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This paper investigates interval estimation for measures of overlap, namely Matusita's measure, Weitzman's measure and based on Kullback–Leibler. Two types of sampling procedures, namely, Simple Random Sample and Upper Record Values from two exponential populations. Bootstrap method series approximation is used to construct confidence intervals for the overlap measures. To illustrate the performance of the likelihood confidence intervals obtained for this overlapping coefficient, under our proposal, we carry out some simulation studies that yield adequate coverage frequencies, this study conducts for comparing the performances of various competing estimators. A real data set is analysed to exemplify our proposal. [ABSTRACT FROM AUTHOR]

Over the last four decades, the cluster regression analysis in a finite population (FP) setup for an exponential family such as linear or binary data was done by using a two-stage cluster sample chosen from the FP but by treating the sample as though it is a single-stage cluster sample from a super-population (SP) which contains the FP as a hypothetical sample. Because the responses within a cluster in the FP are correlated, the aforementioned sample mis-specification makes the sample-based so-called GLS (generalized least square) estimators design biased and inconsistent. In this paper, we demonstrate for the exponential family data how to avoid the sampling mis-specification and accommodate the cluster correlations to obtain unbiased and consistent estimates for the FP parameters. The asymptotic normality of the regression estimators is also given for the construction of confidence intervals when needed. [ABSTRACT FROM AUTHOR]

In this paper, we present a modified one-stage multiple comparison procedure for exponential location parameters with the control under heteroscedasticity including one-sided and two-sided confidence intervals to improve the coverage probability and average confidence length than the old one. These intervals can be used to identify a subset which includes all no-worse-than-the-control treatments in an experimental design and to identify better-than-the-control, worse-than-the- control and not-much-different-from-the-control products in agriculture, stock market, pharmaceutical industries in terms of the minimum guarantee lifetimes. A simulation comparison is done for this modified procedure with the old one in terms of the confidence length and coverage probability. One example is given to demonstrate the proposed modified procedure. [ABSTRACT FROM AUTHOR]

Uniformly most powerful confidence distributions are obtained for parameters in selected models of the exponential family. A conditioning on the selection event as well as on the sufficient statistics of nuisance parameters guarantees valid post-selection inference. Optimal confidence intervals are obtained directly from the confidence distribution without requiring an inversion of pivotal quantities. Simulations showcase that the method works also when all models are misspecified. • Valid inference after model selection for parameters in exponential family models. • Confidence curves conditional on the selected model. • Confidence distributions yield correct p-values and best confidence intervals. • Finite sample uniformly most powerful confidence curves for continuous distributions. • Good approximations for discrete distributions. [ABSTRACT FROM AUTHOR]

The asymptotic behaviour of the commonly used bootstrap percentile confidence interval is investigated when the parameters are subject to linear inequality constraints. We concentrate on the important one- and two-sample problems with data generated from general distributions in the natural exponential family. The focus of this note is on quantifying the coverage probabilities of the parametric bootstrap percentile confidence intervals, in particular their limiting behaviour near boundaries. We propose using a local asymptotic framework to study this subtle coverage behaviour. Under this framework, we discover that when the true parameters are on, or close to, the restriction boundary, the asymptotic coverage probabilities can always exceed the nominal level in the one-sample case; however, they can be, remarkably, both under and over the nominal level in the two-sample case. Using illustrative examples, we show that the results provide theoretical justification and guidance on applying the bootstrap percentile method to constrained inference problems. [ABSTRACT FROM AUTHOR]

For the generalized positive exponential family of distributions, the problems of confidence interval estimation, bounded risk point estimation, and minimum risk point estimation of the rth power of unknown parameter θ are handled. Two-stage and purely sequential methodologies are developed for the estimation purpose. Sequential procedures are developed based on the maximum likelihood estimator (MLE) of the parameter. The exact operating characteristics of these procedures are given. [ABSTRACT FROM AUTHOR]

In this paper, a two-stage sequential estimation procedure is considered to construct fixed-accuracy confidence intervals of the reliability parameter R under the stress–strength model when the stress and strength are independent exponential random variables with different scale parameters. The exact distribution of the total sample size, explicit formulas for the expected value, and mean squared error of the maximum likelihood estimator of the reliability parameter under the stress–strength model are provided under the two-stage sequential procedure. Performances of the proposed methodology are investigated with the help of simulations. Finally, using three pairs of real datasets, the procedure is clearly illustrated. We used MATLAB in order to implement computations, simulation studies, and real data analysis. [ABSTRACT FROM AUTHOR]

Scientists use high-dimensional measurement assays to detect and prioritize regions of strong signal in spatially organized domain. Examples include finding methylation-enriched genomic regions using microarrays, and active cortical areas using brain-imaging. The most common procedure for detecting potential regions is to group neighboring sites where the signal passed a threshold. However, one needs to account for the selection bias induced by this procedure to avoid diminishing effects when generalizing to a population. This article introduces pin-down inference, a model and an inference framework that permit population inference for these detected regions. Pin-down inference provides nonasymptotic point and confidence interval estimators for the mean effect in the region that account for local selection bias. Our estimators accommodate nonstationary covariances that are typical of these data, allowing researchers to better compare regions of different sizes and correlation structures. Inference is provided within a conditional one-parameter exponential family per region, with truncations that match the selection constraints. A secondary screening-and-adjustment step allows pruning the set of detected regions, while controlling the false-coverage rate over the reported regions. We apply the method to genomic regions with differing DNA-methylation rates across tissue. Our method provides superior power compared to other conditional and nonparametric approaches. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

We propose a data-driven approach to quantify the uncertainty of models constructed by kernel methods. Our approach minimizes the needed distributional assumptions, hence, instead of working with, for example, Gaussian processes or exponential families, it only requires knowledge about some mild regularity of the measurement noise, such as it is being symmetric or exchangeable. We show, by building on recent results from finite-sample system identification, that by perturbing the residuals in the gradient of the objective function, information can be extracted about the amount of uncertainty our model has. Particularly, we provide an algorithm to build exact, non-asymptotically guaranteed, distribution-free confidence regions for ideal, noise-free representations of the function we try to estimate. For the typical convex quadratic problems and symmetric noises, the regions are star convex centered around a given nominal estimate, and have efficient ellipsoidal outer approximations. Finally, we illustrate the ideas on typical kernel methods, such as LS-SVC, KRR, ε -SVR and kernelized LASSO. [ABSTRACT FROM AUTHOR]

In this article, a competing risks model based on exponential distributions is considered under the adaptive Type-II progressively censoring scheme introduced by Ng et al. [2009, Naval Research Logistics 56:687-698], for life testing or reliability experiment. Moreover, we assumed that some causes of failures are unknown. The maximum likelihood estimators (MLEs) of unknown parameters are established. The exact conditional and the asymptotic distributions of the obtained estimators are derived to construct the confidence intervals as well as the two different bootstraps of different unknown parameters. Under suitable priors on the unknown parameters, Bayes estimates and the corresponding two sides of Bayesian probability intervals are obtained. Also, for the purpose of evaluating the average bias and mean square error of the MLEs, and comparing the confidence intervals based on all mentioned methods, a simulation study was carried out. Finally, we present one real dataset to conduct the proposed methods. [ABSTRACT FROM PUBLISHER]

In this article, we study the algorithm of Kiefer–Wolfowitz underquasi-associated random errors. We establish the complete convergence and obtain an exponential bound. Additionally, we build a confidence interval for the minimum. Numerical examples are sketched out to confirm the theoretical results and show the accuracy of the algorithm. [ABSTRACT FROM AUTHOR]

This article presents a unified approach for computing nonequal tail optimal confidence intervals (CIs) for the scale parameter of the exponential family of distributions. We prove that there exists a pivotal quantity, as a function of a complete sufficient statistic, with a chi-square distribution. Using the similarity between equations of shortest, unbiased, and highest density CIs, all equations are reduced into a system of two equations that can be solved via a straightforward algorithm. [ABSTRACT FROM AUTHOR]

In this paper on the basis of unbiased estimates the problem of constructing asymptotic confidence interval for P( X < Y) is solved for the case where random variables X and Y have distributions, belonging to one-parameter exponential families. [ABSTRACT FROM AUTHOR]

Among k independent two-parameter exponential distributions which have the common scale parameter, the lower extreme population (LEP) is the one with the smallest location parameter and the upper extreme population (UEP) is the one with the largest location parameter. Given a multiply type II censored sample from each of these k independent two-parameter exponential distributions, 14 estimators for the unknown location parameters and the common unknown scale parameter are considered. Fourteen simultaneous confidence intervals (SCIs) for all distances from the extreme populations (UEP and LEP) and from the UEP from these k independent exponential distributions under the multiply type II censoring are proposed. The critical values are obtained by the Monte Carlo method. The optimal SCIs among 14 methods are identified based on the criteria of minimum confidence length for various censoring schemes. The subset selection procedures of extreme populations are also proposed and two numerical examples are given for illustration. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we propose a new approach to estimation of the tail exponent in financial stock markets. We begin the study with the finite sample behavior of the Hill estimator under -stable distributions. Using large Monte Carlo simulations, we show that the Hill estimator overestimates the true tail exponent and can hardly be used on samples with small length. Utilizing our results, we introduce a Monte Carlo-based method of estimation for the tail exponent. Our proposed method is not sensitive to the choice of tail size and works well also on small data samples. The new estimator also gives unbiased results with symmetrical confidence intervals. Finally, we demonstrate the power of our estimator on the international world stock market indices. On the two separate periods of 2002–2005 and 2006–2009, we estimate the tail exponent. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we revisit the classical problem of interval estimation for one-binomial parameter and for the log odds ratio of two binomial parameters. We examine the confidence intervals provided by two versions of the modified log likelihood root: the usual Barndorff-Nielsen''s and a Bayesian version of the test statistic. For the one-binomial problem, this work updates the findings of Brown et al. [2003. Interval estimation in exponential families. Statistica Sinica 13, 19–49; 2002. Confidence intervals for a binomial proportion and asymptotic expansion. The Annals of Statistics 30, 160–201] and Cai [2005. One-sided confidence intervals in discrete distributions. Journal of Statistical Planning and Inference 131, 63–88] to higher-order methods. For the log odds ratio of two binomial parameters we show via Edgeworth expansion that both versions of the statistics give confidence intervals which nearly completely eliminate the systematic bias in the unconditional smooth coverage probability. We also give expansions for the length of the confidence intervals. [Copyright &y& Elsevier]

Diagnostics are investigated for assessing the agreement between likelihood and standard large-sample confidence regions for parameters from an exponential family nonlinear model (Cordeiro and Paula 1989). The development connects the contour methods proposed by Hodges (1985, 1987) with the curvature measures for normal, nonlinear regression proposed by Bates and Watts (1980) and Jennings's (1982, 1986) diagnostics for logistic regression. The proposed methodology is illustrated with several examples. [ABSTRACT FROM AUTHOR]

Strong consistency and asymptotic normality of the maximum likelihood estimators are established in the context of jointly type-II censored samples from two exponential populations. Large-sample confidence intervals are derived for the individual scale parameters as well as their ratio, and some applications to series and parallel systems are discussed. For the ratio of the scale parameters, an exact confidence procedure is developed on the basis of the failure counts in the two samples, and its asymptotic efficiency is investigated. [ABSTRACT FROM AUTHOR]

In life testing, type I and type II censoring may be combined to form a hybrid life test. Epstein (1954) introduced this testing scheme and Epstein (1960b) proposed a two-sided confidence interval to estimate the mean lifetime, theta, of an exponential lifetime following such a test. His conjecture is modified slightly and a proof is given that establishes the validity of the proposed confidence interval. [ABSTRACT FROM AUTHOR]

Let S be a system of m independent components s[sub 1], s[sub 2], ..., s[sub m] such that the system operates if and only if at least k of these m components operate successfully. Component lifetimes are assumed to follow independent exponential distributions with unknown scale parameters. The minimum variance unbiased estimator of the reliability of this system at a given mission time t is obtained. The performance of the estimator, when the component lifetimes are identically distributed, is compared with the corresponding maximum likelihood estimator for both large and small samples. Finally, a confidence interval for system reliability is obtained. [ABSTRACT FROM AUTHOR]

This paper presents tables for confidence limits on the MTBF of an exponential distribution using observations from sequential tests of MIL-STD 781. An example shows the use of the tables. The theory and calculation of the tables are explained in the Appendices. [ABSTRACT FROM AUTHOR]

The estimation of functions of Bernoulli parameters is of interest in reliability theory. In this article, the theory of exponential families is used to obtain exact confidence limits for products and quotients of Bernoulli parameters when negative binomial observations are available from each population. Sampling methods based on compound Poisson distribution are suggested for estimating more general functions such as sums of products of Bernoulli parameters. Finally, it is shown that exact confidence limits for products can be obtained by a discrete analog of the Lieberman-Ross procedure, which exploits the independence of the minimum and difference of geometric variates. [ABSTRACT FROM AUTHOR]

It is desired to set confidence limits for the probability of successful operation at least until time x[sub 0] of a series system of k dissimilar components. The components follow exponential failure laws, and data are available on failure times of components of each type. Exact confidence limits for k=2 have previously been given by Lentner and Buehler. The present paper deals with a large-sample approximation to the exact solution for arbitrary k. [ABSTRACT FROM AUTHOR]

We suggests herein a procedure for finding approximate fiducial bounds on the true reliability for the two parameter negative exponential distribution, which may be used as approximate "confidence" limits on reliability. These limits are simple and easy to compute. Also, they reduce to the expect confidence limits for the case of the single parameter negative exponential distribution. [ABSTRACT FROM AUTHOR]

The last years, the odd Fréchet-G family has been considered with success in various statistical applications. This notoriety can be explained by its simple and flexible exponential-odd structure quite different to the other existing families, with the use of only one additional parameter. In counter part, some of its statistical properties suffer of a lack of adaptivity in the sense that they really depend on the choice of the baseline distribution. Hence, efforts have been made to relax this subjectivity by investigating extensions or generalizations of the odd transformation at the heart of the construction of this family, with the aim to reach new perspectives of applications as well. This study explores another possibility, based on the transformation of the whole cumulative distribution function of this family (while keeping the odd transformation intact), through the use of the quadratic rank transmutation that has proven itself in other contexts. We thus introduce and study a new family of flexible distributions called the transmuted odd Fréchet-G family. We show how the former odd Fréchet-G family is enriched by the proposed transformation through theoretical and practical results. We emphasize the special distribution based on the standard exponential distribution because of its desirable features for the statistical modeling. In particular, different kinds of monotonic and nonmonotonic shapes for the probability density and hazard rate functions are observed. Then, we show how the new family can be used in practice. We discuss in detail the parametric estimation of a special model, along with a simulation study. Practical data sets are handle with quite favorable results for the new modeling strategy. [ABSTRACT FROM AUTHOR]