Minimum area confidence set optimality for confidence bands in simple linear regression

The average width of a simultaneous confidence band has been used by several authors (e.g. Naiman, 1983, 1984, Piegorsch, 1985a) as a criterion for the comparison of different confidence bands. In this paper, the area of the confidence set corresponding to a confidence band is used as a new criterion. For simple linear regression, comparisons have been carried out under this new criterion between hyperbolic bands, two-segment bands, and three-segment bands, which include constant width bands as special cases. It is found that if one requires a confidence band over the whole range of the covariate, then the best confidence band is given by the Working & Hotelling hyperbolic band. Furthermore, if one needs a confidence band over a finite interval of the covariate, then a restricted hyperbolic band can again be recommended, although a three-segment band may be very slightly superior in certain cases

Comparison of hyperbolic and constant width simultaneous confidence bands in multiple linear regression under MVCS criterion

A simultaneous confidence band provides useful information on the plausible range of the unknown regression model, and different confidence bands can often be constructed for the same regression model. For a simple regression line, Liu and Hayter (2007) propose use of the area of the confidence set corresponding to a confidence band as an optimality criterion in comparison of confidence bands; the smaller the area of the confidence set, the better the corresponding confidence band. This minimum area confidence set (MACS) criterion can begeneralized to a minimum volume confidence set (MVCS) criterion in the study of confidence bands for a multiple linear regression model. In this paper hyperbolic and constant width confidence bands for a multiple linear regression model over a particular ellipsoidal region of the predictor variables are compared under the MVCS criterion. It is observed that whether one band is better than the other depends on the magnitude of one particular angle that determines the size of the predictor variable region. When the angle and so the size of the predictor variable region is small, the constant width band is better than the hyperbolic band but only marginally. When the angle and so the size of the predictor variable region is large the hyperbolic band can be substantially better than the constant width band

Explicit estimates for Artin LL-functions: Duke's short-sum theorem and Dedekind Zeta Residues

Under GRH, we establish a version of Duke's short-sum theorem for entire Artin $L$-functions. This yields corresponding bounds for residues of Dedekind zeta functions. All numerical constants in this work are explicit.Comment: 20 pages, to appear in the Journal of Number Theore

The power function of the studentised range test

In this paper we investigate the power function of the Studentised range test for comparing the means of normal populations in the one-way fixed effects analysis of variance model. The main results provide rigorous proofs of certain least favourable configurations of population means. These results are important in the calculation of the sample sizes required to guarantee power levels under certain restrictions on the ranges of the population means

A new test against an umbrella alternative and the associated simultaneous confidence intervals.

For the usual balanced one-way fixed effects analysis of variance (ANOVA) model, a new test of the null hypothesis H[0]: ?[1] = … = ?[k] against the umbrella alternative H[h]: ?[1] ? …? ?[h] ? …? ?[k] with at least one strict inequality is proposed. More usefully, this new test can be easily inverted to produce a set of one-sided simultaneous confidence intervals (SCIs) for all the ordered pairwise differences ?[j]-? for 1 ? i < j ? h and h ? j < i ? k, and therefore enables the experimenter to infer which ?[j]'s are different when H[0] is rejected. A table of critical values is provided to allow immediate and simple implementation of this new inference procedure

Selecting and sharpening inferences in simultaneous inferences with a Bayesian approach

A frequentist simultaneous confidence interval procedure requires the predetermination of the comparisons and their corresponding forms of confidence intervals before viewing the data in order that the error probability is controlled at a preassigned level. This often renders it less sensitive to detecting actual true differences and may result in it including many noninformative inferences. On the other hand, by taking a Bayesian approach, we can select the comparisons of interest and construct corresponding joint credible intervals after having viewed the data. This enables us to focus on those significant differences of interest and consequently to be able to make sharper inferences. The joint posterior probability of the credible intervals play a similar role as the joint coverage probability of the simultaneous confidence intervals, that is, to guarantee, with at least that probability, all the inferences made using the intervals are correct at the same time. In this article, we consider some standard problems in simultaneous inference and discuss how a Bayesian approach may be implemented. The methodologies are illustrated with examples