Publications (5)4.15 Total impact
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ABSTRACT: The construction of a set of simultaneous confidence intervals for any finite number of contrasts of pp generally correlated normal means is considered. It is shown that the simultaneous confidence level can be expressed as a (p−2)(p−2)dimensional integral for a general p≥3p≥3. This expression allows one to compute quickly and accurately, by using numerical quadrature, the required critical constants and multiplicity adjusted ppvalues for at least p=3p=3, 4 and 5, involving only one, two and threedimensional integrals, respectively. Real data examples from a drug stability study and a dose response study are used to illustrate the method.Computational Statistics & Data Analysis 01/2013; 57(1):141–148. DOI:10.1016/j.csda.2012.06.007 · 1.40 Impact Factor 
Article: Simultaneous Confidence Bands for Linear Regression with Covariates Constrained in Intervals
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ABSTRACT: The focus of this article is on simultaneous confidence bands over a rectangular covariate region for a linear regression model with k>1 covariates, for which only conservative or approximate confidence bands are available in the statistical literature stretching back to Working & Hotelling (J. Amer. Statist. Assoc. 24,1929; 7385). Formulas of simultaneous confidence levels of the hyperbolic and constant width bands are provided. These involve only a kdimensional integral; it is unlikely that the simultaneous confidence levels can be expressed as an integral of less than kdimension. These formulas allow the construction for the first time of exact hyperbolic and constant width confidence bands for at least a small k(>1) by using numerical quadrature. Comparison between the hyperbolic and constant width bands is then addressed under both the average width and minimum volume confidence set criteria. It is observed that the constant width band can be drastically less efficient than the hyperbolic band when k>1. Finally it is pointed out how the methods given in this article can be applied to more general regression models such as fixedeffect or randomeffect generalized linear regression models.Scandinavian Journal of Statistics 09/2012; 39(3). DOI:10.2307/41679811 · 0.87 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: A simultaneous confidence band provides useful information on the plausible range of an unknown regression model. For simple linear regression models, the most frequently quoted bands in the statistical literature include the hyperbolic band and the threesegment bands. One interesting question is whether one can construct confidence bands better than the hyperbolic and threesegment bands. The optimality criteria for confidence bands include the average width criterion considered by Gafarian (1964) and Naiman (1984) among others, and the minimum area confidence set (MACS) criterion of Liu and Hayter (2007). In this paper, two families of exact 1−α confidence bands, the innerhyperbolic bands and the outerhyperbolic bands, which include the hyperbolic and threesegment bands as special cases, are introduced in simple linear regression. Under the MACS criterion, the best confidence band within each family is found by numerical search and compared with the hyperbolic band, the best threesegment band and with each other. The methodologies are illustrated with a numerical example and the Matlab programs used are available upon request.Journal of Statistical Planning and Inference 05/2010; 140(5):12251235. DOI:10.1016/j.jspi.2009.11.005 · 0.68 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: 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 [W. Liu, A.J. Hayter, Minimum area confidence set optimality for confidence bands in simple linear regression, J. Amer. Statist. Assoc. 102 (477) (2007) pp. 181–190] proposed the 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 be generalized 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 hence 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 hence the size of the predictor variable region is large the hyperbolic band can be substantially better than the constant width band.Journal of Multivariate Analysis 08/2009; 100(7):14321439. DOI:10.1016/j.jmva.2008.12.003 · 0.93 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: This paper addresses the problem of confidence band construction for a standard multiple linear regression model. A “ray” method of construction is developed which generalizes the method of Graybill and Bowden [1967. Linear segment confidence bands for simple linear regression models. J. Amer. Statist. Assoc. 62, 403–408] for a simple linear regression model to a multiple linear regression model. By choosing suitable directions for the rays this method requires only critical points from tdistributions so that the confidence bands are easy to construct. Both onesided and twosided confidence bands can be constructed using this method. An illustration of the new method is provided.Communication in Statistics Theory and Methods 02/2009; 139(2):329334. DOI:10.1016/j.jspi.2008.04.029 · 0.27 Impact Factor
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8  Citations  
4.15  Total Impact Points  
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20092013

University of Southampton
 Department of Mathematics
Southampton, England, United Kingdom
