Exact average coverage probabilities and confidence coefficients of confidence intervals for discrete distributions

For a confidence interval (L(X),U(X)) of a parameter θ in one-parameter discrete distributions, the coverage probability is a variable function of θ. The confidence coefficient is the infimum of the coverage probabilities, inf  _θ P _θ (θ∈(L(X),U(X))). Since we do not know which point in the parameter space the infimum coverage probability occurs at, the exact confidence coefficients are unknown. Beside confidence coefficients, evaluation of a confidence intervals can be based on the average coverage probability. Usually, the exact average probability is also unknown and it was approximated by taking the mean of the coverage probabilities at some randomly chosen points in the parameter space. In this article, methodologies for computing the exact average coverage probabilities as well as the exact confidence coefficients of confidence intervals for one-parameter discrete distributions are proposed. With these methodologies, both exact values can be derived.     

Concentration function and sensitivity to the prior

Summary In robust bayesian analysis, ranges of quantities of interest (e. g. posterior means) are usually considered when the prior probability measure varies in a class Γ. Such quantities describe the variation of just one aspect of the posterior measure. The concentration function describes changes in the posterior probability measure more globally, detecting differences in probability concentration and providing, simultaneously, bounds on the posterior probability of all measurable subsets. In this paper, we present a novel use of the concentration function, and two concentration indices, to study such posterior changes for a general class Γ, restricting then our attention to some ∈-contamination classes of priors.     

Concentration functions and Bayesian robustness

The concentration function, extending the classical notion of Lorenz curve, is well suited for comparing probability measures. Such a feature can be useful in different issues in Bayesian robustness, when a probability measure is deemed a baseline to be compared with other measures by means of their functional forms. Neighbourhood classes Γ of probability measures, including well-known ones, can be defined through the concentration function and both prior and posterior expectations of given functions of the unknown parameter are studied. The ranges of such expectations over Γ can be found, restricting the search among the extremal measures in Γ. The concentration function can be also used as a criterion to assess posterior robustness, when considering sensitivity to changes in the likelihood and the prior.     

A measure of asymmetry

It is a general practice to make assertions about the symmetry or asymmetry of a probability density function based on the coefficients of skewness. Since most of the coefficients of skewness are designed to be zero for a symmetric density, they overall do provide an indication of symmetry. However, skewness is primarily influenced by the tail behavior of a density function, and the skewness coefficients are designed to capture this behavior. Thus they do not calibrate asymmetry in the density curves. We provide a necessary condition for a probability density function to be symmetric and use that to measure asymmetry in a continuous density curve on the scale of ?1 to 1. We show through examples that the proposed measure does an admirable job of capturing the visual impression of asymmetry of a continuous density function.     

An analysis of quantile measures of kurtosis: center and tails

The consequences of substituting the denominator Q ₃(p)  −  Q ₁(p) by Q ₂  −  Q ₁(p) in Groeneveld’s class of quantile measures of kurtosis (γ ₂(p)) for symmetric distributions, are explored using the symmetric influence function. The relationship between the measure γ ₂(p) and the alternative class of kurtosis measures κ₂(p) is derived together with the relationship between their influence functions. The Laplace, Logistic, symmetric Two-sided Power, Tukey and Beta distributions are considered in the examples in order to discuss the results obtained pertaining to unimodal, heavy tailed, bounded domain and U-shaped distributions. The authors thank the referee for the careful review.     

Probability Generating Function Based Jeffrey's Divergence for Statistical Inference

Statistical inference procedures based on transforms such as characteristic function and probability generating function have been examined by many researchers because they are much simpler than probability density functions. Here, a probability generating function based Jeffrey's divergence measure is proposed for parameter estimation and goodness-of-fit test. Being a member of the M-estimators, the proposed estimator is consistent. Also, the proposed goodness-of-fit test has good statistical power. The proposed divergence measure shows improved performance over existing probability generating function based measures. Real data examples are given to illustrate the proposed parameter estimation method and goodness-of-fit test.     

UMVU estimation of the reliability function of the generalized life distributions

The problems of estimating the reliability function and P=P_rX > Y are considered for the generalized life distributions. Uniformly minimum variance unbiased estimators (UMVUES) of the powers of the parameter involved in the probabilistic model and the probability density function (pdf) at a specified point are derived. The UMVUE of the pdf is utilized to obtain the UMVUE of the reliability function and ‘P’. Our method of obtaining these estimators is quite simple than the traditional approaches. A theoretical method of studying the behaviour of the hazard-rate is provided.     

Measuring the complexity of generalized linear hierarchical models

Measuring a statistical model's complexity is important for model criticism and comparison. However, it is unclear how to do this for hierarchical models due to uncertainty about how to count the random effects. The authors develop a complexity measure for generalized linear hierarchical models based on linear model theory. They demonstrate the new measure for binomial and Poisson observables modeled using various hierarchical structures, including a longitudinal model and an areal‐data model having both spatial clustering and pure heterogeneity random effects. They compare their new measure to a Bayesian index of model complexity, the effective number p_D of parameters (Spiegelhalter, Best, Carlin & van der Linde 2002); the comparisons are made in the binomial and Poisson cases via simulation and two real data examples. The two measures are usually close, but differ markedly in some instances where p_D is arguably inappropriate. Finally, the authors show how the new measure can be used to approach the difficult task of specifying prior distributions for variance components, and in the process cast further doubt on the commonly‐used vague inverse gamma prior.     

A Note on the Calculation of Pr{X 1 < X 2 < ··· < Xk }

Suppose that X_i are independent random variables, and that X_i has cdf F_i (x), 1 ≤ i ≤ k. Many statistical problems involve the probability Pr{X ₁ < X ₂ < ··· < X_k }. In this note a numerical method is proposed for computing this probability.     

Mathematische aspekte zu einer methode zur numerischen bestimmung individueller thermodynamischer aktivitätskoeffizienten einzelner ionensorten in konzentrierten elektrolytlösungen

The mathematical problems of the – in an communication [3] described – principle for the calculation of individual thermodynamic activity coefficients of single ionic species in concentrated electrolyte solutions are specified. It is the Newtonian approximation method that makes possible the evaluation of the constants b ₁,…b ₄ in the concentration function (0.1) for the product of the activity coefficients.

The efficiency of the method is represented by the example of the activity coefficients of pure and of – with other electrolytes – mixed solutions of NaCIO₄. The individual activity coefficients of the single ionic species are evaluated for several electrolytes of the concentration range from m = 0 to m = 10 mole/kg and published at another place [3, 17, 18].     

Asymptotic behaviour and statistical applications of divergence measures in multinomial populations: a unified study

Divergence measures play an important role in statistical theory, especially in large sample theories of estimation and testing. The underlying reason is that they are indices of statistical distance between probability distributions P and Q; the smaller these indices are the harder it is to discriminate between P and Q. Many divergence measures have been proposed since the publication of the paper of Kullback and Leibler (1951). Renyi (1961) gave the first generalization of Kullback-Leibler divergence, Jeffreys (1946) defined the J-divergences, Burbea and Rao (1982) introduced the R-divergences, Sharma and Mittal (1977) the (r,s)-divergences, Csiszar (1967) the ϕ-divergences, Taneja (1989) the generalized J-divergences and the generalized R-divergences and so on. In order to do a unified study of their statistical properties, here we propose a generalized divergence, called (h,ϕ)-divergence, which include as particular cases the above mentioned divergence measures. Under different assumptions, it is shown that the asymptotic distributions of the (h,ϕ)-divergence statistics are either normal or chi square. The chi square and the likelihood ratio test statistics are particular cases of the (h,ϕ)-divergence test statistics considered. From the previous results, asymptotic distributions of entropy statistics are derived too. Applications to testing statistical hypothesis in multinomial populations are given. The Pitman and Bahadur efficiencies of tests of goodness of fit and independence based on these statistics are obtained. To finish, apendices with the asymptotic variances of many well known divergence and entropy statistics are presented. The research in this paper was supported in part by DGICYT Grants N. PB91-0387 and N. PB91-0155. Their financial support is gratefully acknowledged.     

Statistical mining of interesting association rules

This article utilizes stochastic ideas for reasoning about association rule mining, and provides a formal statistical view of this discipline. A simple stochastic model is proposed, based on which support and confidence are reasonable estimates for certain probabilities of the model. Statistical properties of the corresponding estimators, like moments and confidence intervals, are derived, and items and itemsets are observed for correlations. After a brief review of measures of interest of association rules, with the main focus on interestingness measures motivated by statistical principles, two new measures are described. These measures, called α- and σ-precision, respectively, rely on statistical properties of the estimators discussed before. Experimental results demonstrate the effectivity of both measures.     

Sequential design of computer experiments for the estimation of a probability of failure

This paper deals with the problem of estimating the volume of the excursion set of a function f:ℝ ^d →ℝ above a given threshold, under a probability measure on ℝ ^d that is assumed to be known. In the industrial world, this corresponds to the problem of estimating a probability of failure of a system. When only an expensive-to-simulate model of the system is available, the budget for simulations is usually severely limited and therefore classical Monte Carlo methods ought to be avoided. One of the main contributions of this article is to derive SUR (stepwise uncertainty reduction) strategies from a Bayesian formulation of the problem of estimating a probability of failure. These sequential strategies use a Gaussian process model of f and aim at performing evaluations of f as efficiently as possible to infer the value of the probability of failure. We compare these strategies to other strategies also based on a Gaussian process model for estimating a probability of failure.     

The Kumaraswamy Gumbel distribution

The Gumbel distribution is perhaps the most widely applied statistical distribution for problems in engineering. We propose a generalization—referred to as the Kumaraswamy Gumbel distribution—and provide a comprehensive treatment of its structural properties. We obtain the analytical shapes of the density and hazard rate functions. We calculate explicit expressions for the moments and generating function. The variation of the skewness and kurtosis measures is examined and the asymptotic distribution of the extreme values is investigated. Explicit expressions are also derived for the moments of order statistics. The methods of maximum likelihood and parametric bootstrap and a Bayesian procedure are proposed for estimating the model parameters. We obtain the expected information matrix. An application of the new model to a real dataset illustrates the potentiality of the proposed model. Two bivariate generalizations of the model are proposed.     

A New Family of BAN Estimators for Polytomous Logistic Regression Models based on ϕ- Divergence Measures

In this paper we study polytomous logistic regression model and the asymptotic properties of the minimum ϕ-divergence estimators for this model. A simulation study is conducted to analyze the behavior of these estimators as function of the power-divergence measure ϕ_(λ) Research partially done when was visiting the Bowling Green State University as the Distinguished Lukacs Professor     

On confidence intervals for nonmonotone parametric functions and an application to the squared mean of the normal distribution

Consider the problem of obtaining a confidence interval for some function g(θ) of an unknown parameter θ, for which a (1-α)-confidence interval is given. If g(θ) is one-to-one the solution is immediate. However, if g is not one-to-one the problem is more complex and depends on the structure of g. In this note the situation where g is a nonmonotone convex function is considered. Based on some inequality, a confidence interval for g(θ) with confidence level at least 1-α is obtained from the given (1-α) confidence interval on θ. Such a result is then applied to the n(μ, σ ²) distribution with σ known. It is shown that the coverage probability of the resulting confidence interval, while being greater than 1-α, has in addition an upper bound which does not exceed Θ(3z_1−α/2)-α/2.     

Measuring non-linear dependence for two random variables distributed along a curve

We propose new dependence measures for two real random variables not necessarily linearly related. Covariance and linear correlation are expressed in terms of principal components and are generalized for variables distributed along a curve. Properties of these measures are discussed. The new measures are estimated using principal curves and are computed for simulated and real data sets. Finally, we present several statistical applications for the new dependence measures.     

A new Lindley distribution with location parameter

Abstract

The Lindley distribution has been used recently for modeling lifetime data and studying some stress-strength problems. In this paper, a new three-parameter Lindley distribution is introduced. The added location parameter offers more flexibility in fitting some real data against other common distributions. Several statistical and reliability properties are discussed. A simulation study has been carried to examine the MSE, bias, and coverage probability for the parameters. A real data set is used to illustrate the flexibility of the proposed distribution.     

Multinomial Selection Problem: A Study of BEM and AVC Algorithms

The two well-known and widely used multinomial selection procedures Bechhofor, Elmaghraby, and Morse (BEM) and all vector comparison (AVC) are critically compared in applications related to simulation optimization problems.

Two configurations of population probability distributions in which the best system has the greatest probability p _i of yielding the largest value of the performance measure and has or does not have the largest expected performance measure were studied.

The numbers achieved by our simulations clearly show that none of the studied procedures outperform the other in all situations. The user must take into consideration the complexity of the simulations and the performance measure probability distribution properties when deciding which procedure to employ.

An important discovery was that the AVC does not work in populations in which the best system has the greatest probability p _i of yielding the largest value of the performance measure but does not have the largest expected performance measure.