Exact likelihood inference based on Type-I and Type-II hybrid censored samples from the exponential distribution

Chen and Bhattacharyya (1988,Comm. Statist. Theory Methods,17, 1857–1870) derived the exact distribution of the maximum likelihood estimator of the mean of an exponential distribution and an exact lower confidence bound for the mean based on a hybrid censored sample. In this paper, an alternative simple form for the distribution is obtained and is shown to be equivalent to that of Chen and Bhattacharyya (1988). Noting that this scheme, which would guarantee the experiment to terminate by a fixed timeT, may result in few failures, we propose a new hybrid censoring scheme which guarantees at least a fixed number of failures in a life testing experiment. The exact distribution of the MLE as well as an exact lower confidence bound for the mean is also obtained for this case. Finally, three examples are presented to illustrate all the results developed here.     

指数族ACD模型的参数估计及其Bootstrap置信区间

针对指数族ACD模型,采用Scoring算法对模型进行参数估计,得到指数族情况下Scoring算法中方向向量的计算公式,并讨论了该算法的收敛性以及估计值的相合性.本文运用W ild Bootstrap构建参数的置信区间和置信带,将其结果与传统的残差Bootstrap做比较,数据模拟的结果表明,W ild Bootstrap处理问题更加快捷,结果更加准确.     

Determination of sample sizes for setting β-content tolerance limits controlling both tails of the normal distribution

The problem of determining sample size for two-sided-β-content tolerance intervals which control both tails of the normal distribution is investigated. The tolerance limits are defined to assure that the tail proportions do not exceed specified values p₁ and p₂. We determine the minimum sample size so that, at a given confidence level, the tail proportions will not be too small, i.e., so that the tolerance interval will not be overly conservative. Tables of the sample size n and the corresponding factors are provided.     

M-BAYESIAN CREDIBLE LIMIT METHOD OF PARAMETER AND ITS APPLICATION

This article develops a new method, named M-Bayesian credible limit method, to estimate reliability parameter. In the article, the M-Bayesian credible limit method of failure rate is derived for zero-failure data from products with exponential distribution. Relations between M-Bayesian credible limit and other classical confidence limits are discussed. Finally, the new method is applied to a real zero-failure data set, and as can be seen, it is both efficient and easy to operate.     

A bootstrap approach to model checking for linear models under length-biased data

In this paper, we propose two bootstrap-based model checking tests for a parametric linear model when data are affected by length-bias. These tests are based on the measure of the discrepancy between nonparametric and parametric estimators for the regression function when the data are drawn under a length-biased mechanism. We consider two different discrepancy measures: the supremum and the integral of the quadratic difference between the parametric and nonparametric estimators.     

INSTANTANEOUS AVAILABILITY ASSESSMENT OF RENEWABLE COMPONENT IN EXPONENTIAL DISTRIBUTIONS

A renewable component with exponential failure and repair times is considered, and its instantaneous availability at time t is denoted by A（t）. This paper proposes two methods for constructing lower confidence limits of A（t） with Chebyshev inequation and generalized p-value approach.     

Bootstrapping the mode

The problem of constructing bootstrap confidence intervals for the mode of a density is considered. Estimates of the mode are derived from kernel density estimates based on fixed and data-dependent bandwidths. The asymptotic validity of bootstrap techniques to estimate the sampling distribution of the estimates is investigated. In summary, the results are negative in the sense that a straightforward application of a naive bootstrap yields invalid inferences. In particular, the bootstrap fails if resampling is done from the kernel density estimate. On the other hand, if one resamples from a smoother kernel density estimate (which is necessarily different from the one which yields the original estimate of the mode), the bootstrap is consistent. The bootstrap also fails if resampling is done from the empirical distribution, unless the choice of bandwidth is suboptimal. Similar results hold when applying bootstrap techniques to other functionals of a density.     

Confidence sets for discrete stochastic optimization

Consider the problem of finding the points of maximum of an expectation functional over a finite setS. Based on statistical estimates at each point ofS, confidence sets for theargmax-set are constructed which guarantee a prespecified probability of correct selection. We review known selection methods and propose a new two-stage procedure that works well for largeS and few global maxima. The performance is compared in a simulation study.     

Bayesian inferences on nonlinear functions of the parameters in linear regression

A variety of statistical problems (e.g. the x-intercept in linear regression, the abscissa of the point of intersection of two simple linear regression lines or the point of extremum in quadratic regression) can be viewed as questions of inference on nonlinear functions of the parameters in the general linear regression model. In this paper inferences on the threshold temperatures and summation constants in crop development will be made. A Bayesian approach for the general formulation of this problem will be developed. By using numerical integration, credibility intervals for individual functions as well as for linear combinations of the functions of the parameters can be obtained. The implementation of an odds ratio procedure is facilitated by placing a proper prior on the ratio of the relevant parameters.Financially supported by the University of the Orange Free State Research Fund.     

Confidence Regions on Variance Components in an Extended ANOVA Model for Combining Information

In this paper we consider a general variance components model for combining experiments that allows for a possible interaction of responses with groups. The variance estimate from each experiment cannot take into account this interaction which can additionally be different from experiment to experiment. The interaction term can be interpreted as an additional variance component so that we consider an extended ANOVA model. We derive confidence regions and test of hypotheses on the variance components which – at least approximately – hold the nominal significance level.