt and show that the constant value of a truncated moment characterizes the exponential distribution. Similar results are prov¬ed in the discrete case and it is shown that two consecutive factorial moments are enough to determine the distribution. However, under mild conditions, the constancy of one fac¬torial moment is enough to guarantee that the distribution is geometric. Considering the truncation on the right, a general method of obtaining the distribution, whenever £(h(X) | Xj) is known, is fxmbitud in tnr continuous and in the discrete case. Several of the known characterization theorems toilow trivially trorn our results     

Some joint distributions for conditional random walks

Joint distributions concerning maxima, minima, and their indices are determined for certain conditional random walks called Bernoulli excursion and Bernoulli meander. The distribution of the local time of these processes is treated by generating function technique. Limiting distributions are also given, providing some partial results for Brownian excursion and meander.     

Some bounds related to Harris family of distributions

As a lifetime distribution, Harris family of distributions are applied to the lifetime of a series system with random number of components. In this paper, properties of various ageing classes of mixtures of Harris family of distributions, where the tilt parameter of a Harris distribution is taken as a random variable, are studied. We obtain an upper bound for maximum error in evaluating its reliability function. Two bounds are also presented for survival function and expectation of the mixed Harris family. We also provide some interesting bounds for its residual survival function. Our results generalize several previous findings in this connection. Some illustrative examples are also provided.     

Some characterizations of discrete distributions based on weak records

Let X ₁, X ₂,... be iid random variables (rv's) with the support on nonnegative integers and let (W _n , n≥0) denote the corresponding sequence of weak record values. We obtain new characterization of geometric and some other discrete distributions based on different forms of partial independence of rv's W _n and W _n+r —W _n for some fixed n≥0 and r≥1. We also prove that rv's W ₀ and W _n+1 —W _n have identical distribution if and only if (iff) the underlying distribution is geometric.     

Some linear model techniques for analyzing small-circle spherical data

The author investigates least squares as a method for fitting small-circle models to a sample of unit vectors in R³. He highlights a local linear model underlying the estimation of the parameters of a circle. This model is used to construct an estimation algorithm and regression-type inference procedures for the parameters of a circle. It makes it possible to compare the fit of a small circle with that of a spherical ellipse. The limitations of the least-squares approach are emphasized: when the errors are bounded away from 0, the least-squares estimators are not consistent as the sample size goes to infinity. Two examples, concerned with the migration of elephant seals and with the classification of geological folds, are analyzed using the linear model techniques proposed in this work.     

Some classes of distributions and their application in queueing

New classes of distributions are defined in analogy to the properties NBU, NBUE known from reliability. They are applied to obtain bounds on certain parameters of the GI/G/1 queue, such as the mean and the variance of the stationary waiting time, the probability of waiting, and the covariances of waiting times.     

Some robust tests of independence in symmetrical multivariate distributions

Let X =(x)^ij=(11¹, …, X,)_T, i = l, …n, be an n X random matrix having multivariate symmetrical distributions with parameters μ, Σ. The p-variate normal with mean μ and covariance matrix is a member of this family. Let be the squared multiple correlation coefficient between the first and the succeeding p₁ components, and let p² = + be the squared multiple correlation coefficient between the first and the remaining p₁ + p₂ =p – 1 components of the p-variate normal vector. We shall consider here three testing problems for multivariate symmetrical distributions. They are (A) to test p² =0 against; (B) to test against =0, 0; (C) to test against p² =0, We have shown here that for problem (A) the uniformly most powerful invariant (UMPI) and locally minimax test for the multivariate normal is UMPI and is locally minimax as p² 0 for multivariate symmetrical distributions. For problem (B) the UMPI and locally minimax test is UMPI and locally minimax as for multivariate symmetrical distributions. For problem (C) the locally best invariant (LBI) and locally minimax test for the multivariate normal is also LBI and is locally minimax as for multivariate symmetrical distributions.     

Some multivariate singular unified skew-normal distributions and their application

Abstract

We introduce here the truncated version of the unified skew-normal (SUN) distributions. By considering a special truncations for both univariate and multivariate cases, we derive the joint distribution of consecutive order statistics X_{(r, ..., r + k)} = (X_(r), ..., X_{(r + K)})^T from an exchangeable n-dimensional normal random vector X. Further we show that the conditional distributions of X_{(r + j, ..., r + k)} given X_{(r, ..., r + j ? 1)}, X_{(r, ..., r + k)} given (X_(r) > t)?and X_{(r, ..., r + k)} given (X_{(r + k)} < t) are special types of singular SUN distributions. We use these results to determine some measures in the reliability theory such as the mean past life (MPL) function and mean residual life (MRL) function.     

Some applications of lagrangian distributions in queueing theory and epidemiology

In this note we demonstrate that the Lagrangian distributions have applications in queueing theory and theory of epidemics. The Lagrangian distribution appears as the distribution describing the number of customers served in a busy period under certain conditions. Also, the Lagrangian distribution describes the distribution of number of persons infected by a certain infectious disease.     

Some characterizations of geometric tail distributions based on record values

Let R_j be the jth upper record value from an infinite sequence of independent identically distributed positive integer valued random variables. We show that their common distribution must have geometric tail if R_j+k?R_j and R_j are partially independent for some j≥1 and k≥1 or if E(R_j+2?R_j+1| R_j) is a constant. Three versions of partial independence, each of which provides a characterization of the geometric tail are presented.     

Some properties of the tukey g and h family of distributions

The g and h family of distributions, introduced by J.W. Tukey, is generated by a single transformation of the standard normal which allows for symmetry and heavier tails. Selected percentage points are tabulated, and a closed-form solution for the moments, when they exist, is found. A comparison is made with the Pearson system of distributions. The g and h distributions cover most of the Pearson family to an adequate approximation, when the first four moments exist, and also generate a variety of other types of distributions. Selected distributions graphically illustrate the great variety of possible shapes.     

Some results for type I censored sampling from geometric distributions

This paper gives the likelihood function for a Type I censored sample from the geometric distribution with parameter p, and the maximum likelihood estimator for p. Exact and asymptotic sampling distributions of joint sufficient statistics for p are derived. Such distributional results make it possible to develop tests or confidence intervals based on discrete censored data, which are not available now in the literature. Neyman-Pearson tests for p are developed. Examples are given to illustrate these results.     

Some tests for uniformity of circular distributions powerful against multimodal alternatives

The author introduces new statistics suited for testing uniformity of circular distributions and powerful against multimodal alternatives. One of them has a simple expression in terms of the geometric mean of the sample of chord lengths. The others belong to a family indexed by a continuous parameter. The asymptotic distributions under the null hypothesis are derived. We compare the power of the new tests against Stephens's alternatives with those of Ajne, Watson, and Hermans‐Rasson's tests. Some of the new tests are the most powerful when the alternative has three or four modes. A heuristic justification of this feature is given. An application to the analysis of archaeological data is provided. The Canadian Journal of Statistics 38:80–96; 2010 © 2010 Statistical Society of Canada