Abstract
We consider the problem of numerically evaluating the expected value of a smooth bounded function of a chi-distributed random variable, divided by the square root of the number of degrees of freedom. This problem arises in the contexts of simultaneous inference, the selection and ranking of populations and in the evaluation of multivariate t probabilities. It also arises in the assessment of the coverage probability and expected volume properties of some non-standard confidence regions. We use a transformation put forward by Mori, followed by the application of the trapezoidal rule. This rule has the remarkable property that, for suitable integrands, it is exponentially convergent. We use it to create a nested sequence of quadrature rules, for the estimation of the approximation error, so that previous evaluations of the integrand are not wasted. The application of the trapezoidal rule requires the approximation of an infinite sum by a finite sum. We provide a new easily computed upper bound on the error of this approximation. Our overall conclusion is that this method is a very suitable candidate for the computation of the coverage and expected volume properties of non-standard confidence regions.
Similar content being viewed by others
References
Abeysekera W, Kabaila P (2017) Optimized recentered confidence spheres for the multivariate normal mean. Electron J Stat 11:1935–7524
Avery C, Soler F (1988) Applications of transformations to numerical integration. Coll Math J 19:166–168
Chandrasekhar S (1960) Radiative Transfer. Dover, New York
Davis PJ, Rabinowitz P (1984) Methods of Numerical Integration, 2nd edn. Academic Press, San Diego
Dunnett CW (1989) Algorithm AS 251: multivariate normal probability integrals with product correlation structure. J R Stat Soc Ser C Appl Stat 38:564–579
Dunnett CW, Sobel M (1955) Approximations to the probability integral and certain percentage points of a multivariate analogue of student’s t-distribution. Biometrika 42:258–260
Farchione D, Kabaila P (2008) Confidence intervals for the normal mean utilizing prior information. Stat Probab Lett 78:1094–1100
Genz A, Bretz F (2009) Computation of multivariate normal and t probabilities. Springer, London
Gupta SS, Panchapakesan S (2002) Multiple decision procedures: theory and methodology of selecting and ranking populations. SIAM, Philadelphia
Hochberg Y, Tamhane AC (1987) Multiple comparison procedures. Wiley, New York
Imhof J (1963) On the method for numerical integration of Clenshaw and Curtis. Numer Math 5:138–141
Kabaila P (2018) On the minimum coverage probability of model averaged tail area confidence intervals. Can J Stat 46:279–297
Kabaila P, Farchione D (2012) The minimum coverage of confidence intervals in regression after a preliminary F test. J Stat Plan Inference 142:956–964
Kabaila P, Giri K (2009a) Confidence intervals in regression utilizing prior information. J Stat Plan Inference 139:3419–3429
Kabaila P, Giri K (2009b) Upper bounds on the minimum coverage probability of confidence intervals in regression after model selection. Aust N Z J Stat 51:271–287
Kabaila P, Giri K (2013) Further properties of frequentist confidence intervals in regression that utilize uncertain prior information. Aust N Z J Stat 55:259–270
Kabaila P, Tissera D (2014) Confidence intervals in regression that utilize uncertain prior information about a vector parameter. Aust N Z J Stat 56:371–383
Kabaila P, Welsh A, Abeysekera W (2016) Model-averaged confidence intervals. Scand J Stat 43:35–48
Kabaila P, Welsh A, Mainzer R (2017) The performance of model averaged tail area confidence intervals. Commun Stat Theory Methods 46:10718–10732
Mi X, Miwa T, Hothorn T (2009) mvtnorm: new numerical algorithm for multivariate normal probabilities. R J 1:37–39
Miller RG (1981) Simultaneous statistical inference, 2nd edn. Springer, New York
Miwa T, Hayter AJ, Kuriki S (2003) The evaluation of general non-centred orthonant probabilities. J Roy Stat Soc B 65:223–234
Mori M (1985) Quadrature formulas obtained by variable transformation and the de-rule. J Comput Appl Math 12(13):119–130
Mori M (1988) The double exponential formula for numerical integration over the half infinite interval. In: Agarwal R, Chow Y, Wilson S (eds) Numerical Mathematics (Singapore 1988). Birkhauser, Basel, pp 367–379
Mori M, Sugihara M (2001) The double-exponential transformation in numerical analysis. J Comput Appl Math 127:287–296
Papoulis A (1962) The Fourier integral and its applications. McGraw-Hill, New York
Sag TW, Szekeres G (1964) Numerical evaluation of high-dimensional integrals. Math Comput 18:245–253
Schwartz C (1969) Numerical integration of analytic functions. J Comput Phys 4:19–29
Shea B (1988) Algorithm AS 239: Chi-squared and incomplete gamma integral. J R Stat Soc Ser C 37:466–473
Takahasi H, Mori M (1973) Quadrature formulas obtained by variable transformation. Numer Math 21:206–219
Trefethen LN, Weideman JAC (2014) The exponentially convergent trapezoidal rule. SIAM Rev 56:385–458
Acknowledgements
This work was supported by an Australian Government Research Training Program Scholarship. The authors thank the reviewers for their comments and suggestions as these led to an improved paper.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Kabaila, P., Ranathunga, N. Computation of the expected value of a function of a chi-distributed random variable. Comput Stat 36, 313–332 (2021). https://doi.org/10.1007/s00180-020-01005-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00180-020-01005-y